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.
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.,
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Elsevier DOI BibRef 9002

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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.,
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MVA(7), No. 3, 1994, pp. 165-177. BibRef 9400

Xie, M., Trassoudaine, L., Alizon, J., Thonnat, M., Gallice, J.,
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Bhanu, B., Das, S., Roberts, B., Duncan, D.,
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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
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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.,
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Nishitani, K.[Katsuo],
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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
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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.,
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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.,
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PRL(16), 1995, pp. 925-932. BibRef 9500

Schneiderman, H., Nashman, M.,
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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],
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US_Patent5,706,355, Jan 6, 1998
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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,
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Takeda, S.[Shu], Tojima, M.[Masanori], Takeda, K.[Koji],
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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
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Earlier:
The Detection of Obstacles Using Features by the Horizon View Camera,
VI02(133).
PDF File. 0208
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Hahn, S.[Stefan], Stein, F.J.[Fridtjof J.],
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US_Patent6,067,111, 05/23/2000.
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Stiller, C., Hipp, J., Rössig, C., Ewald, A.,
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Franke, U., Heinrich, S.,
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Franke, U.[Uwe],
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Elsevier DOI 0304
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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
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Gandhi, T.[Tarak], Trivedi, M.M.[Mohan M.],
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Motion based vehicle surround analysis using an omni-directional camera,
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AVSBS06(78-78).
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Earlier:
Dynamic Panoramic Surround Map: Motivation and Omni Video Based Approach,
MVIV05(III: 61-61).
IEEE DOI 0507
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Earlier:
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Huang, D., Leung, H.,
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Belkhouche, F., Belkhouche, B., Rastgoufard, P.,
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Homography; Fundamental matrix; Segmentation; Reciprocal-polar rectification; Image rectification
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Caraffi, C., Cattani, S.[Stefano], Grisleri, P.,
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Takeda, N.[Nobuyuki], Hattori, H.[Hiroshi], Onoguchi, K.[Kazunori],
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Wedel, A.[Andreas], Franke, U.[Uwe], Klappstein, J.[Jens], Brox, T.[Thomas], Cremers, D.[Daniel],
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Wedel, A.[Andreas], Schoenemann, T.[Thomas], Brox, T.[Thomas], Cremers, D.[Daniel],
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Adaptive optics, Biomedical optical imaging, Cameras, Collision avoidance, Dynamics, Optical imaging, Optical sensors, Collision avoidance, image processing, intelligent vehicles, optical feedback, optimization BibRef

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Alarm systems, Cameras, Distortion, Feature extraction, Head, Lenses, Optical distortion, Backover warning system, rearview camera BibRef

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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
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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.
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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.
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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,
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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.
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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.
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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],
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IET-ITS(14), No. 7, July 2020, pp. 753-763.
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Gao, M.[Ming], Jin, L.S.[Li-Sheng], Jiang, Y.Y.[Yu-Ying], Bie, J.[Jing],
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Li, J.[Jing], Shi, X.X.[Xin-Xin], Wang, J.Z.[Jun-Zheng], Yan, M.[Min],
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IET-IPR(14), No. 10, August 2020, pp. 2216-2226.
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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
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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
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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
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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
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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
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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
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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


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
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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

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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],
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Hazards22(618-627)
IEEE DOI 2202
Training, Image segmentation, Adaptation models, Annotations, Stochastic processes, Estimation, Training data BibRef

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MP-Mono: Monocular 3D Detection Using Multiple Priors for Autonomous Driving,
3DV21(535-544)
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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)
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Training, Adaptation models, Visualization, Target tracking, Embedded systems, Computational modeling, Computer architecture, autonomous systems BibRef

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AVVision21(3071-3079)
IEEE DOI 2112
Training, Measurement, Deep learning, Machine learning algorithms, Estimation, Object detection BibRef

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Learning Uncertainty for Safety-Oriented Semantic Segmentation in Autonomous Driving,
ICIP21(3353-3357)
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Training, Uncertainty, Measurement uncertainty, Semantics, Estimation, Training data, Observers, Uncertainty, Segmentation, Autonomous Driving BibRef

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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, Market research 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)
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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)
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Training, Manifolds, Detectors, Object detection BibRef

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Occupancy Grid Mapping with Cognitive Plausibility for Autonomous Driving Applications,
AVVision21(2934-2941)
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Visualization, Navigation, Information filters, Cognition, Vehicle dynamics BibRef

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WACVW21(152-160) Autonomous Vehicle Vision
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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],
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ICPR21(6359-6366)
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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)
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Semantics, Vegetation mapping, Lighting, Pattern recognition, Autonomous vehicles, Meteorology, vegetation dataset, efficient BibRef

Ohgushi, T.[Toshiaki], Horiguchi, K.[Kenji], Yamanaka, M.[Masao],
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SAIAD20(1369-1378)
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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.],
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Chen, N.F.Y.,
Pseudo-Labels for Supervised Learning on Dynamic Vision Sensor Data, Applied to Object Detection Under Ego-Motion,
ECVW18(757-75709)
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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)
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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)
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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)
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cameras, driver information systems, embedded systems, learning (artificial intelligence), neural nets, Deep Learning BibRef

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Deep learning, lunar crater, annotation BibRef

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Beyond Bounding Box: Fine-Grained Vehicle Detection via Single Stage Detector with Hierarchical output,
ICIP19(3950-3954)
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vehicle detection, beyond bounding box, autonomous driving, deep learning BibRef

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IVCNZ18(1-9)
IEEE DOI 1902
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Monocular Stixels: A LIDAR-guided Approach,
IVCNZ18(1-6)
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Laser radar, Cameras, Roads, Estimation, Sensors, Interpolation BibRef

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Finding learned obstacles to avoid collisions in autonomous robotic navigation,
IVCNZ17(1-5)
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collision avoidance, image colour analysis, image segmentation, image sequences, learning (artificial intelligence), Apparent size BibRef

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SLAM (robots), cameras, feature extraction, image classification, image representation, image segmentation, image sequences, BibRef

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Towards lightweight convolutional neural networks for object detection,
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feature extraction, inference mechanisms, object detection, road vehicles, self-organising feature maps, Real-time systems BibRef

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Detectors, Feature extraction, Object detection, Proposals, Real-time systems, Training, Vehicle detection BibRef

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Laser radar, Neurons, Object detection, Pose estimation, Training BibRef

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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:Mar 16, 2024 at 20:36:19