15.3.3.7 Obstacle Dectection, Objects on the Road

Chapter Contents (Back)
Tracking. Path Planning. Obstacle Detection. Obstacle Avoidance. Planning the path:
See also Path Planning for Obstacle Avoidance.
See also Other Vehicles.
See also Vehicle Trajectory Prediction, Autonomous Driving.
See also Platoons, Platooning, Groups, Formation, Vehicle Control, Vehicle Cooperation.
See also Ground Plane Detection.
See also Traffic Surveillance, Analysis of Traffic.
See also Collision Avoidance, Collision Detection, Vehicles, Objects on the Road.
See also Target Tracking, Collision Detection. Holes, etc.
See also Inspection -- Pavement, Road Surface, Asphalt, Concrete.
See also YOLO, You Only Look Once, Family Object Detection.

Zheng, Y., Jones, D.G., Billings, S.A., Mayhew, J.E.W., Frisby, J.P.,
Switcher: A Stereo Algorithm for Ground Plane Obstacle Detection,
IVC(8), No. 1, February 1990, pp. 57-62.
Elsevier DOI BibRef 9002

Ellwood, G.J., Zheng, Y., Mayhew, J.E.W.,
Docking for mobile robots,
BMVC94(xx-yy).
PDF File. 9409
BibRef

Ellwood, G.J., Zheng, Y., Billings, S.A., Mayhew, J.E.W., Frisby, J.P.,
Ground Plane Motion Parameter Estimation for Non-circular Paths,
BMVC92(xx-yy).
PDF File. 9209
BibRef

Cornell, S.M., Porrill, J., Mayhew, J.E.W.,
Ground Plane Obstacle Detection Under Variable Camera Geometry Using a Predictive Stereo Matcher,
BMVC92(xx-yy).
PDF File. 9209
BibRef

Shao, Y., Mayhew, J.E.W., Hippisley-Cox, S.D.,
Ground Plane Obstacle Detection of Stereo Vision under Variable Camera Geometry Using Neural Nets,
BMVC95(xx-yy).
PDF File. 9509
BibRef

Xie, M., Trassoudaine, L., Alizon, J., Gallice, J.,
Road Obstacle Detection and Tracking by an Active and Intelligent Sensing Strategy,
MVA(7), No. 3, 1994, pp. 165-177. BibRef 9400

Xie, M., Trassoudaine, L., Alizon, J., Thonnat, M., Gallice, J.,
Active and intelligent sensing of road obstacles: Application to the European Eureka-PROMETHEUS project,
ICCV93(616-623).
IEEE DOI 0403
BibRef

Russo, M.F., Petersen, J., Echols, M.,
Machine Vision for Error-Detection and Avoidance in Laboratory Robotics,
LRobA(7), No. 3, June 1995, pp. 145-157. BibRef 9506

Bhanu, B., Das, S., Roberts, B., Duncan, D.,
A System for Obstacle Detection During Rotorcraft Low-Altitude Flight,
AeroSys(32), No. 3, July 1996, pp. 875-897. 9608
BibRef
Earlier: A1, A3, A4, A2: WACV92(92-99).
IEEE DOI BibRef

Xie, M.,
Matching Free Stereovision for Detecting Obstacles on a Ground Plane,
MVA(9), No. 1, 1996, pp. 9-13.
Springer DOI 9608
BibRef

Maekawa, H.[Hiroko],
Image tracking device and image tracking method,
US_Patent5,530,771, Jun 25, 1996
WWW Link. BibRef 9606

Zhang, Z.F.[Zhong-Fei], Weiss, R.[Richard], and Hanson, A.R.[Allen R.],
Obstacle Detection Based on Qualitative and Quantitative 3D Reconstruction,
PAMI(19), No. 1, January 1997, pp. 15-26.
IEEE DOI 9702
BibRef
Earlier:
Obstacle Detection Based on Partial 3D Reconstruction,
ARPA94(II:1077-1082). BibRef
And:
Qualitative Obstacle Detection,
CVPR94(554-559).
IEEE DOI BibRef
And: UMassCS-TR-94-20, March 1990.
PS File. Three algorithms using different assumptions. Two are qualitative, only Yes or No regarding obstacles. One uses knowledge of the ground plane, the second only assumes the ground plane is planar. The third is quantitative, it estimates the ground plane and approximate (partial) 3D structure by determining Height above the ground plane. Conclued Third is superior.
See also 3D Reconstruction Based on Homography Mapping. BibRef

Zhang, Z.F.[Zhong-Fei], Weiss, R.[Richard], and Hanson, A.R.[Allen R.],
Automatic Calibration and Visual Servoing for a Robot Navigation System,
UMassCS-TR-93-14, February 1993. BibRef 9302
Earlier:
PS File.
Automatic Calibration for a Robot Navigation System,
UMassCS-TR-92-70, October 1992.
PS File. BibRef

Griswold, N.C., Eem, J.,
Control for Mobile Robots in the Presence of Moving Objects,
RA(6), 1990, pp. 263-268. BibRef 9000

Nishitani, K.[Katsuo],
Distance measuring apparatus of a target tracking type,
US_Patent5,515,448, May 7, 1996
WWW Link. BibRef 9605

Fujimori, A., Nikiforuk, P.N., Gupta, M.M.,
Adaptive Navigation of Mobile Robots with Obstacle Avoidance,
RA(13), No. 4, August 1997, pp. 596-602. 9708
BibRef

Sargent, R., Bailey, B., Witty, C., Wright, A.,
Dynamic Object Capture Using Fast Vision Tracking,
AIMag(18), No. 1, Spring 1997, pp. 65-72. 9704
Robot Contest system. BibRef

Asayama, Y.[Yoshiaki],
Obstacle detecting system for a motor vehicle,
US_Patent5,633,705, May 27, 1997
WWW Link. with stereo BibRef 9705

Whittaker, W.L.[William L.], West, J.H.[Jay H.], Singh, S.J.[Sanjiv J.], Lay, N.K.[Norman K.], Devier, L.J.[Lonnie J.],
System and method for detecting obstacles in a road,
US_Patent5,680,313, Oct 21, 1997
WWW Link. BibRef 9710

Bertozzi, M.[Massimo], Broggi, A.[Alberto],
GOLD: A Parallel Real-Time Stereo Vision System for Generic Obstacle and Lane Detection,
IP(7), No. 1, January 1998, pp. 62-81.
IEEE DOI 9801
BibRef

Bertozzi, M.[Massimo], Broggi, A., Fascioli, A.,
A Stereo Vision System for Real-Time Automotive Obstacle Detection,
ICIP96(II: 681-684).
IEEE DOI BibRef 9600

Nair, D., Aggarwal, J.K.,
Moving Obstacle Detection from a Navigating Robot,
RA(14), No. 3, June 1998, pp. 404-416. 9806
BibRef
Earlier:
Detecting unexpected moving obstacles that appear in the path of a navigating robot,
ICIP94(II: 311-315).
IEEE DOI 9411
BibRef

Stella, E., Lovergine, F.P., d'Orazio, T., and Distante, A.,
A Visual Tracking Technique Suitable for Control of Convoys,
PRL(16), 1995, pp. 925-932. BibRef 9500

Schneiderman, H., Nashman, M.,
A Discriminating Feature Tracker for Vision-Based Autonomous Driving,
RA(10), 1994, pp. 769-775. BibRef 9400
Earlier:
Visual Processing for Autonomous Driving,
WACV92(164-171).
IEEE DOI
See also Vision-Based Robotic Convoy Driving. BibRef

Raboisson, S.[Stephane], Even, G.[Gilles],
Method of analyzing sequences of road images, device for implementing it and its application to detecting obstacles,
US_Patent5,706,355, Jan 6, 1998
WWW Link. BibRef 9801

Ruichek, Y.[Yassine], Postaire, J.G.[Jack-Gérard],
A New Neural Real-Time Implementation for Obstacle Detection using Linear Stereo Vision,
RealTimeImg(5), No. 2, April 1999, pp. 141-153.
See also Neural Matching Algorithm for 3-D Reconstruction from Stereo Pairs of Linear Images, A. BibRef 9904

Ruichek, Y.[Yassine],
Multilevel- and Neural-Network-Based Stereo-Matching Method for Real-Time Obstacle Detection Using Linear Cameras,
ITS(6), No. 1, March 2005, pp. 54-62.
IEEE Abstract. 0501
BibRef

Takeda, S.[Shu], Tojima, M.[Masanori], Takeda, K.[Koji],
Unmanned vehicle running system,
US_Patent6,321,147, Nov 20, 2001
WWW Link. Obstacle detection and other things. BibRef 0111

Iwata, A.[Ayami], Kato, K.[Kunihito], Yamamoto, K.[Kazuhiko],
The Detection Of Obstacles By The Horizon View Camera,
IJIG(2), No. 2, April 2002, pp. 331-341. 0204
BibRef
Earlier:
The Detection of Obstacles Using Features by the Horizon View Camera,
VI02(133).
PDF File. 0208
BibRef

Hahn, S.[Stefan], Stein, F.J.[Fridtjof J.],
System for optical acquisition of the road,
US_Patent6,067,111, 05/23/2000.
HTML Version. BibRef 0005

Stiller, C., Hipp, J., Rössig, C., Ewald, A.,
Multisensor obstacle detection and tracking,
IVC(18), No. 5, April 2000, pp. 389-396.
Elsevier DOI 0003
BibRef

Franke, U., Heinrich, S.,
Fast obstacle detection for urban traffic situations,
ITS(3), No. 3, September 2002, pp. 173-181.
IEEE Abstract. 0402
BibRef

Franke, U.[Uwe],
Method for stereo image object detection,
US_Patent5,937,079, Aug 10, 1999
WWW Link. BibRef 9908

Zhou, H.Y.[Hui-Yu], Wallace, A.M.[Andrew M.], Green, P.R.[Patrick R.],
A multistage filtering technique to detect hazards on the ground plane,
PRL(24), No. 9-10, June 2003, pp. 1453-1461.
Elsevier DOI 0304
BibRef

Nichani, S.[Sanjay],
Obstacle detection system,
US_Patent6,678,394, Jan 13, 2004
WWW Link. BibRef 0401
And:
Lane detection system and apparatus,
US_Patent6,819,779, Nov 16, 2004
WWW Link. BibRef

Gandhi, T.[Tarak], Trivedi, M.M.[Mohan M.],
Parametric ego-motion estimation for vehicle surround analysis using an omnidirectional camera,
MVA(16), No. 2, February 2005, pp. 85-95.
Springer DOI 0501
BibRef
Earlier:
Motion based vehicle surround analysis using an omni-directional camera,
IVS04(560-565).
IEEE DOI 0411
BibRef

Gandhi, T.[Tarak], Trivedi, M.M.[Mohan M.],
Panoramic Appearance Map (PAM) for Multi-camera Based Person Re-identification,
AVSBS06(78-78).
IEEE DOI 0611
BibRef
Earlier:
Dynamic Panoramic Surround Map: Motivation and Omni Video Based Approach,
MVIV05(III: 61-61).
IEEE DOI 0507
BibRef
Earlier:
Vehicle Mounted Wide FOV Stereo for Traffic and Pedestrian Detection,
ICIP05(II: 121-124).
IEEE DOI 0512
BibRef

Huang, D., Leung, H.,
An Expectation-Maximization-Based Interacting Multiple Model Approach for Cooperative Driving Systems,
ITS(6), No. 2, June 2005, pp. 206-228.
IEEE Abstract. 0506
BibRef

Belkhouche, F., Belkhouche, B., Rastgoufard, P.,
Line of Sight Robot Navigation Toward a Moving Goal,
SMC-B(36), No. 2, April 2006, pp. 255-267.
IEEE DOI 0604
BibRef

Oh, C., Oh, J.S., Ritchie, S.G.,
Real-Time Hazardous Traffic Condition Warning System: Framework and Evaluation,
ITS(6), No. 3, September 2005, pp. 265-272.
IEEE DOI 0509
BibRef

Panwai, S., Dia, H.,
Comparative Evaluation of Microscopic Car-Following Behavior,
ITS(6), No. 3, September 2005, pp. 314-325.
IEEE DOI 0509
BibRef

Kawada, R.[Ryoichi], Wada, M.[Masahiro], Matsumoto, S.[Shuichi],
Detection apparatus for road obstructions,
US_Patent6,952,449, Oct 4, 2005
WWW Link. BibRef 0510

Abdel-Aty, M.A., Pande, A.,
ATMS implementation system for identifying traffic conditions leading to potential crashes,
ITS(7), No. 1, March 2006, pp. 78-91.
IEEE DOI 0604
BibRef

Chen, Z.Z.[Ze-Zhi], Pears, N.E.[Nick E.], Liang, B.J.[Bo-Jian],
Monocular obstacle detection using reciprocal-polar rectification,
IVC(24), No. 12, 1 December 2006, pp. 1301-1312.
Elsevier DOI 0610
Homography; Fundamental matrix; Segmentation; Reciprocal-polar rectification; Image rectification
See also Mobile Robot Visual Navigation Using Multiple Features. BibRef

Caraffi, C., Cattani, S.[Stefano], Grisleri, P.,
Off-Road Path and Obstacle Detection Using Decision Networks and Stereo Vision,
ITS(8), No. 4, December 2007, pp. 607-618.
IEEE DOI 0712
BibRef

Broggi, A., Caraffi, C., Fedriga, R.I., Grisleri, P.,
Obstacle Detection with Stereo Vision for Off-Road Vehicle Navigation,
MVIV05(III: 65-65).
IEEE DOI 0507
BibRef

Ahmed, J.[Javed], Jafri, M.N., Shah, M.[Mubarak], Akbar, M.[Muhammad],
Real-time edge-enhanced dynamic correlation and predictive open-loop car-following control for robust tracking,
MVA(19), No. 1, January 2008, pp. 1-25.
Springer DOI 0801
BibRef

Ahmed, J.[Javed], Jafri, M.N.[M. Noman],
Best-match rectangle adjustment algorithm for persistent and precise correlation tracking,
ICMV07(91-96).
IEEE DOI 0712
BibRef

Zhang, W., Wu, Q.M.J., Yang, X., Fang, X.,
Multilevel Framework to Detect and Handle Vehicle Occlusion,
ITS(9), No. 1, March 2008, pp. 161-174.
IEEE DOI 0803
BibRef

Toulminet, G.[GwenaËlle], Mousset, S.[StÉphane], Bensrhair, A.[Abdelaziz],
Fast And Accurate Stereo Vision-based Estimation Of 3d Position And Axial Motion Of Road Obstacles,
IJIG(4), No. 1, January 2004, pp. 99-125. 0401
BibRef

Wang, C., Coifman, B.,
The Effect of Lane-Change Maneuvers on a Simplified Car-Following Theory,
ITS(9), No. 3, September 2008, pp. 523-535.
IEEE DOI 0809
BibRef

Vasquez, D.[Dizan], Fraichard, T.[Thierry], Aycard, O.[Olivier], Laugier, C.[Christian],
Intentional motion on-line learning and prediction,
MVA(19), No. 5-6, October 2008, pp. xx-yy.
Springer DOI 0810
BibRef

Vasquez, D.[Dizan], Fraichard, T.[Thierry], Laugier, C.[Christian],
Incremental Learning of Statistical Motion Patterns With Growing Hidden Markov Models,
ITS(10), No. 3, September 2009, pp. 403-416.
IEEE DOI 0909
BibRef

Vasquez, D., Large, F., Fraichard, T., Laugier, C.,
Moving obstacles' motion prediction for autonomous navigation,
ICARCV04(I: 149-154).
IEEE DOI 0412
BibRef

Takeda, N.[Nobuyuki], Hattori, H.[Hiroshi], Onoguchi, K.[Kazunori],
System and method for detecting obstacle,
US_Patent7,349,581, Mar 25, 2008
WWW Link. BibRef 0803

Nakai, H., Takeda, N., Hattori, H., Okamoto, Y., Onoguchi, K.,
A practical stereo scheme for obstacle detection in automotive use,
ICPR04(III: 346-350).
IEEE DOI 0409
BibRef

Darms, M.S., Rybski, P.E., Baker, C., Urmson, C.,
Obstacle Detection and Tracking for the Urban Challenge,
ITS(10), No. 3, September 2009, pp. 475-485.
IEEE DOI 0909
BibRef

Wedel, A.[Andreas], Badino, H., Rabe, C., Loose, H., Franke, U.[Uwe], Cremers, D.[Daniel],
B-Spline Modeling of Road Surfaces With an Application to Free-Space Estimation,
ITS(10), No. 4, December 2009, pp. 572-583.
IEEE DOI 0912
BibRef

Wedel, A.[Andreas], Franke, U.[Uwe], Klappstein, J.[Jens], Brox, T.[Thomas], Cremers, D.[Daniel],
Realtime Depth Estimation and Obstacle Detection from Monocular Video,
DAGM06(475-484).
Springer DOI 0610
BibRef

Wedel, A.[Andreas], Schoenemann, T.[Thomas], Brox, T.[Thomas], Cremers, D.[Daniel],
WarpCut: Fast Obstacle Segmentation in Monocular Video,
DAGM07(264-273).
Springer DOI 0709
BibRef

Vanholme, B., Gruyer, D., Lusetti, B., Glaser, S., Mammar, S.,
Highly Automated Driving on Highways Based on Legal Safety,
ITS(14), No. 1, March 2013, pp. 333-347.
IEEE DOI 1303
BibRef

Mulder, M., Pauwelussen, J.J.A., van Paassen, M.M., Mulder, M., Abbink, D.A.,
Active Deceleration Support in Car Following,
SMC-A(40), No. 6, November 2010, pp. 1271-1284.
IEEE DOI 1011
BibRef

Chen, C.L.[Chieh-Li], Liao, Y.F.[Yan-Fa], Tai, C.L.[Chung-Li],
Image-to-MIDI mapping based on dynamic fuzzy color segmentation for visually impaired people,
PRL(32), No. 4, 1 March 2011, pp. 549-560.
Elsevier DOI 1102
Color segmentation; Dynamic fuzzy logic; Image-to-MIDI mapping; RGB raito; Visually impaired people Color space transformations. Road detection, turn into audio information on position and size of obstacles. BibRef

Milanes, V., Alonso, J., Bouraoui, L., Ploeg, J.,
Cooperative Maneuvering in Close Environments Among Cybercars and Dual-Mode Cars,
ITS(12), No. 1, March 2011, pp. 15-24.
IEEE DOI 1103
BibRef

Mulder, M., Abbink, D.A., van Paassen, M.M., Mulder, M.,
Design of a Haptic Gas Pedal for Active Car-Following Support,
ITS(12), No. 1, March 2011, pp. 268-279.
IEEE DOI 1103
BibRef

Morales, N.[Nestor], Toledo, J.T.[Jonay T.], Acosta, L.[Leopoldo], Arnay, R.[Rafael],
Real-time adaptive obstacle detection based on an image database,
CVIU(115), No. 9, September 2011, pp. 1273-1287.
Elsevier DOI 1107
Image registration; Feature matching; Obstacle detection; Image database applications; Visually guided vehicles; Mobile robots BibRef

Gurkahraman, K., Unsal, E., Cebi, Y.,
Omni-directional vision system with fibre grating device for obstacle detection,
IET-CV(5), No. 5, 2011, pp. 267-281.
DOI Link 1110
BibRef

Biral, F., Lot, R., Rota, S., Fontana, M., Huth, V.,
Intersection Support System for Powered Two-Wheeled Vehicles: Threat Assessment Based on a Receding Horizon Approach,
ITS(13), No. 2, June 2012, pp. 805-816.
IEEE DOI 1206
BibRef

Wen, S., Zheng, W., Zhu, J., Li, X., Chen, S.,
Elman Fuzzy Adaptive Control for Obstacle Avoidance of Mobile Robots Using Hybrid Force/Position Incorporation,
SMC-C(42), No. 4, July 2012, pp. 603-608.
IEEE DOI 1206
BibRef

Dlotko, P.[Pawel],
A fast algorithm to compute cohomology group generators of orientable 2-manifolds,
PRL(33), No. 11, 1 August 2012, pp. 1468-1476.
Elsevier DOI 1206
Computational cohomology; Cohomology generators; Combinatorial 2-manifold BibRef

Dlotko, P.[Pawel], Kropatsch, W.G.[Walter G.], Wagner, H.[Hubert],
Characterizing Obstacle-Avoiding Paths Using Cohomology Theory,
CAIP11(I: 310-317).
Springer DOI 1109
For assisted living. BibRef

Khodayari, A., Ghaffari, A., Kazemi, R., Braunstingl, R.,
A Modified Car-Following Model Based on a Neural Network Model of the Human Driver Effects,
SMC-A(42), No. 6, November 2012, pp. 1440-1449.
IEEE DOI 1210
BibRef

Szczurek, P., Xu, B., Wolfson, O., Lin, J.,
Estimating Relevance for the Emergency Electronic Brake Light Application,
ITS(13), No. 4, December 2012, pp. 1638-1656.
IEEE DOI 1212
BibRef

Asamer, J., van Zuylen, H.J., Heilmann, B.,
Calibrating car-following parameters for snowy road conditions in the microscopic traffic simulator VISSIM,
IET-ITS(7), No. 1, 2013, pp. 114-121.
DOI Link 1307
BibRef

Wang, Y.F.[Yi-Fei], Gao, Y.[Yuan], Achim, A.[Alin], Dahnoun, N.[Naim],
Robust obstacle detection based on a novel disparity calculation method and G-disparity,
CVIU(123), No. 1, 2014, pp. 23-40.
Elsevier DOI 1405
Obstacle detection BibRef

Park, M.W.[Min Woo], Jung, S.K.[Soon Ki],
Dynamic Obstacle Detection of Road Scenes using Equi-Height Mosaicking Image,
ELCVIA(13), No. 2, 2014, pp. xx-yy.
DOI Link 1407
Ph.D.. Thesis. BibRef

Park, J., Yoon, J.H., Park, M.G., Yoon, K.J.,
Dynamic Point Clustering with Line Constraints for Moving Object Detection in DAS,
SPLetters(21), No. 10, October 2014, pp. 1255-1259.
IEEE DOI 1407
Clustering methods Detecting moving objects in stereo image sequences. Collision detection. BibRef

Monteil, J., Billot, R., Sau, J., El Faouzi, N.E.,
Linear and Weakly Nonlinear Stability Analyses of Cooperative Car-Following Models,
ITS(15), No. 5, October 2014, pp. 2001-2013.
IEEE DOI 1410
Korteweg-de Vries equation BibRef

Jia, B.Z.[Bao-Zhi], Liu, R.[Rui], Zhu, M.[Ming],
Real-time obstacle detection with motion features using monocular vision,
VC(31), No. 3, March 2015, pp. 281-293.
WWW Link. 1503
BibRef

Zhang, Z., Xu, H., Chao, Z., Li, X., Wang, C.,
A Novel Vehicle Reversing Speed Control Based on Obstacle Detection and Sparse Representation,
ITS(16), No. 3, June 2015, pp. 1321-1334.
IEEE DOI 1506
Calibration BibRef

Velasco, G.A.M.[G.A. Mercado], Borst, C., Ellerbroek, J., van Paassen, M.M., Mulder, M.,
The Use of Intent Information in Conflict Detection and Resolution Models Based on Dynamic Velocity Obstacles,
ITS(16), No. 4, August 2015, pp. 2297-2302.
IEEE DOI 1508
Approximation methods BibRef

Rahman, M., Chowdhury, M., Khan, T., Bhavsar, P.,
Improving the Efficacy of Car-Following Models With a New Stochastic Parameter Estimation and Calibration Method,
ITS(16), No. 5, October 2015, pp. 2687-2699.
IEEE DOI 1511
Bayes methods BibRef

Wang, C., Fang, Y., Zhao, H., Guo, C., Mita, S., Zha, H.,
Probabilistic Inference for Occluded and Multiview On-road Vehicle Detection,
ITS(17), No. 1, January 2016, pp. 215-229.
IEEE DOI 1601
Cameras BibRef

Kristan, M.[Matej], Kenk, V.S.[Vildana Sulic_], Kovacic, S.[Stanislav], Pers, J.,
Fast Image-Based Obstacle Detection From Unmanned Surface Vehicles,
Cyber(46), No. 3, March 2016, pp. 641-654.
IEEE DOI 1602
Cameras BibRef

Huang, Y., Liu, S.,
Multi-class obstacle detection and classification using stereovision and improved active contour models,
IET-ITS(10), No. 3, 2016, pp. 197-205.
DOI Link 1604
feature extraction BibRef

Shi, Y.F., Yang, L.C., Hao, S.X., Liu, Q.,
Clustered car-following strategy for improving car-following stability under Cooperative Vehicles Infrastructure Systems,
IET-ITS(10), No. 3, 2016, pp. 141-147.
DOI Link 1604
cooperative systems BibRef

Wang, M., Daamen, W., Hoogendoorn, S.P., van Arem, B.,
Cooperative Car-Following Control: Distributed Algorithm and Impact on Moving Jam Features,
ITS(17), No. 5, May 2016, pp. 1459-1471.
IEEE DOI 1605
Acceleration BibRef

Liu, Z., Li, D., Wang, L., Dong, D.,
Synchronization of a Group of Mobile Agents With Variable Speeds Over Proximity Nets,
Cyber(46), No. 7, July 2016, pp. 1579-1590.
IEEE DOI 1606
Analytical models BibRef

Guan, K., Ai, B., Fricke, A., He, D., Zhong, Z., Matolak, D.W., Kürner, T.,
Excess Propagation Loss Modeling of Semiclosed Obstacles for Intelligent Transportation System,
ITS(17), No. 8, August 2016, pp. 2171-2181.
IEEE DOI 1608
Computational modeling BibRef

Jia, B.Z.[Bao-Zhi], Feng, W.G.[Wei-Guo], Zhu, M.[Ming],
Obstacle detection in single images with deep neural networks,
SIViP(10), No. 6, June 2016, pp. 1033-1040.
WWW Link. 1608
BibRef

Guo, J., Hu, P., Wang, R.,
Nonlinear Coordinated Steering and Braking Control of Vision-Based Autonomous Vehicles in Emergency Obstacle Avoidance,
ITS(17), No. 11, November 2016, pp. 3230-3240.
IEEE DOI 1609
Autonomous automobiles BibRef

Chen, T., Wang, R., Dai, B., Liu, D., Song, J.,
Likelihood-Field-Model-Based Dynamic Vehicle Detection and Tracking for Self-Driving,
ITS(17), No. 11, November 2016, pp. 3142-3158.
IEEE DOI 1609
Computational modeling BibRef

Li, Z., Filev, D.P., Kolmanovsky, I., Atkins, E., Lu, J.,
A New Clustering Algorithm for Processing GPS-Based Road Anomaly Reports With a Mahalanobis Distance,
ITS(18), No. 7, July 2017, pp. 1980-1988.
IEEE DOI 1706
Algorithm design and analysis, Clustering algorithms, Covariance matrices, Heuristic algorithms, Roads, Vehicles, Evolving clustering algorithm, Mahalanobis distance, Woodbury matrix inversion lemma, road, anomaly, report BibRef

Schaub, A., Baumgartner, D., Burschka, D.,
Reactive Obstacle Avoidance for Highly Maneuverable Vehicles Based on a Two-Stage Optical Flow Clustering,
ITS(18), No. 8, August 2017, pp. 2137-2152.
IEEE DOI 1708
Adaptive optics, Biomedical optical imaging, Cameras, Collision avoidance, Dynamics, Optical imaging, Optical sensors, Collision avoidance, image processing, intelligent vehicles, optical feedback, optimization BibRef

Huang, D.Y.[Deng-Yuan], Chen, C.H.[Chao-Ho], Chen, T.Y.[Tsong-Yi], Hu, W.C.[Wu-Chih], Feng, K.W.[Kai-Wei],
Vehicle detection and inter-vehicle distance estimation using single-lens video camera on urban/suburb roads,
JVCIR(46), No. 1, 2017, pp. 250-259.
Elsevier DOI 1706
Vehicle, detection BibRef

Suhr, J.K., Jung, H.G.,
Rearview Camera-Based Backover Warning System Exploiting a Combination of Pose-Specific Pedestrian Recognitions,
ITS(19), No. 4, April 2018, pp. 1122-1129.
IEEE DOI 1804
Alarm systems, Cameras, Distortion, Feature extraction, Head, Lenses, Optical distortion, Backover warning system, rearview camera BibRef

Al-Mayyahi, A.[Auday], Wang, W.J.[Wei-Ji], Birch, P.[Phil], Hussien, A.[Alaa],
Obstacle detection system based on colour segmentation using monocular vision for an unmanned ground vehicle,
IJCVR(8), No. 3, 2018, pp. 241-266.
DOI Link 1807
BibRef

Rozsa, Z.[Zoltan], Sziranyi, T.[Tamas],
Obstacle Prediction for Automated Guided Vehicles Based on Point Clouds Measured by a Tilted LIDAR Sensor,
ITS(19), No. 8, August 2018, pp. 2708-2720.
IEEE DOI 1808
Laser radar, Robot sensing systems, Shape, Autonomous vehicles, bag of features BibRef

Rozsa, Z.[Zoltan], Sziranyi, T.[Tamas],
Temporal Up-Sampling of LIDAR Measurements Based on a Mono Camera,
CIAP22(II:51-64).
Springer DOI 2205
BibRef

Liu, W.[Wei], Cheng, D.[Dayu], Yin, P.C.[Peng-Cheng], Yang, M.Y.[Meng-Yuan], Li, E.[Erzhu], Xie, M.[Meng], Zhang, L.P.[Lian-Peng],
Small Manhole Cover Detection in Remote Sensing Imagery with Deep Convolutional Neural Networks,
IJGI(8), No. 1, 2019, pp. xx-yy.
DOI Link 1901
BibRef

Su, I.F.[I-Fang], Chen, D.L.[Ding-Li], Lee, C.A.[Chi-Ang], Chung, Y.C.[Yu-Chi],
Finding Visible kNN Objects in the Presence of Obstacles within the User's View Field,
IJGI(8), No. 3, 2019, pp. xx-yy.
DOI Link 1903
BibRef

Jia, Y., Cebon, D.,
Measuring the Motion of Vulnerable Road Users Relative to Moving HGVs,
ITS(20), No. 4, April 2019, pp. 1404-1415.
IEEE DOI 1904
Acoustics, Sensor arrays, Motion measurement, Temperature sensors, Ultrasonic variables measurement, Quadratic programming, vulnerable road users BibRef

Burnett, K., Samavi, S., Waslander, S.L., Barfoot, T.D., Schoellig, A.P.,
aUToTrack: A Lightweight Object Detection and Tracking System for the SAE AutoDrive Challenge,
CRV19(209-216)
IEEE DOI 1908
Detectors, Object detection, Laser radar, Benchmark testing, Object Recognition and Detection BibRef

Corcoran, G., Clark, J.,
Traffic Risk Assessment: A Two-Stream Approach Using Dynamic-Attention,
CRV19(166-173)
IEEE DOI 1908
Vehicles, Visualization, Videos, Accidents, Risk management, Hazards, Traffic rick assessment, dynamic-attention, optical flow BibRef

Fraga-Lamas, P.[Paula], Ramos, L.[Lucía], Mondéjar-Guerra, V.[Víctor], Fernández-Caramés, T.M.[Tiago M.],
A Review on IoT Deep Learning UAV Systems for Autonomous Obstacle Detection and Collision Avoidance,
RS(11), No. 18, 2019, pp. xx-yy.
DOI Link 1909
BibRef

Lin, C.,
Cross Domain Adaptation for on-Road Object Detection Using Multimodal Structure-Consistent Image-to-Image Translation,
ICIP19(3029-3030)
IEEE DOI 1910
generative adversarial network, domain adaptation, image-to-image translation BibRef

Chen, J.Y.[Jun-Ying], Bai, T.Y.[Tong-Yao],
SAANet: Spatial adaptive alignment network for object detection in automatic driving,
IVC(94), 2020, pp. 103873.
Elsevier DOI 2003
Object detection, Fusion-based deep framework, Local orientation encoding, Spatial adaptive alignment, Autonomous driving BibRef

Santana, M.C.S.[Marcos C. S.], Passos Júnior, L.A.[Leandro Aparecido], Moreira, T.P.[Thierry P.], Colombo, D.[Danilo], de Albuquerque, V.H.C.[Victor Hugo C.], Papa, J.P.[João Paulo],
A Novel Siamese-Based Approach for Scene Change Detection With Applications to Obstructed Routes in Hazardous Environments,
IEEE_Int_Sys(35), No. 1, January 2020, pp. 44-53.
IEEE DOI 2004
Decoding, Image segmentation, Semantics, Training data, Neural networks, Intelligent systems, Task analysis, Route Obstruction Detection BibRef

Brambilla, M., Nicoli, M., Soatti, G., Deflorio, F.,
Augmenting Vehicle Localization by Cooperative Sensing of the Driving Environment: Insight on Data Association in Urban Traffic Scenarios,
ITS(21), No. 4, April 2020, pp. 1646-1663.
IEEE DOI 2004
Global navigation satellite system, Sensors, Vehicle-to-everything, Feature extraction, Roads, Standards, controlled arterials BibRef

Leng, J., Liu, Y., Du, D., Zhang, T., Quan, P.,
Robust Obstacle Detection and Recognition for Driver Assistance Systems,
ITS(21), No. 4, April 2020, pp. 1560-1571.
IEEE DOI 2004
Obstacle location, obstacle recognition, U-V disparity map, neural network, road extraction BibRef

Shen, C.[Chao], Zhao, X.M.[Xiang-Mo], Liu, Z.W.[Zhan-Wen], Gao, T.[Tao], Xu, J.[Jiang],
Joint vehicle detection and distance prediction via monocular depth estimation,
IET-ITS(14), No. 7, July 2020, pp. 753-763.
DOI Link 2006
BibRef

Gao, M.[Ming], Jin, L.S.[Li-Sheng], Jiang, Y.Y.[Yu-Ying], Bie, J.[Jing],
Multiple object tracking using a dual-attention network for autonomous driving,
IET-ITS(14), No. 8, August 2020, pp. 842-848.
DOI Link 2007
BibRef

Li, J.[Jing], Shi, X.X.[Xin-Xin], Wang, J.Z.[Jun-Zheng], Yan, M.[Min],
Adaptive road detection method combining lane line and obstacle boundary,
IET-IPR(14), No. 10, August 2020, pp. 2216-2226.
DOI Link 2008
BibRef

Lin, Y.X.[Yong-Xiang], Tan, D.S.[Daniel Stanley], Chen, Y.Y.[Yung-Yao], Huang, C.C.[Ching-Chun], Hua, K.L.[Kai-Lung],
Domain Adaptation With Foreground/Background Cues and Gated Discriminators,
MultMedMag(27), No. 3, July 2020, pp. 44-53.
IEEE DOI 2009
Image segmentation, Semantics, Adaptation models, Logic gates, Automobiles, Training data, Computer science, Autonmous automobiles BibRef

Xue, F., Ming, A., Zhou, Y.,
Tiny Obstacle Discovery by Occlusion-Aware Multilayer Regression,
IP(29), 2020, pp. 9373-9386.
IEEE DOI 1806
Image edge detection, Proposals, Nonhomogeneous media, Roads, Cameras, Training, Obstacle discovery, regression BibRef

Prasad, D.K., Dong, H., Rajan, D., Quek, C.,
Are Object Detection Assessment Criteria Ready for Maritime Computer Vision?,
ITS(21), No. 12, December 2020, pp. 5295-5304.
IEEE DOI 2012
Measurement, Object detection, Sensors, Indexes, Image edge detection, Artificial intelligence, Object detection, performance evaluation BibRef

Chen, L., Zou, Q., Pan, Z., Lai, D., Zhu, L., Hou, Z., Wang, J., Cao, D.,
Surrounding Vehicle Detection Using an FPGA Panoramic Camera and Deep CNNs,
ITS(21), No. 12, December 2020, pp. 5110-5122.
IEEE DOI 2012
Cameras, Videos, Vehicle detection, Lenses, Field programmable gate arrays, Real-time systems, autonomous vehicle BibRef

Hnewa, M., Radha, H.,
Object Detection Under Rainy Conditions for Autonomous Vehicles: A Review of State-of-the-Art and Emerging Techniques,
SPMag(38), No. 1, January 2021, pp. 53-67.
IEEE DOI 2012
Training, Visualization, Object detection, Data models, Safety, Autonomous vehicles, Testing BibRef

Chen, S., Liu, B., Feng, C., Vallespi-Gonzalez, C., Wellington, C.,
3D Point Cloud Processing and Learning for Autonomous Driving: Impacting Map Creation, Localization, and Perception,
SPMag(38), No. 1, January 2021, pp. 68-86.
IEEE DOI 2012
Laser radar, Tools, Sensors, Autonomous vehicles, Surface treatment, Videos BibRef

Carranza-García, M.[Manuel], Torres-Mateo, J.[Jesús], Lara-Benítez, P.[Pedro], García-Gutiérrez, J.[Jorge],
On the Performance of One-Stage and Two-Stage Object Detectors in Autonomous Vehicles Using Camera Data,
RS(13), No. 1, 2021, pp. xx-yy.
DOI Link 2101
BibRef

Shekar, A.K.[Arvind Kumar], Gou, L.[Liang], Ren, L.[Liu], Wendt, A.[Axel],
Label-Free Robustness Estimation of Object Detection CNNs for Autonomous Driving Applications,
IJCV(129), No. 4, April 2021, pp. 1185-1201.
Springer DOI 2104
BibRef

Li, X.[Xi], Ma, H.M.[Hui-Min], Yi, S.[Sheng], Chen, Y.X.[Yan-Xian], Ma, H.B.[Hong-Bing],
Single annotated pixel based weakly supervised semantic segmentation under driving scenes,
PR(116), 2021, pp. 107979.
Elsevier DOI 2106
Weakly supervised condition, Semantic segmentation, Complex driving scenes, Optimal feature setting BibRef

Lee, H.[Hojoon], Yoon, J.[Jeongsik], Jeong, Y.[Yonghwan], Yi, K.[Kyongsu],
Moving Object Detection and Tracking Based on Interaction of Static Obstacle Map and Geometric Model-Free Approachfor Urban Autonomous Driving,
ITS(22), No. 6, June 2021, pp. 3275-3284.
IEEE DOI 2106
Radar tracking, Laser radar, Real-time systems, Estimation, Target tracking, LiDAR BibRef

Wu, Y.N.[Ya-Nan], Feng, S.H.[Song-He], Huang, X.K.[Xian-Kai], Wu, Z.Z.[Zi-Zhang],
L4Net: An anchor-free generic object detector with attention mechanism for autonomous driving,
IET-CV(15), No. 1, 2021, pp. 36-46.
DOI Link 2106
BibRef

Liu, Y.Z.[Ya-Zhou], Cao, S.[Sen], Lasang, P.[Pongsak], Shen, S.[Shengmei],
Modular Lightweight Network for Road Object Detection Using a Feature Fusion Approach,
SMCS(51), No. 8, August 2021, pp. 4716-4728.
IEEE DOI 2107
Object detection, Feature extraction, Computational modeling, Convolution, Task analysis, Deep learning, Roads, object detection BibRef

Ke, X.[Xiao], Li, J.P.[Jian-Ping],
U-FPNDet: A one-shot traffic object detector based on U-shaped feature pyramid module,
IET-IPR(15), No. 10, 2021, pp. 2146-2156.
DOI Link 2108
Include vehicles and pedestrians. BibRef

Ren, J.[Jia], Zhang, J.[Jing], Cui, Y.[Yani],
Autonomous Obstacle Avoidance Algorithm for Unmanned Surface Vehicles Based on an Improved Velocity Obstacle Method,
IJGI(10), No. 9, 2021, pp. xx-yy.
DOI Link 2109
BibRef

Lu, Q.[Qun], Zhang, D.[Dan], Ye, W.J.[Wen-Jun], Fan, J.Y.[Jing-Yu], Liu, S.[Steven], Su, C.Y.[Chun-Yi],
Targeting Posture Control With Dynamic Obstacle Avoidance of Constrained Uncertain Wheeled Mobile Robots Including Unknown Skidding and Slipping,
SMCS(51), No. 11, November 2021, pp. 6650-6659.
IEEE DOI 2110
Wheels, Mobile robots, Uncertainty, Collision avoidance, Trajectory tracking, Kinematics, Input saturation, wheeled mobile robots (WMRs) BibRef

Cai, Y.F.[Ying-Feng], Dai, L.[Lei], Wang, H.[Hai], Li, Z.X.[Zhi-Xiong],
Multi-Target Pan-Class Intrinsic Relevance Driven Model for Improving Semantic Segmentation in Autonomous Driving,
IP(30), 2021, pp. 9069-9084.
IEEE DOI 2112
Semantics, Feature extraction, Context modeling, Automobiles, Image segmentation, Convolution, Buildings, semantic segmentation BibRef

Zhang, P.P.[Ping-Ping], Liu, W.[Wei], Lei, Y.J.[Yin-Jie], Lu, H.C.[Hu-Chuan],
Semantic Scene Labeling via Deep Nested Level Set,
ITS(22), No. 11, November 2021, pp. 6853-6865.
IEEE DOI 2112
Assisted driving. Level set, Semantics, Labeling, Feature extraction, Deep learning, Image segmentation, Intelligent transportation systems, fully convolutional network BibRef

Yang, K.L.[Kai-Lun], Hu, X.X.[Xin-Xin], Fang, Y.C.[Yi-Cheng], Wang, K.W.[Kai-Wei], Stiefelhagen, R.[Rainer],
Omnisupervised Omnidirectional Semantic Segmentation,
ITS(23), No. 2, February 2022, pp. 1184-1199.
IEEE DOI 2202
Semantics, Image segmentation, Training, Data models, Sensors, Task analysis, Cameras, Intelligent vehicles, scene understanding, omnidirectional images BibRef

Kim, Y.[Youngjun], Hwang, H.Y.[Hyek-Young], Shin, J.[Jitae],
Robust object detection under harsh autonomous-driving environments,
IET-IPR(16), No. 4, 2022, pp. 958-971.
DOI Link 2203
BibRef

Qiao, J.J.[Jian-Jun], Wu, X.[Xiao], He, J.Y.[Jun-Yan], Li, W.[Wei], Peng, Q.[Qiang],
SWNet: A Deep Learning Based Approach for Splashed Water Detection on Road,
ITS(23), No. 4, April 2022, pp. 3012-3025.
IEEE DOI 2204
Roads, Meteorology, Semantics, Deep learning, Accidents, Image segmentation, Water, Splashed water detection, deep learning BibRef

Singh, A.S.P.[Amrik Singh Phuman], Nishihara, O.[Osamu],
Trajectory Tracking and Integrated Chassis Control for Obstacle Avoidance With Minimum Jerk,
ITS(23), No. 5, May 2022, pp. 4625-4641.
IEEE DOI 2205
Tires, Trajectory, Collision avoidance, Force, Friction, Mathematical model, Trajectory tracking, Autonomous vehicle, sliding mode control BibRef

Geisler, S.[Simon], Cunha, C.[Carlos], Satzoda, R.K.[Ravi Kumar],
Better, Faster Small Hazard Detection: Instance-Aware Techniques, Metrics and Benchmarking,
ITS(23), No. 7, July 2022, pp. 9062-9077.
IEEE DOI 2207
Hazards, Semantics, Roads, Image segmentation, Object detection, Measurement, Benchmark testing, Small hazard detection, disparity BibRef

Zhou, W.[Weina], Huang, X.X.[Xin-Xin], Zeng, X.Y.[Xiao-Yang],
Obstacle Detection for Unmanned Surface Vehicles by Fusion Refinement Network,
IEICE(E105-D), No. 8, August 2022, pp. 1393-1400.
WWW Link. 2207
BibRef

Pano, B.[Béatrice], Chevrel, P.[Philippe], Claveau, F.[Fabien], Sentouh, C.[Chouki], Mars, F.[Franck],
Obstacle Avoidance in Highly Automated Cars: Can Progressive Haptic Shared Control Make it Safer and Smoother?,
HMS(52), No. 4, August 2022, pp. 547-556.
IEEE DOI 2208
Vehicles, Torque, Manuals, Wheels, Roads, Haptic interfaces, Autonomous vehicles, Autonomous vehicles, driver behavior, multiobjective ( H_2/H_inf ) control BibRef

Badrloo, S.[Samira], Varshosaz, M.[Masood], Pirasteh, S.[Saied], Li, J.[Jonathan],
Image-Based Obstacle Detection Methods for the Safe Navigation of Unmanned Vehicles: A Review,
RS(14), No. 15, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Song, P.F.[Peng-Fei], Fan, H.[Hui], Li, J.J.[Jin-Jiang], Hua, F.[Feng],
Attention-Based Multi-Scale Feature Fusion for Free-Space Detection,
IET-ITS(16), No. 9, 2022, pp. 1222-1235.
DOI Link 2208
BibRef

Zhang, H.[Hui], Luo, G.[Guiyang], Li, J.L.[Jing-Lin], Wang, F.Y.[Fei-Yue],
C2FDA: Coarse-to-Fine Domain Adaptation for Traffic Object Detection,
ITS(23), No. 8, August 2022, pp. 12633-12647.
IEEE DOI 2208
Feature extraction, Object detection, Adaptation models, Proposals, Meteorology, Visualization, Detectors, Object detection, intelligent visual perception BibRef

Feng, D.[Di], Wang, Z.N.[Zi-Ning], Zhou, Y.Y.[Yi-Yang], Rosenbaum, L.[Lars], Timm, F.[Fabian], Dietmayer, K.[Klaus], Tomizuka, M.[Masayoshi], Zhan, W.[Wei],
Labels are Not Perfect: Inferring Spatial Uncertainty in Object Detection,
ITS(23), No. 8, August 2022, pp. 9981-9994.
IEEE DOI 2208
Uncertainty, Object detection, Laser radar, Measurement, Probabilistic logic, Detectors, Uncertainty estimation, deep learning BibRef

Liao, J.C.[Jia-Cai], Cao, L.[Libo], Luo, X.L.[Xiao-Le], Sun, X.[Xu], Duan, C.[Cong], Li, J.H.[Jian-Hua], Yuan, F.[Feng],
Road Garbage Segmentation With Deep Supervision and High Fusion Network for Cleaning Vehicles,
ITS(23), No. 8, August 2022, pp. 11190-11204.
IEEE DOI 2208
Roads, Image segmentation, Cleaning, Semantics, Feature extraction, Deep learning, Training, Semantic segmentation, intelligent vehicle BibRef

Bovcon, B.[Borja], Muhovic, J.[Jon], Vranac, D.[Duško], Mozetic, D.[Dean], Perš, J.[Janez], Kristan, M.[Matej],
MODS: A USV-Oriented Object Detection and Obstacle Segmentation Benchmark,
ITS(23), No. 8, August 2022, pp. 13403-13418.
IEEE DOI 2208
Visualization, Object detection, Benchmark testing, Image segmentation, Training, Sea measurements, Surveillance, benchmark BibRef

Song, T.J.[Taek-Jin], Jeong, J.[Jongoh], Kim, J.H.[Jong-Hwan],
End-to-End Real-Time Obstacle Detection Network for Safe Self-Driving via Multi-Task Learning,
ITS(23), No. 9, September 2022, pp. 16318-16329.
IEEE DOI 2209
Semantics, Feature extraction, Roads, Real-time systems, Task analysis, Image segmentation, Point cloud compression, real-time perception BibRef

Shepel, I.[Ilya], Adeshkin, V.[Vasily], Belkin, I.[Ilya], Yudin, D.A.[Dmitry A.],
Occupancy Grid Generation With Dynamic Obstacle Segmentation in Stereo Images,
ITS(23), No. 9, September 2022, pp. 14779-14789.
IEEE DOI 2209
Semantics, Point cloud compression, Laser radar, Cameras, Image segmentation, Sensors, Heuristic algorithms, Occupancy grid, unmanned ground vehicle BibRef

Su, Q.H.[Qing-Hua], Wang, H.D.[Hao-Dong], Xie, M.[Min], Song, Y.[Yue], Ma, S.B.[Shao-Bo], Li, B.X.[Bo-Xiong], Yang, Y.[Ying], Wang, L.[Liyong],
Real-time traffic cone detection for autonomous driving based on YOLOv4,
IET-ITS(16), No. 10, 2022, pp. 1380-1390.
DOI Link 2209
BibRef

Raja, G.[Gunasekaran], Anbalagan, S.[Sudha], Senthilkumar, S.[Senbagapriya], Dev, K.[Kapal], Qureshi, N.M.F.[Nawab Muhammad Faseeh],
SPAS: Smart Pothole-Avoidance Strategy for Autonomous Vehicles,
ITS(23), No. 10, October 2022, pp. 19827-19836.
IEEE DOI 2210
Roads, Speech recognition, Computational modeling, Convolutional neural networks, Autonomous vehicles, speech and gesture recognition BibRef

Li, X.X.[Xiao-Xiao], Xu, Z.H.[Zhi-Hao], Li, S.[Shuai], Su, Z.R.[Ze-Rong], Zhou, X.F.[Xue-Feng],
Simultaneous Obstacle Avoidance and Target Tracking of Multiple Wheeled Mobile Robots With Certified Safety,
Cyber(52), No. 11, November 2022, pp. 11859-11873.
IEEE DOI 2211
Robots, Collision avoidance, Mobile robots, Target tracking, Wheels, Manipulators, Safety, Collision avoidance, motion planning, quadratic programming BibRef

Li, X.X.[Xiao-Xiao], Xu, Z.H.[Zhi-Hao], Su, Z.R.[Ze-Rong], Wang, H.P.[Hong-Peng], Li, S.[Shuai],
Distance- and Velocity-Based Simultaneous Obstacle Avoidance and Target Tracking for Multiple Wheeled Mobile Robots,
ITS(25), No. 2, February 2024, pp. 1736-1748.
IEEE DOI 2402
Robots, Collision avoidance, Mobile robots, Wheels, Kinematics, Task analysis, Safety, Collision avoidance, trajectory tracking, optimization BibRef

Mosbah, R.[Ramzi], Guezouli, L.[Larbi],
Convolutional neural networks for obstacle detection on the road and driving assistance,
IJCVR(12), No. 5, 2022, pp. 573-594.
DOI Link 2211
BibRef

Liu, C.L.[Cheng-Long], Nie, T.[Tong], Du, Y.C.[Yu-Chuan], Cao, J.[Jing], Wu, D.F.[Di-Fei], Li, F.[Feng],
A Response-Type Road Anomaly Detection and Evaluation Method for Steady Driving of Automated Vehicles,
ITS(23), No. 11, November 2022, pp. 21984-21995.
IEEE DOI 2212
Roads, Anomaly detection, Vibrations, Vehicle dynamics, Estimation, Automobiles, Tires, Road anomaly detection, automated vehicles, comfort evaluation BibRef

Ramli, M.F.[Muhammad Faiz], Shamsudin, S.S.[Syariful Syafiq],
Obstacle detection technique to solve poor texture appearance of the obstacle by categorising image's region using cues from expansion of feature points for small UAV,
IJCVR(13), No. 1, 2023, pp. 91-115.
DOI Link 2212
BibRef

Jiang, J.[Jianwu], Li, F.[Fuda], Yang, J.T.[Jun-Tao], Kang, Z.Z.[Zhi-Zhong], Li, J.W.[Jing-Wen],
Construction of indoor obstacle element map based on scene-aware priori obstacle rules,
PandRS(195), 2023, pp. 43-64.
Elsevier DOI 2301
Obstacle element map, Obstacle mobility recognition, Obstacle extraction, Scene recognition, Point cloud semantic segmentation BibRef

Li, F., Wang, H., Akwensi, P.H., Kang, Z.Z.[Zhi-Zhong],
Construction of Obstacle Element Map Based On Indoor Scene Recognition,
Indoor3D19(819-825).
DOI Link 1912
BibRef

Ding, J.C.[Jian-Chuan], Gao, L.P.[Ling-Ping], Liu, W.X.[Wen-Xi], Piao, H.[Haiyin], Pan, J.[Jia], Du, Z.J.[Zhen-Jun], Yang, X.[Xin], Yin, B.C.[Bao-Cai],
Monocular Camera-Based Complex Obstacle Avoidance via Efficient Deep Reinforcement Learning,
CirSysVideo(33), No. 2, February 2023, pp. 756-770.
IEEE DOI 2302
Collision avoidance, Robots, Robot sensing systems, Semantics, Measurement by laser beam, Cameras, Sensors, robot navigation BibRef

Luo, Z.P.[Zhi-Peng], Gao, L.P.[Li-Peng], Xiang, H.D.[Hao-Dong], Li, J.[Jonathan],
Road object detection for HD map: Full-element survey, analysis and perspectives,
PandRS(197), 2023, pp. 122-144.
Elsevier DOI 2303
High-Definition map, Autonomous driving, Road surface information extraction, Road object detection, 3D point clouds BibRef

Chen, Y.L.[Yi-Lun], Huang, S.[Shijia], Liu, S.[Shu], Yu, B.[Bei], Jia, J.Y.[Jia-Ya],
DSGN++: Exploiting Visual-Spatial Relation for Stereo-Based 3D Detectors,
PAMI(45), No. 4, April 2023, pp. 4416-4429.
IEEE DOI 2303
Feature extraction, Detectors, Cameras, Sensors, Costs, Solid modeling, 3D object detection, stereo matching, autonomous driving BibRef

Gao, X.S.[Xiao-Shan], Yan, L.[Liang], Li, Z.J.[Zhi-Jun], Wang, G.[Gang], Chen, I.M.[I-Ming],
Improved Deep Deterministic Policy Gradient for Dynamic Obstacle Avoidance of Mobile Robot,
SMCS(53), No. 6, June 2023, pp. 3675-3682.
IEEE DOI 2305
Mobile robots, Collision avoidance, Heuristic algorithms, Q-learning, Navigation, Fuzzy logic, Force, obstacle avoidance BibRef

Chen, C.[Chen], Yao, G.R.[Guo-Run], Liu, L.[Lei], Pei, Q.Q.[Qing-Qi], Song, H.B.[Hou-Bing], Dustdar, S.[Schahram],
A Cooperative Vehicle-Infrastructure System for Road Hazards Detection With Edge Intelligence,
ITS(24), No. 5, May 2023, pp. 5186-5198.
IEEE DOI 2305
Roads, Image edge detection, Feature extraction, Accidents, Data models, Task analysis, Training, knowledge distillation BibRef

Nguyen, V.D.[Vinh Dinh], Trinh, T.D.[Thong Duc], Tran, H.N.[Hoang Ngoc],
A Robust Triangular Sigmoid Pattern-Based Obstacle Detection Algorithm in Resource-Limited Devices,
ITS(24), No. 6, June 2023, pp. 5936-5945.
IEEE DOI 2306
Feature extraction, Object detection, Roads, Classification algorithms, Lighting, Charge coupled devices, deep learning model BibRef

Wang, Y.T.[Yi-Tian], Lin, J.[Jun], Zhang, L.[Liu], Wang, T.H.[Tian-Hao], Xu, H.[Hao], Qi, Y.[Yuehan], Zhang, G.[Guanyu], Liu, Y.[Yang],
Stable Obstacle Avoidance Strategy for Crawler-Type Intelligent Transportation Vehicle in Non-Structural Environment Based on Attention-Learning,
ITS(24), No. 7, July 2023, pp. 7813-7830.
IEEE DOI 2307
Collision avoidance, Deep learning, Autonomous vehicles, Acoustics, Humanoid robots, Stability criteria, Roads, Attention-LSTM, obstacle avoidance strategy BibRef

Fu, Y.J.[Yong-Jian], Gao, D.[Dingli], Liu, T.[Ting], Zheng, H.[Hang], Hao, D.[Dayang], Pan, Z.J.[Zhi-Jie],
Evolving Into a Transformer: From a Training-Free Retrieval-Based Method for Anomaly Obstacle Segmentation,
IP(32), 2023, pp. 6195-6209.
IEEE DOI 2311
BibRef

Vitale, F.[Francesco], Roncoli, C.[Claudio],
Reference Tracking Optimization With Obstacle Avoidance via Task Prioritization for Automated Driving,
ITS(25), No. 2, February 2024, pp. 1200-1214.
IEEE DOI 2402
Task analysis, Trajectory, Collision avoidance, Behavioral sciences, Safety, Vehicle dynamics, Planning, mixed traffic BibRef

Shi, Y.[Yi], Zhao, S.X.[Shi-Xuan], Wu, J.[Jiang], Wu, Z.B.[Zhang-Bi], Yan, H.M.[Hong-Mei],
Fixated Object Detection Based on Saliency Prior in Traffic Scenes,
CirSysVideo(34), No. 3, March 2024, pp. 1413-1426.
IEEE DOI Code:
WWW Link. 2403
Object detection, Visualization, Task analysis, Computational modeling, Predictive models, Safety, Vehicles, deep learning BibRef

Kuang, J.J.[Jian-Jie], Tan, G.F.[Gang-Feng], Guo, X.X.[Xue-Xun], Pei, X.F.[Xiao-Fei], Peng, D.Z.[Deng-Zhi],
Research of obstacle vehicles avoidance for automated heavy vehicle platoon by switching the formation,
IET-ITS(18), No. 4, 2024, pp. 630-644.
DOI Link 2404
automated driving and intelligent vehicles, collision avoidance, decision making, path planning, velocity control BibRef

Zhang, X.Z.[Xi-Zheng], Cao, X.[Xu], Zhang, H.[Hui], Shen, Y.P.[Yong-Peng], Yuan, X.F.[Xiao-Fang], Cui, Z.J.[Zi-Jian], Lu, Z.Y.[Zhang-Yu],
An Intelligent Obstacle Detection for Autonomous Mining Transportation With Electric Locomotive via Cellular Vehicle-to-Everything and Vehicular Edge Computing,
ITS(25), No. 3, March 2024, pp. 3177-3190.
IEEE DOI 2405
Feature extraction, 6G mobile communication, Transportation, Image edge detection, Edge computing, Detection algorithms, attention mechanism BibRef

Sun, B.Y.[Bang-Yong], Ma, M.[Ming], Yuan, N.Z.[Nian-Zeng], Li, J.H.[Jun-Huai], Yu, T.[Tao],
Detecting the Background-Similar Objects in Complex Transportation Scenes,
ITS(25), No. 3, March 2024, pp. 2920-2932.
IEEE DOI 2405
Feature extraction, Task analysis, Semantics, Roads, Object detection, Meteorology, Transportation, guide-learning BibRef

Ding, M.[Meng], Guan, S.[Song], Liu, H.[Hao], Yu, K.[Kuaikuai],
TIR-YOLO-ADAS: A thermal infrared object detection framework for advanced driver assistance systems,
IET-ITS(18), No. 5, 2024, pp. 822-834.
DOI Link 2405
advanced driver assistance systems, infrared detectors, object detection BibRef

Zhu, Y.[Yuan], Xu, R.D.[Rui-Dong], Tao, C.[Chongben], An, H.[Hao], Wang, H.[Huaide], Sun, Z.P.[Zhi-Peng], Lu, K.[Ke],
DS-Trans: A 3D Object Detection Method Based on a Deformable Spatiotemporal Transformer for Autonomous Vehicles,
RS(16), No. 9, 2024, pp. 1621.
DOI Link 2405
BibRef

Du, B.[Bin], Xie, W.[Wei], Zhang, W.D.[Wei-Dong], Chen, H.T.[Hong-Tian],
A Target Tracking Guidance for Unmanned Surface Vehicles in the Presence of Obstacles,
ITS(25), No. 5, May 2024, pp. 4102-4115.
IEEE DOI Code:
WWW Link. 2405
Target tracking, Navigation, Collision avoidance, Vehicle dynamics, Rivers, Reinforcement learning, Marine vehicles, obstacle avoidance BibRef

Liu, Y.Z.[Ya-Zhou], Wei, X.Y.[Xiang-Yu], Lasang, P.[Pongsak], Pranata, S.[Sugiri], Subramanian, K.[Karthikk], Seow, H.[Hocktong],
Ensemble Uncertainty Guided Road Scene Anomaly Detection: A Simple Meta-Learning Approach,
ITS(25), No. 9, September 2024, pp. 10754-10765.
IEEE DOI Code:
WWW Link. 2409
Uncertainty, Roads, Anomaly detection, Training data, Data models, Training, Task analysis, Anomaly detection, semantic segmentation, meta learning BibRef

Fan, J.[Jiayu], Murgovski, N.[Nikolce], Liang, J.[Jun], Elawad, A.[Amal],
Exact Obstacle Avoidance for Autonomous Vehicles in Polygonal Domains,
SMCS(54), No. 10, October 2024, pp. 5964-5976.
IEEE DOI 2410
Collision avoidance, Autonomous vehicles, Trajectory planning, Planning, Shape, Trajectory, Vectors, Autonomous vehicles, nonconvex vehicles and obstacles BibRef

Zeng, S.[Shuai], Zheng, W.Z.[Wen-Zhao], Lu, J.W.[Ji-Wen], Yan, H.B.[Hai-Bin],
Hardness-Aware Scene Synthesis for Semi-Supervised 3D Object Detection,
MultMed(26), 2024, pp. 9644-9656.
IEEE DOI 2410
Object detection, Point cloud compression, Training, Semisupervised learning, Data models, Predictive models, autonomous driving BibRef

Gratzer, A.L.[Alexander L.], Broger, M.M.[Maximilian M.], Schirrer, A.[Alexander], Jakubek, S.[Stefan],
Two-Layer MPC Architecture for Efficient Mixed-Integer-Informed Obstacle Avoidance in Real-Time,
ITS(25), No. 10, October 2024, pp. 13767-13784.
IEEE DOI 2410
Collision avoidance, Real-time systems, Predictive models, Computer architecture, Computational modeling, Vehicle dynamics, single-track model BibRef

Zhang, H.C.[Han-Cheng], Hu, Y.Y.[Yuan-Yuan], Qian, Z.D.[Zhen-Dong], Sha, J.[Jirui], Xie, M.[Min], Wan, Y.Y.[Yu-Yang], Liu, P.F.[Peng-Fei],
Enhancing Rare Object Detection on Roadways Through Conditional Diffusion Models for Data Augmentation,
ITS(25), No. 11, November 2024, pp. 19018-19029.
IEEE DOI 2411
Diffusion models, Training, Object detection, Accuracy, Roads, Noise measurement, Gaussian noise, Traffic scene perception, data augmentation BibRef

Chen, H.[Hao], Min, B.W.[Byung-Won], Zhang, H.[Haifei],
A study on a target detection model for autonomous driving tasks,
IET-IPR(18), No. 12, 2024, pp. 3447-3459.
DOI Link 2411
image classification, image processing, learning (artificial intelligence) BibRef

Song, Z.[Ziying], Liu, L.[Lin], Jia, F.[Feiyang], Luo, Y.[Yadan], Jia, C.Y.[Cai-Yan], Zhang, G.X.[Guo-Xin], Yang, L.[Lei], Wang, L.[Li],
Robustness-Aware 3D Object Detection in Autonomous Driving: A Review and Outlook,
ITS(25), No. 11, November 2024, pp. 15407-15436.
IEEE DOI 2411
Robustness, Object detection, Autonomous vehicles, Accuracy, Sensors, Noise, 3D object detection, perception, robustness, autonomous driving BibRef

Lin, J.P.[Jin-Peng], Liang, Z.H.[Zhi-Hao], Deng, S.[Shengheng], Cai, L.[Lile], Jiang, T.[Tao], Li, T.R.[Tian-Rui], Jia, K.[Kui], Xu, X.[Xun],
Exploring Diversity-Based Active Learning for 3D Object Detection in Autonomous Driving,
ITS(25), No. 11, November 2024, pp. 15454-15466.
IEEE DOI 2411
Object detection, Costs, Annotations, Detectors, Uncertainty, Diversity reception, Feature extraction, Autonomous vehicles, autonomous driving BibRef


Shi, Y.N.[Yi-Ning], Jiang, K.[Kun], Wang, K.[Ke], Li, J.[Jiusi], Wang, Y.L.[Yun-Long], Yang, M.M.[Meng-Meng], Yang, D.[Diange],
StreamingFlow: Streaming Occupancy Forecasting with Asynchronous Multi-modal Data Streams via Neural Ordinary Differential Equation,
CVPR24(14833-14842)
IEEE DOI 2410
Training, Interpolation, Recurrent neural networks, Ordinary differential equations, Sensor fusion, Streaming perception BibRef

Tang, P.[Pin], Wang, Z.D.[Zhong-Dao], Wang, G.Q.[Guo-Qing], Zheng, J.[Jilai], Ren, X.X.[Xiang-Xuan], Feng, B.[Bailan], Ma, C.[Chao],
SparseOcc: Rethinking Sparse Latent Representation for Vision-Based Semantic Occupancy Prediction,
CVPR24(15035-15044)
IEEE DOI 2410
Interpolation, Technological innovation, Solid modeling, Sparse approximation, Scalability, Semantics, Occupancy Prediction, Autonomous Driving BibRef

Huang, K.C.[Kuan-Chih], Lyu, W.J.[Wei-Jie], Yang, M.H.[Ming-Hsuan], Tsai, Y.H.[Yi-Hsuan],
PTT: Point-Trajectory Transformer for Efficient Temporal 3D Object Detection,
CVPR24(14938-14947)
IEEE DOI Code:
WWW Link. 2410
Point cloud compression, Representation learning, Runtime, Memory management, Object detection, Detectors, Autonomous Driving BibRef

Xia, Y.[Yan], Shi, L.[Letian], Ding, Z.[Zifeng], Henriques, J.F.[João F.], Cremers, D.[Daniel],
Text2Loc: 3D Point Cloud Localization from Natural Language,
CVPR24(14958-14967)
IEEE DOI Code:
WWW Link. 2410
Location awareness, Point cloud compression, Visualization, Accuracy, Semantics, Pipelines, 3D localization, point cloud, text, autonomous driving BibRef

Shoeb, Y., Chan, R., Schwalbe, G., Nowzad, A., Güney, F., Gottschalk, H.,
Have We Ever Encountered This Before? Retrieving Out-of-Distribution Road Obstacles from Driving Scenes,
WACV24(7381-7391)
IEEE DOI 2404
Image segmentation, Roads, Training data, Streaming media, Safety, Recording, Task analysis, Applications, Autonomous Driving, Vision + language and/or other modalities BibRef

Tong, W.W.[Wen-Wen], Sima, C.[Chonghao], Wang, T.[Tai], Chen, L.[Li], Wu, S.[Silei], Deng, H.M.[Han-Ming], Gu, Y.[Yi], Lu, L.W.[Le-Wei], Luo, P.[Ping], Lin, D.[Dahua], Li, H.Y.[Hong-Yang],
Scene as Occupancy,
ICCV23(8372-8381)
IEEE DOI 2401
BibRef

Wei, Y.[Yi], Zhao, L.Q.[Lin-Qing], Zheng, W.Z.[Wen-Zhao], Zhu, Z.[Zheng], Zhou, J.[Jie], Lu, J.W.[Ji-Wen],
SurroundOcc: Multi-Camera 3D Occupancy Prediction for Autonomous Driving,
ICCV23(21672-21683)
IEEE DOI Code:
WWW Link. 2401
BibRef

Agro, B.[Ben], Sykora, Q.[Quinlan], Casas, S.[Sergio], Urtasun, R.[Raquel],
Implicit Occupancy Flow Fields for Perception and Prediction in Self-Driving,
CVPR23(1379-1388)
IEEE DOI 2309
BibRef

Choe, T.E.[Tae Eun], Wu, J.[Jane], Lin, X.L.[Xiao-Lin], Kwon, K.[Karen], Park, M.W.[Min-Woo],
HazardNet: Road Debris Detection by Augmentation of Synthetic Models,
WAD23(161-171)
IEEE DOI 2309
BibRef

Lambert, R.[Reeve], Li, J.W.[Jian-Wen], Chavez-Galaviz, J.[Jalil], Mahmoudian, N.[Nina],
A Survey on the Deployability of Semantic Segmentation Networks for Fluvial Navigation,
Maritime23(255-264)
IEEE DOI 2302
Training, Embedded systems, Navigation, Semantic segmentation, Semantics, Neural networks, Training data BibRef

Tian, Y.[Yu], Liu, Y.Y.[Yu-Yuan], Pang, G.S.[Guan-Song], Liu, F.[Fengbei], Chen, Y.H.[Yuan-Hong], Carneiro, G.[Gustavo],
Pixel-Wise Energy-Biased Abstention Learning for Anomaly Segmentation on Complex Urban Driving Scenes,
ECCV22(XXIX:246-263).
Springer DOI 2211
BibRef

Li, K.[Kaican], Chen, K.[Kai], Wang, H.Y.[Hao-Yu], Hong, L.Q.[Lan-Qing], Ye, C.Q.[Chao-Qiang], Han, J.H.[Jian-Hua], Chen, Y.[Yukuai], Zhang, W.[Wei], Xu, C.J.[Chun-Jing], Yeung, D.Y.[Dit-Yan], Liang, X.D.[Xiao-Dan], Li, Z.G.[Zhen-Guo], Xu, H.[Hang],
CODA: A Real-World Road Corner Case Dataset for Object Detection in Autonomous Driving,
ECCV22(XXXVIII:406-423).
Springer DOI 2211
BibRef

Luo, C.X.[Chen-Xu], Yang, X.D.[Xiao-Dong], Yuille, A.L.[Alan L.],
Exploring Simple 3D Multi-Object Tracking for Autonomous Driving,
ICCV21(10468-10477)
IEEE DOI 2203
Point cloud compression, Solid modeling, Pediatrics, Laser radar, Tracking, Motion estimation, Motion and tracking, BibRef

Jung, S.[Sanghun], Lee, J.[Jungsoo], Gwak, D.[Daehoon], Choi, S.[Sungha], Choo, J.[Jaegul],
Standardized Max Logits: A Simple yet Effective Approach for Identifying Unexpected Road Obstacles in Urban-Scene Segmentation,
ICCV21(15405-15414)
IEEE DOI 2203
Training, Image segmentation, Visualization, Smoothing methods, Roads, Semantics, Vision for robotics and autonomous vehicles, grouping and shape BibRef

Žust, L.[Lojze], Kristan, M.[Matej],
Learning Maritime Obstacle Detection from Weak Annotations by Scaffolding,
WACV22(1888-1897)
IEEE DOI 2202
Training, Costs, Annotations, Image edge detection, Sea measurements, Labeling, Vision for Robotics BibRef

Robinet, F.[François], Parera, C.[Claudia], Hundt, C.[Christian], Frank, R.[Raphaël],
Weakly-Supervised Free Space Estimation through Stochastic Co-Teaching,
Hazards22(618-627)
IEEE DOI 2202
Training, Image segmentation, Adaptation models, Annotations, Stochastic processes, Estimation, Training data BibRef

Liu, P.[Pei], Yang, G.R.[Guo-Run], Li, P.X.[Pei-Xuan], Wang, Z.[Zhe], Shi, J.P.[Jian-Ping], Deng, Z.D.[Zhi-Dong], Qiao, Y.[Yu],
MP-Mono: Monocular 3D Detection Using Multiple Priors for Autonomous Driving,
3DV21(535-544)
IEEE DOI 2201
Geometry, Shape, Clustering algorithms, Object detection, Inference algorithms, Proposals BibRef

Karakostas, I.[Iason], Mygdalis, V.[Vasileios], Pitas, I.[Ioannis],
Adversarial Optimization Scheme for Online Tracking Model Adaptation In Autonomous Systems,
ICIP21(3358-3362)
IEEE DOI 2201
Training, Adaptation models, Visualization, Target tracking, Embedded systems, Computational modeling, Computer architecture, autonomous systems BibRef

Kishore, A.[Aman], Choe, T.E.[Tae Eun], Kwon, J.[Junghyun], Park, M.W.[Min-Woo], Hao, P.F.[Peng-Fei], Mittel, A.[Akshita],
Synthetic Data Generation using Imitation Training,
AVVision21(3071-3079)
IEEE DOI 2112
Training, Measurement, Deep learning, Machine learning algorithms, Estimation, Object detection BibRef

Besnier, V.[Victor], Picard, D.[David], Briot, A.[Alexandre],
Learning Uncertainty for Safety-Oriented Semantic Segmentation in Autonomous Driving,
ICIP21(3353-3357)
IEEE DOI 2201
Training, Uncertainty, Measurement uncertainty, Semantics, Estimation, Training data, Observers, Uncertainty, Segmentation, Autonomous Driving BibRef

Duong, L.H.[Le Hoang], Trung, H.T.[Huynh Thanh], Tam, P.M.[Pham Minh], Ko, G.[Gwangzeen], Moon, J.I.[Jung Ick], Jo, J.[Jun], Hung, N.Q.V.[Nguyen Quoc Viet],
ODAR: A Lightweight Object Detection Framework for Autonomous Driving Robots,
DICTA21(01-08)
IEEE DOI 2201
Deep learning, Wireless communication, Computational modeling, Object detection, Detectors, Feature extraction, Real-time systems, deep neural network BibRef

Sagar, A.[Abhinav], Soundrapandiyan, R.[RajKumar],
Semantic Segmentation With Multi Scale Spatial Attention For Self Driving Cars,
VSPW21(2650-2656)
IEEE DOI 2112
Measurement, Training, Image segmentation, Semantics, Neural networks BibRef

Agarwal, A.[Ashutosh], Majee, A.[Anay], Subramanian, A.[Anbumani], Arora, C.[Chetan],
Attention Guided Cosine Margin to Overcome Class-Imbalance in Few-Shot Road Object Detection,
Novelty22(221-230)
IEEE DOI 2202
Measurement, Head, Roads, Detectors, Object detection, Benchmark testing BibRef

Tambwekar, A.[Anuj], Agrawal, K.[Kshitij], Majee, A.[Anay], Subramanian, A.[Anbumani],
Few-Shot Batch Incremental Road Object Detection via Detector Fusion,
AVVision21(3063-3070)
IEEE DOI 2112
Deep learning, Roads, Object detection, Detectors, Robustness BibRef

Reuse, M.[Matthias], Simon, M.[Martin], Sick, B.[Bernhard],
About the Ambiguity of Data Augmentation for 3D Object Detection in Autonomous Driving,
ERCVAD21(979-987)
IEEE DOI 2112
Training, Manifolds, Detectors, Object detection BibRef

Plebe, A.[Alice], Kooij, J.F.P.[Julian F. P.], Papini, G.P.R.[Gastone Pietro Rosati], da Lio, M.[Mauro],
Occupancy Grid Mapping with Cognitive Plausibility for Autonomous Driving Applications,
AVVision21(2934-2941)
IEEE DOI 2112
Visualization, Navigation, Information filters, Cognition, Vehicle dynamics BibRef

Fugošic, K.[Kristijan], Šaric, J.[Josip], Šegvic, S.[Siniša],
Multimodal Semantic Forecasting Based on Conditional Generation of Future Features,
GCPR20(474-487).
Springer DOI 2110
BibRef

Rahman, Q.M.[Quazi Marufur], Sünderhauf, N.[Niko], Dayoub, F.[Feras],
Per-frame mAP Prediction for Continuous Performance Monitoring of Object Detection During Deployment,
WACVW21(152-160) Autonomous Vehicle Vision
IEEE DOI 2105
Measurement, Deep learning, Object detection, Detectors, Feature extraction BibRef

Wang, W.[Wei], Zhou, S.[Shibo], Li, J.X.[Jing-Xi], Li, X.H.[Xiao-Hua], Yuan, J.S.[Jun-Song], Jin, Z.P.[Zhan-Peng],
Temporal Pulses Driven Spiking Neural Network for Time and Power Efficient Object Recognition in Autonomous Driving,
ICPR21(6359-6366)
IEEE DOI 2105
Laser radar, Power demand, Computational modeling, Neural networks, Vision sensors, Real-time systems, Object recognition BibRef

Tian, K.[Kun], Zhou, T.[Tong], Xiang, S.M.[Shi-Ming], Pan, C.H.[Chun-Hong],
Forground-Guided Vehicle Perception Framework,
ICPR21(8015-8020)
IEEE DOI 2105
Training, Deep learning, Visualization, Vehicle detection, Pattern recognition, Task analysis, attention BibRef

Metzger, K.A.[Kai A.], Mortimer, P.[Peter], Wuensche, H.J.[Hans-Joachim],
A Fine-Grained Dataset and its Efficient Semantic Segmentation for Unstructured Driving Scenarios,
ICPR21(7892-7899)
IEEE DOI 2105
Semantics, Vegetation mapping, Lighting, Autonomous vehicles, Meteorology, vegetation dataset, efficient BibRef

Ohgushi, T.[Toshiaki], Horiguchi, K.[Kenji], Yamanaka, M.[Masao],
Road Obstacle Detection Method Based on an Autoencoder with Semantic Segmentation,
ACCV20(VI:223-238).
Springer DOI 2103
BibRef

Li, P.X.[Pei-Xuan], Zhao, H.[Huaici], Liu, P.F.[Peng-Fei], Cao, F.[Feidao],
RTM3D: Real-time Monocular 3d Detection from Object Keypoints for Autonomous Driving,
ECCV20(III:644-660).
Springer DOI 2012
BibRef

Wang, J.[Jun], Lan, S.Y.[Shi-Yi], Gao, M.F.[Ming-Fei], Davis, L.S.[Larry S.],
Infofocus: 3d Object Detection for Autonomous Driving with Dynamic Information Modeling,
ECCV20(X:405-420).
Springer DOI 2011
BibRef

Varghese, S., Bayzidi, Y., Bär, A., Kapoor, N., Lahiri, S., Schneider, J.D., Schmidt, N., Schlicht, P., Hüger, F., Fingscheidt, T.,
Unsupervised Temporal Consistency Metric for Video Segmentation in Highly-Automated Driving,
SAIAD20(1369-1378)
IEEE DOI 2008
Semantics, Measurement, Image segmentation, Optical imaging, Video sequences, Adaptive optics, Nonlinear optics BibRef

Walsh, S.[Sean], Ku, J.[Jason], Pon, A.D.[Alex D.], Waslander, S.L.[Steven L.],
Leveraging Temporal Data for Automatic Labelling of Static Vehicles,
CRV20(134-141)
IEEE DOI 2006
The vehicle maps to the same location each frame. labelling, object detection, dataset, 3D BibRef

Chen, N.F.Y.,
Pseudo-Labels for Supervised Learning on Dynamic Vision Sensor Data, Applied to Object Detection Under Ego-Motion,
ECVW18(757-75709)
IEEE DOI 1812
Vision sensors, Cameras, Vehicle dynamics, Object detection, Voltage control, Supervised learning, Automobiles BibRef

Khodabandeh, M.[Mehran], Vahdat, A.[Arash], Ranjbar, M.[Mani], Macready, W.[William],
A Robust Learning Approach to Domain Adaptive Object Detection,
ICCV19(480-490)
IEEE DOI 2004
learning (artificial intelligence), object detection, domain shift, unconstrained road environments, Computational modeling BibRef

Choi, H.M.[Hee Min], Kang, H.[Hyoa], Hyun, Y.[Yoonsuk],
Multi-View Reprojection Architecture for Orientation Estimation,
ADW19(2357-2366)
IEEE DOI 2004
geometry, image motion analysis, image reconstruction, object detection, pose estimation, regression analysis, MVRA BibRef

Ma, X., Wang, Z., Li, H., Zhang, P., Ouyang, W., Fan, X.,
Accurate Monocular 3D Object Detection via Color-Embedded 3D Reconstruction for Autonomous Driving,
ICCV19(6850-6859)
IEEE DOI 2004
driver information systems, feature extraction, image colour analysis, image fusion, image reconstruction, Transforms BibRef

Blum, H., Sarlin, P., Nieto, J., Siegwart, R., Cadena, C.,
Fishyscapes: A Benchmark for Safe Semantic Segmentation in Autonomous Driving,
ADW19(2403-2412)
IEEE DOI 2004
Bayes methods, image segmentation, learning (artificial intelligence), mobile robots, deep learning for robotics BibRef

Hua, M., Nan, Y., Lian, S.,
Small Obstacle Avoidance Based on RGB-D Semantic Segmentation,
CVRSUAD19(886-894)
IEEE DOI 2004
cameras, collision avoidance, image capture, image colour analysis, image segmentation, image sensors, image sequences, motion blur BibRef

Dahal, A., Hossen, J., Sumanth, C., Sistu, G., Malhan, K., Amasha, M., Yogamani, S.,
DeepTrailerAssist: Deep Learning Based Trailer Detection, Tracking and Articulation Angle Estimation on Automotive Rear-View Camera,
ADW19(2339-2346)
IEEE DOI 2004
cameras, driver information systems, embedded systems, learning (artificial intelligence), neural nets, Deep Learning BibRef

Orsic, M.[Marin], Kreso, I.[Ivan], Bevandic, P.[Petra], Segvic, S.[Sinisa],
In Defense of Pre-Trained ImageNet Architectures for Real-Time Semantic Segmentation of Road-Driving Images,
CVPR19(12599-12608).
IEEE DOI 2002
BibRef

Yang, J., Kang, Z.,
A Gradient-region Constrained Level Set Method for Autonomous Rock Detection From Mars Rover Image,
PRSM19(1479-1485).
DOI Link 1912
Rocks. On the surface. BibRef

Wang, Y., Peng, M., Di, K., Wan, W., Liu, Z., Yue, Z., Xing, Y., Mao, X., Teng, B.,
Vision Based Obstacle Detection Using Rover Stereo Images,
PRSM19(1471-1477).
DOI Link 1912
BibRef

Hashimoto, S., Mori, K.,
System Construction for Both Lunar Obstacle Detection and Annotation Support Based on Neurons' Decision Validity,
ICIP19(3447-3451)
IEEE DOI 1910
Deep learning, lunar crater, annotation BibRef

Liu, R., Luo, F., Yuan, Z.,
Beyond Bounding Box: Fine-Grained Vehicle Detection via Single Stage Detector with Hierarchical output,
ICIP19(3950-3954)
IEEE DOI 1910
vehicle detection, beyond bounding box, autonomous driving, deep learning BibRef

Kiran, B.R.[B. Ravi], Roldão, L.[Luis], Irastorza, B.[Beñat], Verastegui, R.[Renzo], Süss, S.[Sebastian], Yogamani, S.[Senthil], Talpaert, V.[Victor], Lepoutre, A.[Alexandre], Trehard, G.[Guillaume],
Real-Time Dynamic Object Detection for Autonomous Driving Using Prior 3D-Maps,
AutoNUE18(V:567-582).
Springer DOI 1905
BibRef

Hsu, Y., Zhong, K., Perng, J., Yin, T., Chen, C.,
Developing an On-Road Obstacle Detection System Using Monovision,
IVCNZ18(1-9)
IEEE DOI 1902
Feature extraction, Classification algorithms, Automobiles, Motorcycles, Support vector machines, Cameras, Object recognition, object recognition BibRef

Saleem, N.H., Griffin, A., Klette, R.,
Monocular Stixels: A LIDAR-guided Approach,
IVCNZ18(1-6)
IEEE DOI 1902
Laser radar, Cameras, Roads, Estimation, Sensors, Interpolation BibRef

Garcia, A.S., Figueroa, H.R., Hernandez, A.M., Ramirez, E.R., Uribe, D.O.,
Finding learned obstacles to avoid collisions in autonomous robotic navigation,
IVCNZ17(1-5)
IEEE DOI 1902
collision avoidance, image colour analysis, image segmentation, image sequences, learning (artificial intelligence), Apparent size BibRef

Li, P.L.[Pei-Liang], Qin, T.[Tong], Shen, S.J.[Shao-Jie],
Stereo Vision-Based Semantic 3D Object and Ego-Motion Tracking for Autonomous Driving,
ECCV18(II: 664-679).
Springer DOI 1810
BibRef

Patra, S., Maheshwari, P., Yadav, S., Banerjee, S., Arora, C.,
A Joint 3D-2D Based Method for Free Space Detection on Roads,
WACV18(643-652)
IEEE DOI 1806
SLAM (robots), cameras, feature extraction, image classification, image representation, image segmentation, image sequences, BibRef

Anisimov, D., Khanova, T.,
Towards lightweight convolutional neural networks for object detection,
AVSS17(1-8)
IEEE DOI 1806
feature extraction, inference mechanisms, object detection, road vehicles, self-organising feature maps, Real-time systems BibRef

Wolcott, R.[Ryan], Eustice, R.[Ryan],
Probabilistic Obstacle Partitioning of Monocular Video for Autonomous Vehicles,
BMVC16(xx-yy).
HTML Version. 1805
BibRef

Lin, C.T., Santoso, P.S., Chen, S.P., Lin, H.J., Lai, S.H.,
Fast Vehicle Detector for Autonomous Driving,
CVRoads17(222-229)
IEEE DOI 1802
Detectors, Feature extraction, Object detection, Proposals, Real-time systems, Training, Vehicle detection BibRef

Garnett, N., Silberstein, S., Oron, S., Fetaya, E., Verner, U., Ayash, A., Goldner, V., Cohen, R., Horn, K., Levi, D.,
Real-Time Category-Based and General Obstacle Detection for Autonomous Driving,
CVRoads17(198-205)
IEEE DOI 1802
Laser radar, Neurons, Object detection, Pose estimation, Training BibRef

Kostavelis, I.[Ioannis], Kargakos, A.[Andreas], Giakoumis, D.[Dimitrios], Tzovaras, D.[Dimitrios],
Robot's Workspace Enhancement with Dynamic Human Presence for Socially-Aware Navigation,
CVS17(279-288).
Springer DOI 1711
Other obstacles are people. BibRef

Lee, S.[Sinjae], Kee, S.C.[Seok-Cheol],
The New Detection Algorithm for an Obstacle's Information in Low Speed Vehicles,
CVS17(427-436).
Springer DOI 1711
BibRef

Franzius, M., Dunn, M., Einecke, N., Dirnberger, R.,
Embedded Robust Visual Obstacle Detection on Autonomous Lawn Mowers,
ECVW17(361-369)
IEEE DOI 1709
Cameras, Collision avoidance, Image color analysis, Image segmentation, Robots, Robustness, Sun BibRef

Gaisser, F., Jonker, P.P.,
Road user detection with convolutional neural networks: An application to the autonomous shuttle WEpod,
MVA17(101-104)
DOI Link 1708
Cameras, Radar detection, Roads, Sensors, Vehicle dynamics, Visualization BibRef

El Mikaty, M., Stathaki, T.,
Detection of cars in complex urban areas,
MVA17(105-108)
DOI Link 1708
Automobiles, Covariance matrices, Eigenvalues and eigenfunctions, Feature extraction, Histograms, Image, color, analysis BibRef

Carrillo, D.A.P., Sutherland, A.,
Fast Obstacle Detection Using Sparse Edge-Based Disparity Maps,
3DV16(66-72)
IEEE DOI 1701
image segmentation BibRef

Saleh, K., Hossny, M., Nahavandi, S.,
Kangaroo Vehicle Collision Detection Using Deep Semantic Segmentation Convolutional Neural Network,
DICTA16(1-7)
IEEE DOI 1701
Cameras BibRef

Sadhu, T., Albu, A.B., Hoeberechts, M., Wisernig, E., Wyvill, B.,
Obstacle Detection for Image-Guided Surface Water Navigation,
CRV16(45-52)
IEEE DOI 1612
linear regression BibRef

Kovács, L.,
Visual Monocular Obstacle Avoidance for Small Unmanned Vehicles,
ECVW16(877-884)
IEEE DOI 1612
BibRef

Levi, D.[Dan], Garnett, N.[Noa], Fetaya, E.[Ethan],
StixelNet: A Deep Convolutional Network for Obstacle Detection and Road Segmentation,
BMVC15(xx-yy).
DOI Link 1601
BibRef

Jafari, M.[Mohammad], Sengupta, S.[Shamik], La, H.M.[Hung Manh],
Adaptive Flocking Control of Multiple Unmanned Ground Vehicles by Using a UAV,
ISVC15(II: 628-637).
Springer DOI 1601
BibRef

Alhamwi, A.[Ali], Vandeportaele, B.[Bertrand], Piat, J.[Jonathan],
Real Time Vision System for Obstacle Detection and Localization on FPGA,
CVS15(80-90).
Springer DOI 1507
BibRef

Ruhle, J.[Johannes], Rodner, E.[Erik], Denzler, J.[Joachim],
Beyond thinking in common categories: Predicting obstacle vulnerability using large random codebooks,
MVA15(198-201)
IEEE DOI 1507
Cameras BibRef

Schauerte, B.[Boris], Koester, D.[Daniel], Martinez, M.[Manel], Stiefelhagen, R.[Rainer],
Way to Go! Detecting Open Areas Ahead of a Walking Person,
ACVR14(349-360).
Springer DOI 1504
BibRef

Mittal, A.[Ajay], Bensrhair, A.[Abdelaziz], Hancock, E.R.[Edwin R.],
Obstacle detection by means of stereo feature matching,
ICIP14(1618-1622)
IEEE DOI 1502
Cameras BibRef

Nguyen, T.[Thang], La, H.M.[Hung Manh],
Formation Control of Multiple Rectangular Agents with Limited Communication Ranges,
ISVC14(II: 915-924).
Springer DOI 1501
BibRef

Chen, H.T.[Hua-Tsung], Lai, C.Y.[Chun-Yu], Hsu, C.C.[Chun-Chieh], Lee, S.Y.[Suh-Yin], Lin, B.S.P.[Bao-Shuh Paul], Ho, C.P.[Chien-Peng],
Vision-Based Road Bump Detection Using a Front-Mounted Car Camcorder,
ICPR14(4537-4542)
IEEE DOI 1412
Cameras BibRef

Song, S.Y.[Shi-Yu], Chandraker, M.[Manmohan],
Robust Scale Estimation in Real-Time Monocular SFM for Autonomous Driving,
CVPR14(1566-1573)
IEEE DOI 1409
Autonomous driving; Object localization; Structure from motion BibRef

Shrivastava, P., Das, S.,
Fast area of contact computation for collision detection of a deformable object using FEM,
NCVPRIPG13(1-4)
IEEE DOI 1408
finite element analysis BibRef

Irki, Z.[Zohir], Oussar, A.[Abdelatif], Hamdi, M.[Mohamed], Seddi, F.[Fatah],
A Fuzzy UV-disparity based approach for obstacles avoidance,
WSSIP14(67-70) 1406
Algorithm design and analysis BibRef

Jayalath, A.N., Wang, Z.P.[Zheng-Ping],
Vision based inter-vehicle distance estimation with extended outlier correspondence,
IVCNZ13(323-327)
IEEE DOI 1402
computer vision BibRef

Holz, D., Nieuwenhuisen, M., Droeschel, D., Schreiber, M., Behnke, S.,
Towards Multimodal Omnidirectional Obstacle Detection for Autonomous Unmanned Aerial Vehicles,
UAV-g13(201-206).
DOI Link 1311
BibRef

Haghighi, R.[Reza], Cheah, C.C.[Chien Chern],
Distributed shape formation of multi-agent systems,
ICARCV12(1466-1471).
IEEE DOI 1304
robots in formation within a region. BibRef

Wang, H.[Han], Wei, Z.[Zhuo], Ow, C.S.[Chek Seng], Ho, K.T.[Kah Tong], Feng, B.[Benjamin], Huang, J.J.[Jun-Jie],
Improvement in real-time obstacle detection system for USV,
ICARCV12(1317-1322).
IEEE DOI 1304
BibRef

Dong, Y.[Yi], Huang, J.[Jie],
Leader-following rendezvous with connectivity preservation of single-integrator multi-agent systems,
ICARCV12(1686-1690).
IEEE DOI 1304
BibRef

Stein, P., Spalanzani, A., Laugier, C., Santos, V.,
Leader selection and following in dynamic environments,
ICARCV12(124-129).
IEEE DOI 1304
BibRef

Huang, J.Y.[Jing-Yi], Yao, J.[Jing], Sun, J.[Jian], Wang, J.[Jun],
A new coupled map car-following model under inter-vehicle communication,
ICARCV12(430-435).
IEEE DOI 1304
BibRef

Ronen, R., Arogeti, S.,
Coordinated path following control for a group of car-like vehicles,
ICARCV12(719-724).
IEEE DOI 1304
BibRef

Molineros, J.[Jose], Cheng, S.Y.[Shinko Y.], Owechko, Y.[Yuri], Levi, D.[Dan], Zhang, W.[Wende],
Monocular Rear-View Obstacle Detection Using Residual Flow,
CVVT12(II: 504-514).
Springer DOI 1210
BibRef

Kyutoku, H.[Haruya], Deguchi, D.[Daisuke], Takahashi, T.[Tomokazu], Mekada, Y.[Yoshito], Ide, I.[Ichiro], Murase, H.[Hiroshi],
Subtraction-Based Forward Obstacle Detection Using Illumination Insensitive Feature for Driving-Support,
CVVT12(II: 515-525).
Springer DOI 1210
BibRef

Iyidir, I.K.[Ibrahim K.], Tek, F.B.[F. Boray], Klrcali, D.[Dogan],
Adaptive Visual Obstacle Detection for Mobile Robots Using Monocular Camera and Ultrasonic Sensor,
CVVT12(II: 526-535).
Springer DOI 1210
BibRef

Broten, G.[Gregory], Mackay, D.[David], Collier, J.[Jack],
Probabilistic Obstacle Detection Using 2 1/2 D Terrain Maps,
CRV12(17-23).
IEEE DOI 1207
BibRef

Wijnhoven, R.G.J.[Rob G.J.], de With, P.H.N.[Peter H.N.],
Unsupervised sub-categorization for object detection: Finding cars from a driving vehicle,
CVVT11(2077-2083).
IEEE DOI 1201
BibRef

Kanitkar, A.[Aditya], Bharti, B.[Brijendra], Hivarkar, U.N.[Umesh N.],
Vision based preceding vehicle detection using self shadows and structural edge features,
ICIIP11(1-6).
IEEE DOI 1112
BibRef

Hou, A.L.[A-Lin], Cui, X.[Xue], Geng, Y.[Ying], Yuan, W.J.[Wen-Ju], Hou, J.[Jie],
Measurement of Safe Driving Distance Based on Stereo Vision,
ICIG11(902-907).
IEEE DOI 1109
BibRef

Ibrahim, A.W.N., Ching, P.W.[Pang Wee], Seet, G.L.G., Lau, W.S.M., Czajewski, W.,
Moving Objects Detection and Tracking Framework for UAV-based Surveillance,
PSIVT10(456-461).
IEEE DOI 1011
BibRef

Tsai, Y.M.[Yi-Min], Huang, K.Y.[Keng-Yen], Tsai, C.C.[Chih-Chung], Chen, L.G.[Liang-Gee],
An exploration of on-road vehicle detection using hierarchical scaling schemes,
ICIP10(3937-3940).
IEEE DOI 1009
BibRef

Teshima, T.[Tomoaki], Saito, H.[Hideo], Shimizu, M.[Masayoshi], Taguchi, A.[Akinori],
Classification of Wet/Dry Area Based on the Mahalanobis Distance of Feature from Time Space Image Analysis,
MVA09(467-).
PDF File. 0905
On a road. Water leads to reflections. BibRef

Tomita, M.[Masaaki], Yamamoto, M.[Motoji],
A Sensor Based Navigation Algorithm for Moving Obstacles Assuring Convergence Property,
MVA09(295-).
PDF File. 0905
BibRef

Kocamaz, M.K.[Mehmet Kemal], Rasmussen, C.[Christopher],
Automatic Refinement of Foreground Regions for Robot Trail Following,
ICPR10(4077-4080).
IEEE DOI 1008
BibRef

Arora, S.[Sankalp], Indu, S.,
A Novel Time Decaying Approach to Obstacle Avoidance,
PReMI09(543-548).
Springer DOI 0912
BibRef

Tang, R., Green, R.,
Obstacle avoidance on a mobile inverted pendulum robot,
IVCNZ09(254-259).
IEEE DOI 0911
BibRef

Fazli, S.[Saeid], Dehnavi, H.M.[Hajar Mohammadi], Moallem, P.[Payman],
A Robust Obstacle Detection Method in Highly Textured Environments Using Stereo Vision,
ICMV09(97-100).
IEEE DOI 0912
BibRef

Chavez, A.[Aaron], Gustafson, D.[David],
Vision-Based Obstacle Avoidance Using SIFT Features,
ISVC09(II: 550-557).
Springer DOI 0911
BibRef

Naroditsky, O.[Oleg], Zhu, Z.W.[Zhi-Wei], Das, A.[Aveek], Samarasekera, S.[Supun], Oskiper, T.[Taragay], Kumar, R.[Rakesh],
VideoTrek: A vision system for a tag-along robot,
CVPR09(1101-1108).
IEEE DOI 0906
BibRef

Klein, J.[John], Lecomte, C.[Christele], Miche, P.[Pierre],
Preceding car tracking using belief functions and a particle filter,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Rass, S.[Stefan], Fuchs, S.[Simone], Kyamakya, K.[Kyandoghere],
A Game-Theoretic Approach to Co-operative Context-Aware Driving with Partially Random Behavior,
SSC08(154-167).
Springer DOI 0810
BibRef

Ramisa, A.[Arnau], Vasudevan, S.[Shrihari], Scaramuzza, D.[Davide], de Mántaras, R.L.[Ramón López], Siegwart, R.[Roland],
A Tale of Two Object Recognition Methods for Mobile Robots,
CVS08(xx-yy).
Springer DOI 0805
BibRef

Foggia, P., Jolion, J.M.[Jean-Michel], Limongiello, A., Vento, M.,
Stereo Vision for Obstacle Detection: A Graph-Based Approach,
GbRPR07(37-48).
Springer DOI 0706
BibRef

Suvonvorn, N.[Nikom], Le Coat, F.[Francois],
Marrying Level-Line Junctions for Obstacle Detection,
ICIP07(IV: 305-308).
IEEE DOI 0709
BibRef

Suvonvorn, N.[Nikom], Bouchafa, S.[Samia], Zavidovique, B.[Bertrand],
Marrying Level Lines for Stereo or Motion,
ICIAR05(391-398).
Springer DOI 0509
BibRef

Cucchiara, R.[Rita], Perini, E.[Emanuele], Pistoni, G.[Giuliano],
Efficient Stereo Vision for Obstacle Detection and AGV Navigation,
CIAP07(291-296).
IEEE DOI 0709
BibRef

Zanin, M.[Michele],
Localization of ahead vehicles with on-board stereo cameras,
CIAP07(111-116).
IEEE DOI 0709
BibRef

Pacheco, L.[Lluís], Cufí, X.[Xavier], Cobos, J.[Javi],
Constrained Monocular Obstacle Perception with Just One Frame,
IbPRIA07(I: 611-619).
Springer DOI 0706
BibRef

Wen, X.Z.[Xue-Zhi], Zhao, H.[Hong], Wang, N.[Nan], Yuan, H.[Huai],
A Rear-Vehicle Detection System for Static Images Based on Monocular Vision,
ICARCV06(1-4).
IEEE DOI 0612
BibRef

Shmaglit, A., Rinat, K., Brand, Z., Fischler, A., Velger, M.,
Autonomous Vehicle Control and Obstacle Avoidance Concepts Oriented to Meet the Challenging Requirements of Realistic Missions,
ICARCV06(1-6).
IEEE DOI 0612
BibRef

Shih, M.Y.[Ming-Yu], Fu, B.C.[Bwo-Chau],
Robust Moving Object Detection on Moving Platforms,
PSIVT06(591-600).
Springer DOI 0612
BibRef

Wang, W.H.[Wen-Hao], Wu, R.C.[Ruei-Cheng],
Fusion of Luma and Chroma GMMs for HMM-Based Object Detection,
PSIVT06(573-581).
Springer DOI 0612
BibRef

Chen, C.H.[Chung-Hao], Cheng, C.[Chang], Page, D.L.[David L.], Koschan, A.F.[Andreas F.], Abidi, M.A.[Mongi A.],
A Moving Object Tracked by A Mobile Robot with Real-Time Obstacles Avoidance Capacity,
ICPR06(III: 1091-1094).
IEEE DOI 0609
BibRef

Broggi, A., Cerri, P., Ghidoni, S.,
A Correlation-Based Approach to Recognition and Localization of the Preceding Vehicle in Highway Environments,
CIAP05(1166-1173).
Springer DOI 0509
BibRef

Dang, T.[Thao], Hoffmann, C.[Christian],
Fast Object Hypotheses Generation Using 3D Position and 3D Motion,
MVIV05(III: 56-56).
IEEE DOI 0507
BibRef

Vincent, C.Y., Tjahjadi, T.,
Planar Direct Method: A New Framework for Stereo Vision Based Guidance and Obstacle Detection,
ICIP05(III: 381-384).
IEEE DOI 0512
BibRef

Shang, W.[Wen], Ma, X.D.[Xu-Dong], Dai, X.Z.[Xian-Zhong],
3D objects detection with Bayesian networks for vision-guided mobile robot navigation,
ICARCV04(II: 1134-1139).
IEEE DOI 0412
BibRef

Tang, L.[Li], Fang, L.J.[Li-Jin], Wang, H.G.[Hong-Guang],
Obstacle-navigation control for a mobile robot suspended on overhead ground wires,
ICARCV04(III: 2082-2087).
IEEE DOI 0412
BibRef

Sluzek, A., Seong, T.C.[Tan Ching],
Visual detection of 3D obstacles using gated images,
ICARCV04(I: 92-97).
IEEE DOI 0412
BibRef
And:
A feasibility study on a novel method of visual obstacle detection,
ICIP04(IV: 2447-2450).
IEEE DOI 0505
BibRef

Furukawa, K., Okada, R., Taniguchi, Y., Onoguchi, K.,
Onboard surveillance system for automobiles using image processing LSI,
IVS04(555-559).
IEEE DOI 0411
Three cameras to detect near by cars. BibRef

Janssen, H., Niehsen, W.,
Vehicle surround sensing based on information fusion of monocular video and digital map,
IVS04(244-249).
IEEE DOI 0411
BibRef

Techmer, A.,
Real time motion analysis for monitoring the rear and lateral road,
IVS04(704-709).
IEEE DOI 0411
BibRef

Matuszyk, L., Zelinsky, A., Nilsson, L., Rilbe, M.,
Stereo panoramic vision for monitoring vehicle blind-spots,
IVS04(31-36).
IEEE DOI 0411
BibRef

ten Kate, T.K., van Leewen, M.B., Moro-Ellenberger, S.E., Driessen, B.J.F., Versluis, A.H.G., Groen, F.C.A.,
Mid-range and distant vehicle detection with a mobile camera,
IVS04(72-77).
IEEE DOI 0411
BibRef

Tokoro, S., Moriizumi, K., Kawasaki, T., Nagao, T., Abe, K., Fujita, K.,
Sensor fusion system for pre-crash safety system,
IVS04(945-950).
IEEE DOI 0411
BibRef

Gietelink, O.J., Verburg, D.J., Labibes, K., Oostendorp, A.F.,
Pre-crash system validation with PRESCAN and VEHIL,
IVS04(913-918).
IEEE DOI 0411
BibRef

Rebut, J., Toulminet, G., Bensrhair, A.,
Road obstacles detection using a self-adaptive stereo vision sensor: a contribution to the ARCOS French project,
IVS04(738-743).
IEEE DOI 0411
BibRef

Demonceaux, C., Kachi-Akkouche, D.,
Fast motion estimation and motion segmentation using multi-scale approach,
ICIP04(I: 377-380).
IEEE DOI 0505
BibRef
And:
Robust obstacle detection with monocular vision based on motion analysis,
IVS04(527-532).
IEEE DOI 0411
BibRef

Sadou, M., Polotski, V., Cohen, P.,
Occlusions in obstacle detection for safe navigation,
IVS04(716-721).
IEEE DOI 0411
BibRef

Puhlmann, I., Schussler, S., Hulin, B.,
Improvements on obstacle detection in the pantograph gauge due to the recognition of steady arms,
IVS04(518-521).
IEEE DOI 0411
BibRef

Chang, P.[Peng], Hirvonen, D., Camus, T., Southall, B.,
Stereo-Based Object Detection, Classification, and Quantitative Evaluation with Automotive Applications,
MVIV05(III: 62-62).
IEEE DOI 0507
BibRef

Bansal, M., Jain, A., Camus, T., Das, A.,
Towards a Practical Stereo Vision Sensor,
MVIV05(III: 63-63).
IEEE DOI 0507
BibRef

Large, F., Vasquez, D., Fraichard, T., Laugier, C.,
Avoiding cars and pedestrians using velocity obstacles and motion prediction,
IVS04(375-379).
IEEE DOI 0411
BibRef

Guermeur, P.,
A new integrative approach to time varying image interpretation,
CRV04(120-128).
IEEE DOI 0408
BibRef

Garibotto, G.B., Corvi, M., Cibei, C., Sciarrino, S.,
3DMODS 3D moving obstacle detection system,
CIAP03(618-623).
IEEE DOI 0310
BibRef

Okada, R., Taniguchi, Y., Furukawa, K., Onoguchi, K.,
Obstacle detection using projective invariant and vanishing lines,
ICCV03(330-337).
IEEE DOI 0311
BibRef

Leuck, H.[Holger], Nagel, H.H.[Hans-Hellmut],
Automatic Differentiation Facilitates OF-Integration into Steering-Angle-Based Road Vehicle Tracking,
CVPR99(II: 360-365).
IEEE DOI Monitor traffic. BibRef 9900

Shioyama, T.[Tadayoshi], Wu, H.Y.[Hai-Yuan], Takebe, M.[Masaya], Shimaoka, N.[Naoya],
Segmentation and Free Space Detection Using Gabor Filters,
SCIA03(311-319).
Springer DOI 0310
Road without obstacle. BibRef

Shioyama, T.[Tadayoshi], Wu, H.Y.[Hai-Yuan], Iwai, A.[Atsushi],
Detection of Vehicles Using Gabor Filters and Affine Moment Invariants from an Image,
SCIA03(942-952).
Springer DOI 0310
BibRef

Shioyama, T.[Tadayoshi], Wu, H.Y.[Hai Yuan], Mitani, S.[Shigetomo],
Object Detection with Gabor Filters and Cumulative Histograms,
ICPR00(Vol I: 704-707).
IEEE DOI 0009
BibRef
Earlier:
Segmentation and object detection with Gabor filters and cumulative histograms,
CIAP99(412-417).
IEEE DOI 9909
BibRef

Shioyama, T., Wu, H.Y., Yamazoe, M.,
Object Recognition Based on 3-D Moment Invariants from Monocular Intensity Image,
SCIA99(Computer Vision). BibRef 9900

Shioyama, T.[Tadayoshi], Wu, H.Y.[Hai Yuan], Jiang, W.B.[Wen Biao], Terauchi, S.[Susumu],
3-D object positioning from monocular image brightnesses,
CIAP97(I: 628-635).
Springer DOI 9709
BibRef

Huber, R.[Reinhold], Biber, J.[Jürgen], Nowak, C.[Christoph], Spatzek, B.[Bernhard],
Recognition of Obstacles on Structured 3D Background,
CVS03(111 ff).
Springer DOI 0306
BibRef

Sakaino, H.,
Nonlinear robust velocity estimation of vehicles from a snowfall traffic scene,
ICPR02(IV: 60-63).
IEEE DOI 0211
BibRef
And:
Moving vehicle velocity estimation from obscure falling snow scenes based on brightness and contrast model,
ICIP02(III: 905-908).
IEEE DOI 0210
BibRef

Yoshioka, T.[Tohru], Uemura, H.[Hiroki],
Development of Detection Algorithm for Vehicles Using Multi-line CCD Sensor,
ICIP99(IV:21-24).
IEEE DOI BibRef 9900

Yuille, A.L., Coughlan, J.M.[James M.],
High-Level and Generic Models for Visual Search: When Does High Level Knowledge Help?,
CVPR99(II: 631-637).
IEEE DOI Detecting a road target in clutter. BibRef 9900

Wu, D.H.[Dong-Hui], Ye, X.Q.[Xiu-Qin], Gu, W.K.[Wei-Kang],
Tracking vehicles in image sequence for avoiding obstacles,
CIAP99(286-290).
IEEE DOI 9909
BibRef

Tsunashima, N., Nakajima, M.,
Detection of the Front Vehicle from the Stereoscopic Image Using Hierarchy Process,
MVA98(xx-yy). BibRef 9800

Lourakis, M.I.A., and Orphanoudakis, S.C.,
Visual Detection of Obstacles Assuming a Locally Planar Ground,
ACCV98(II: 527-534)
PS File. BibRef 9800

Thorpe, C.E.[Chuck E.],
Mixed Traffic and Automated Highways,
DARPA97(367-374). BibRef 9700

Ng, K.C.[Kim C.], Trivedi, M.M.,
Multirobot convoying using neuro-fuzzy control,
ICPR96(IV: 417-421).
IEEE DOI 9608
Ultrasonic scanner. (Univ. of California, San Diego, USA) BibRef

Gourley, C., and Trivedi, M.M.,
Fast Obstacle Avoidance Algorithm for Mobile Robots,
CRA94(1306-1311). Generation of 3-D maps of scene. BibRef 9400

Badal, S., Ravela, S.[Srinivas], Draper, B.A., and Hanson, A.R.,
A Practical Obstacle Detection and Avoidance System,
WACV94(97-104).
IEEE Abstract. BibRef 9400
And: UMassCS-TR-95-29, April 1995. BibRef

Yakovleff, A.J.S.,
Obstacle Avoidance and Visually-Induced Navigation,
CAMP95(xx). BibRef 9500

Sull, S., Sridhar, B.,
Model-based obstacle detection from image sequences,
ICIP95(II: 647-650).
IEEE DOI 9510
BibRef

Grandjean, P., Matthies, L.H.,
Perception Control for Obstacle Detection by a Cross Country Rover,
CRA93(20-27). 9701
BibRef

Storjohann, K., Zielke, T., Mallot, H.A., and von Seelen, W.,
Visual Obstacle Detection for Automatically Guided Vehicles,
CRA90(xx). BibRef 9000

Olin, K.E., Daily, M.J., Harris, J.G., Vilnrotter, F.M.,
Knowledge-Based Vision Technology Overview for Obstacle Detection and Avoidance,
DARPA89(134-143). BibRef 8900

Olin, K.E., Vilnrotter, F.M., Daily, M.J., and Reiser, K.,
Developments in Knowledge-Based Vision for Obstacle Detection and Avoidance,
DARPA87(78-86). BibRef 8700

Daily, M.J., Harris, J.G., and Reiser, K.,
Detecting Obstacles in Range Imagery,
DARPA87(87-97). BibRef 8700

Chen, Q.[Qian], Asada, M., Tsuji, S.,
A new 2-D world representation system for mobile robots,
ICPR88(I: 604-606).
IEEE DOI 8811
BibRef

Tsuji, S., Yagi, Y., Asada, M.,
Finding of Objects Moving in a Pathway by a Moving Observer,
ICPR86(1103-1106). BibRef 8600

Chapter on Active Vision, Camera Calibration, Mobile Robots, Navigation, Road Following continues in
Path Planning for Obstacle Avoidance .


Last update:Nov 26, 2024 at 16:40:19