Hollowbush, R.R.[Richard R.],
Ultrasonic obstacle detector,
US_Patent5,059,946, Oct 22, 1991
WWW Link. Vehicles
BibRef
9110
Dombrowski, A.E.[Anthony E.],
School bus obstacle detection device,
US_Patent5,250,945, Oct 5, 1993
WWW Link.
BibRef
9310
Shisgal, B.Z.[Ben Z.],
Anderman, H.[Hanan],
Fashchik, Y.[Yosef],
Porat, Y.[Yariv],
Obstacle detection system for vehicles moving in reverse,
US_Patent5,574,426, Nov 12, 1996
WWW Link.
BibRef
9611
Yuan, Z.P.[Zhi-Ping],
Vehicular safety distance alarm system,
US_Patent5,627,510, May 6, 1997
WWW Link.
BibRef
9705
Kato, T.,
Ninomiya, Y.,
Masaki, I.,
An obstacle detection method by fusion of radar and motion stereo,
ITS(3), No. 3, September 2002, pp. 182-188.
IEEE Abstract.
0402
BibRef
Shimomura, N.,
Fujimoto, K.,
Oki, T.,
Muro, H.,
An algorithm for distinguishing the types of objects on the road using
laser radar and vision,
ITS(3), No. 3, September 2002, pp. 189-195.
IEEE Abstract.
0402
BibRef
Tsang, S.H.,
Hall, P.S.,
Hoare, E.G.,
Clarke, N.J.,
Advance Path Measurement for Automotive Radar Applications,
ITS(7), No. 3, September 2006, pp. 273-281.
IEEE DOI
0609
BibRef
Jimenez Ruiz, A.R.,
Seco Granja, F.,
A Short-Range Ship Navigation System Based on Ladar Imaging and Target
Tracking for Improved Safety and Efficiency,
ITS(10), No. 1, March 2009, pp. 186-197.
IEEE DOI
0903
BibRef
Zhao, H.,
Chiba, M.,
Shibasaki, R.,
Shao, X.,
Cui, J.,
Zha, H.,
A Laser-Scanner-Based Approach Toward Driving Safety and Traffic Data
Collection,
ITS(10), No. 3, September 2009, pp. 534-546.
IEEE DOI
0909
Laser scanner data. Use both for obstacles and collecting general data.
BibRef
Viikari, V.V.,
Varpula, T.,
Kantanen, M.,
Road-Condition Recognition Using 24-GHz Automotive Radar,
ITS(10), No. 4, December 2009, pp. 639-648.
IEEE DOI
0912
BibRef
Pietzsch, S.,
Vu, T.D.,
Burlet, J.,
Aycard, O.,
Hackbarth, T.,
Appenrodt, N.,
Dickmann, J.,
Radig, B.,
Results of a Precrash Application Based on Laser Scanner and
Short-Range Radars,
ITS(10), No. 4, December 2009, pp. 584-593.
IEEE DOI
0912
BibRef
Yamamoto, H.[Hiroshi],
Ishii, Y.[Yoshinori],
Yamazaki, K.[Katsuyuki],
Development and Evaluation of Roadside/Obstacle Detection Method Using
3D Scanned Data Processing,
IEICE(E95-D), No. 2, February 2012, pp. 540-541.
WWW Link.
1202
BibRef
Kim, T.[Taewung],
Jeong, H.Y.[Hyun-Yong],
A Novel Algorithm for Crash Detection Under General Road Scenes Using
Crash Probabilities and an Interactive Multiple Model Particle Filter,
ITS(15), No. 6, December 2014, pp. 2480-2490.
IEEE DOI
1412
Monte Carlo methods
BibRef
Lehtomaki, M.,
Jaakkola, A.,
Hyyppa, J.,
Lampinen, J.,
Kaartinen, H.,
Kukko, A.,
Puttonen, E.,
Hyyppa, H.,
Object Classification and Recognition From Mobile Laser Scanning
Point Clouds in a Road Environment,
GeoRS(54), No. 2, February 2016, pp. 1226-1239.
IEEE DOI
1601
Accuracy
BibRef
Kellner, D.,
Barjenbruch, M.,
Klappstein, J.,
Dickmann, J.,
Dietmayer, K.,
Tracking of Extended Objects with High-Resolution Doppler Radar,
ITS(17), No. 5, May 2016, pp. 1341-1353.
IEEE DOI
1605
Doppler radar
BibRef
Luo, H.[Huan],
Wang, C.[Cheng],
Wen, C.L.[Cheng-Lu],
Chen, Z.Y.[Zi-Yi],
Zai, D.,
Yu, Y.T.[Yong-Tao],
Li, J.[Jonathan],
Semantic Labeling of Mobile LiDAR Point Clouds via Active Learning
and Higher Order MRF,
GeoRS(56), No. 7, July 2018, pp. 3631-3644.
IEEE DOI
1807
Markov processes, image representation, image segmentation,
learning (artificial intelligence), optical radar,
semantic labeling
See also Capsule-Based Networks for Road Marking Extraction and Classification from Mobile LiDAR Point Clouds.
BibRef
Rastiveis, H.[Heidar],
Shams, A.[Alireza],
Sarasua, W.A.[Wayne A.],
Li, J.[Jonathan],
Automated Extraction of Lane Markings from Mobile LiDAR Point Clouds
Based on Fuzzy Inference,
PandRS(160), 2020, pp. 149-166.
Elsevier DOI
2001
Mobile LiDAR, Road lane markings, Point cloud, Fuzzy inference system
BibRef
Nijsure, Y.A.,
Kaddoum, G.,
Khaddaj Mallat, N.,
Gagnon, G.,
Gagnon, F.,
Cognitive Chaotic UWB-MIMO Detect-Avoid Radar for Autonomous UAV
Navigation,
ITS(17), No. 11, November 2016, pp. 3121-3131.
IEEE DOI
1609
Bayes methods
BibRef
Jung, H.[Ha_Rim],
Kim, U.M.[Ung-Mo],
The SSP-Tree: A Method for Distributed Processing of Range Monitoring
Queries in Road Networks,
IJGI(6), No. 11, 2017, pp. xx-yy.
DOI Link
1712
BibRef
Deng, Z.,
Zhou, L.,
Detection and Recognition of Traffic Planar Objects Using Colorized
Laser Scan and Perspective Distortion Rectification,
ITS(19), No. 5, May 2018, pp. 1485-1495.
IEEE DOI
1805
Cameras, Distortion, Feature extraction, Image color analysis,
Laser noise, Shape, Autonomous vehicle,
planar object detection and recognition
BibRef
Ma, L.F.[Ling-Fei],
Li, Y.[Ying],
Li, J.[Jonathan],
Wang, C.[Cheng],
Wang, R.[Ruisheng],
Chapman, M.A.[Michael A.],
Mobile Laser Scanned Point-Clouds for Road Object Detection and
Extraction: A Review,
RS(10), No. 10, 2018, pp. xx-yy.
DOI Link
1811
BibRef
Hossain, M.A.[Md Anowar],
Elshafiey, I.[Ibrahim],
Al-Sanie, A.[Abdulhameed],
Waveform diversity for mutual interference mitigation in automotive
radars under realistic traffic environments,
SIViP(13), No. 1, February 2019, pp. 1-8.
Springer DOI
1901
BibRef
Lisowski, J.[Józef],
Mohamed-Seghir, M.[Mostefa],
Comparison of Computational Intelligence Methods Based on Fuzzy Sets
and Game Theory in the Synthesis of Safe Ship Control Based on
Information from a Radar ARPA System,
RS(11), No. 1, 2019, pp. xx-yy.
DOI Link
1901
BibRef
Stateczny, A.[Andrzej],
Kazimierski, W.[Witold],
Gronska-Sledz, D.[Daria],
Motyl, W.[Weronika],
The Empirical Application of Automotive 3D Radar Sensor for Target
Detection for an Autonomous Surface Vehicle's Navigation,
RS(11), No. 10, 2019, pp. xx-yy.
DOI Link
1906
BibRef
Lee, S.,
Lee, B.,
Lee, J.,
Kim, S.,
Statistical Characteristic-Based Road Structure Recognition in
Automotive FMCW Radar Systems,
ITS(20), No. 7, July 2019, pp. 2418-2429.
IEEE DOI
1907
Roads, Automotive engineering, Radar detection, Vehicles,
Support vector machines, Radar measurements,
support vector machine (SVM)
BibRef
Parmar, Y.[Yashrajsinh],
Natarajan, S.[Sudha],
Sobha, G.[Gayathri],
DeepRange: deep-learning-based object detection and ranging in
autonomous driving,
IET-ITS(13), No. 8, August 2019, pp. 1256-1264.
DOI Link
1908
BibRef
Alaqeel, A.A.,
Ibrahim, A.A.,
Nashashibi, A.Y.,
Shaman, H.N.,
Sarabandi, K.,
Experimental Characterization of Multi-Polarization Radar Backscatter
Response of Vehicles at J-Band,
ITS(20), No. 9, September 2019, pp. 3337-3350.
IEEE DOI
1909
Radar antennas, Radar imaging, Radar scattering,
Radar cross-sections, Backscatter, Automotive radar,
vehicular sensors
BibRef
Chen, S.,
Niu, S.,
Lan, T.,
Liu, B.,
PCT: Large-Scale 3d Point Cloud Representations Via Graph Inception
Networks with Applications to Autonomous Driving,
ICIP19(4395-4399)
IEEE DOI
1910
3D point cloud representations, graph deep neural networks, autonomous driving
BibRef
Lee, J.,
Jo, J.,
Park, T.,
Segmentation of Vehicles and Roads by a Low-Channel Lidar,
ITS(20), No. 11, November 2019, pp. 4251-4256.
IEEE DOI
1911
Laser radar, Convolution,
Image segmentation, Roads, Autonomous vehicles, receptive field
BibRef
Jung, D.H.[Dae-Hwan],
Kang, H.S.[Hyun-Seong],
Kim, C.K.[Chul-Ki],
Park, J.[Junhyeong],
Park, S.O.[Seong-Ook],
Sparse Scene Recovery for High-Resolution Automobile FMCW SAR via
Scaled Compressed Sensing,
GeoRS(57), No. 12, December 2019, pp. 10136-10146.
IEEE DOI
1912
Automobile frequency-modulated continuous-wave synthetic aperture radar.
Synthetic aperture radar, Automobiles, Azimuth,
Image reconstruction, Bandwidth, Radar polarimetry, sparse reconstruction
BibRef
Mei, J.L.[Ji-Lin],
Gao, B.[Biao],
Xu, D.H.[Dong-Hao],
Yao, W.[Wen],
Zhao, X.[Xijun],
Zhao, H.J.[Hui-Jing],
Semantic Segmentation of 3D LiDAR Data in Dynamic Scene Using
Semi-Supervised Learning,
ITS(21), No. 6, June 2020, pp. 2496-2509.
IEEE DOI
2006
Image segmentation, Semantics,
Laser radar, Semisupervised learning, Feature extraction,
semi-supervised learning
BibRef
Fazekas, A.,
Oeser, M.,
Performance Metrics and Validation Methods for Vehicle Position
Estimators,
ITS(21), No. 7, July 2020, pp. 2853-2863.
IEEE DOI
2007
Measurement, Radar tracking, Solid modeling, Microscopy, Sensors,
Estimation, Data acquisition, Vehicle tracking,
vision based traffic analysis
BibRef
Hong, D.S.[Dza-Shiang],
Chen, H.H.[Hung-Hao],
Hsiao, P.Y.[Pei-Yung],
Fu, L.C.[Li-Chen],
Siao, S.M.[Siang-Min],
CrossFusion net: Deep 3D object detection based on RGB images and
point clouds in autonomous driving,
IVC(100), 2020, pp. 103955.
Elsevier DOI
2008
Deep learning, 3D object detection, Data fusion, Autonomous driving
BibRef
Geng, K.[Keke],
Dong, G.[Ge],
Yin, G.D.[Guo-Dong],
Hu, J.Y.[Jing-Yu],
Deep Dual-Modal Traffic Objects Instance Segmentation Method Using
Camera and LIDAR Data for Autonomous Driving,
RS(12), No. 20, 2020, pp. xx-yy.
DOI Link
2010
BibRef
Lee, S.,
Lee, J.Y.,
Kim, S.C.,
Mutual Interference Suppression Using Wavelet Denoising in Automotive
FMCW Radar Systems,
ITS(22), No. 2, February 2021, pp. 887-897.
IEEE DOI
2102
Radar, Automotive engineering, Noise reduction, Wavelet transforms,
Sensors, Interference suppression,
wavelet denoising
BibRef
Guo, W.,
Chen, J.,
Wang, W.,
Luo, H.,
Wang, S.,
Three-Dimensional Object Co-Localization From Mobile LiDAR Point
Clouds,
ITS(22), No. 4, April 2021, pp. 1996-2007.
IEEE DOI
2104
Object detection, Laser radar, graph matching,
Task analysis, Semantics, Search problems, Location awareness.
BibRef
Czaplewski, K.[Krzysztof],
Swierczynski, S.[Slawomir],
A Method of Increasing the Accuracy of Radar Distance Measurement in
VTS Systems for Vessels with Very Large Dimensions,
RS(13), No. 16, 2021, pp. xx-yy.
DOI Link
2109
Vessel Traffic System.
Not really roads, but the same idea.
BibRef
Zhang, L.[Liwen],
Zheng, J.Y.[Jian-Ying],
Sun, R.C.[Rong-Chuan],
Tao, Y.Y.[Yan-Yun],
GC-Net: Gridding and Clustering for Traffic Object Detection With
Roadside LiDAR,
IEEE_Int_Sys(36), No. 4, July 2021, pp. 104-113.
IEEE DOI
2109
Laser radar, Feature extraction,
Intelligent systems, Data structures, Surveillance, Roads, detection,
intelligent transportation system
BibRef
Li, J.[Jiong],
Zhang, Y.[Yu],
Liu, X.[Xixia],
Zhang, X.D.[Xu-Dong],
Bai, R.[Rui],
Obstacle detection and tracking algorithm based on multi-lidar fusion
in urban environment,
IET-ITS(15), No. 11, 2021, pp. 1372-1387.
DOI Link
2110
Autonomous Vehicle, Lidar, Obstacle detection and tracking, Sensor fusion
BibRef
Guo, R.[Rui],
Li, D.[Deng],
Han, Y.[Yahong],
Deep multi-scale and multi-modal fusion for 3D object detection,
PRL(151), 2021, pp. 236-242.
Elsevier DOI
2110
3D Object detection, Feature fusion, Autonomous driving, Point cloud
BibRef
Wang, G.M.[Guang-Ming],
Wu, X.[Xinrui],
Liu, Z.[Zhe],
Wang, H.[Hesheng],
PWCLO-Net: Deep LiDAR Odometry in 3D Point Clouds Using Hierarchical
Embedding Mask Optimization,
CVPR21(15905-15914)
IEEE DOI
2111
Learning systems, Solid modeling, Laser radar,
Costs, Filtering, Pose estimation
BibRef
Zhang, A.[Ao],
Nowruzi, F.E.[Farzan Erlik],
Laganiere, R.[Robert],
RADDet: Range-Azimuth-Doppler based Radar Object Detection for
Dynamic Road Users,
CRV21(95-102)
IEEE DOI
2108
Deep learning, Tensors, Roads,
Radar detection, Radar, Object detection, Radar, Range, Azimuth, Doppler,
Deep Learning
BibRef
Ouaknine, A.[Arthur],
Newson, A.[Alasdair],
Rebut, J.[Julien],
Tupin, F.[Florence],
Pérez, P.[Patrick],
CARRADA Dataset: Camera and Automotive Radar with Range- Angle-
Doppler Annotations,
ICPR21(5068-5075)
IEEE DOI
2105
Visualization, Laser radar, Annotations, Radar,
Sensor phenomena and characterization, Cameras, Time measurement
BibRef
Wang, Y.[Yue],
Fathi, A.[Alireza],
Kundu, A.[Abhijit],
Ross, D.A.[David A.],
Pantofaru, C.[Caroline],
Funkhouser, T.[Tom],
Solomon, J.[Justin],
Pillar-based Object Detection for Autonomous Driving,
ECCV20(XXII:18-34).
Springer DOI
2011
BibRef
McCrae, S.,
Zakhor, A.,
3d Object Detection For Autonomous Driving Using Temporal Lidar Data,
ICIP20(2661-2665)
IEEE DOI
2011
Laser radar, Object detection, Autonomous vehicles, autonomous driving
BibRef
Ma, S.Z.[Si-Zhuo],
Gupta, M.[Mohit],
Inertial Safety from Structured Light,
ECCV20(XXIII:744-761).
Springer DOI
2011
fast detection of obstacles in scenarios involving camera or scene motion.
Robot navigation, or robot-human environments.
BibRef
Schilling, H.,
Gutsche, M.,
Brock, A.,
Späth, D.,
Rother, C.,
Krispin, K.,
Mind the Gap - A Benchmark for Dense Depth Prediction Beyond Lidar,
SAIAD20(1379-1388)
IEEE DOI
2008
Benchmark testing, Laser radar, Autonomous vehicles, Measurement,
Sensors, Vehicle dynamics
BibRef
Dong, X.,
Wang, P.,
Zhang, P.,
Liu, L.,
Probabilistic Oriented Object Detection in Automotive Radar,
PBVS20(458-467)
IEEE DOI
2008
Radar imaging, Laser radar, Object detection, Radar detection,
Doppler radar, Autonomous vehicles
BibRef
Weng, X.,
Kitani, K.,
Monocular 3D Object Detection with Pseudo-LiDAR Point Cloud,
CVRSUAD19(857-866)
IEEE DOI
2004
Code, Object Detection.
WWW Link. image representation, image sensors, object detection,
optical radar, KITTI benchmark, 3D localization,
Point Cloud
BibRef
Sless, L.,
Shlomo, B.E.,
Cohen, G.,
Oron, S.,
Road Scene Understanding by Occupancy Grid Learning from Sparse Radar
Clusters using Semantic Segmentation,
CVRSUAD19(867-875)
IEEE DOI
2004
image segmentation,
learning (artificial intelligence), optical radar,
radar free space
BibRef
Wang, Z.,
Ding, S.,
Li, Y.,
Zhao, M.,
Roychowdhury, S.,
Wallin, A.,
Sapiro, G.,
Qiu, Q.,
Range Adaptation for 3D Object Detection in LiDAR,
ADW19(2320-2328)
IEEE DOI
2004
object detection, optical radar, radar detection,
point cloud annotation, autonomous driving
BibRef
Meyer, G.P.[Gregory P.],
Laddha, A.[Ankit],
Kee, E.[Eric],
Vallespi-Gonzalez, C.[Carlos],
Wellington, C.K.[Carl K.],
LaserNet: An Efficient Probabilistic 3D Object Detector for Autonomous
Driving,
CVPR19(12669-12678).
IEEE DOI
2002
BibRef
Li, P.L.[Pei-Liang],
Chen, X.Z.[Xiao-Zhi],
Shen, S.J.[Shao-Jie],
Stereo R-CNN Based 3D Object Detection for Autonomous Driving,
CVPR19(7636-7644).
IEEE DOI
2002
BibRef
Li, B.[Buyu],
Ouyang, W.L.[Wan-Li],
Sheng, L.[Lu],
Zeng, X.Y.[Xing-Yu],
Wang, X.G.[Xiao-Gang],
GS3D: An Efficient 3D Object Detection Framework for Autonomous Driving,
CVPR19(1019-1028).
IEEE DOI
2002
BibRef
Wang, Y.[Yan],
Chao, W.L.[Wei-Lun],
Garg, D.[Divyansh],
Hariharan, B.[Bharath],
Campbell, M.[Mark],
Weinberger, K.Q.[Kilian Q.],
Pseudo-LiDAR From Visual Depth Estimation: Bridging the Gap in 3D
Object Detection for Autonomous Driving,
CVPR19(8437-8445).
IEEE DOI
2002
BibRef
Wu, Y.,
Pang, Y.,
Gao, B.,
Han, J.,
Complementary Features with Reasonable Receptive Field for Road Scene
3D Object Detection,
ICIP19(3905-3909)
IEEE DOI
1910
3D object detection, lidar, deep learning, complementary features
BibRef
Yoon, D.[David],
Tang, T.[Tim],
Barfoot, T.[Timothy],
Mapless Online Detection of Dynamic Objects in 3D Lidar,
CRV19(113-120)
IEEE DOI
1908
Online detection of dynamic objects in LiDAR.
Laser radar, Dynamics, Pipelines, Sensors, Vehicle dynamics,
Measurement, Lidar, Dynamic Object Detection
BibRef
Simon, M.[Martin],
Milz, S.[Stefan],
Amende, K.[Karl],
Gross, H.M.[Horst-Michael],
Complex-YOLO: An Euler-Region-Proposal for Real-Time 3D Object
Detection on Point Clouds,
CVRoads18(I:197-209).
Springer DOI
1905
Lidar, automated driving.
BibRef
Vaquero, V.,
Sanfeliu, A.,
Moreno-Noguer, F.,
Hallucinating Dense Optical Flow from Sparse Lidar for Autonomous
Vehicles,
ICPR18(1959-1964)
IEEE DOI
1812
Laser radar, Optical imaging, Optical sensors, Image resolution,
Optical variables measurement, Training
BibRef
Razali, H.,
Ouarti, N.,
Lidarbox: A fast and accurate method for object proposals VIA lidar
point clouds for autonomous vehicles,
ICIP17(1352-1356)
IEEE DOI
1803
Automobiles, Autonomous vehicles, Laser radar, Proposals, Sensors,
Point-Clouds
BibRef
Chen, X.,
Ma, H.,
Wan, J.,
Li, B.,
Xia, T.,
Multi-view 3D Object Detection Network for Autonomous Driving,
CVPR17(6526-6534)
IEEE DOI
1711
Laser radar, Object detection, Proposals,
BibRef
Askeland, S.A.,
Ekman, T.,
Tracking With a High-Resolution 2D Spectral Estimation Based
Automotive Radar,
ITS(16), No. 5, October 2015, pp. 2418-2423.
IEEE DOI
1511
estimation theory
BibRef
Peasley, B.[Brian],
Birchfield, S.T.[Stanley T.],
Real-time obstacle detection and avoidance in the presence of
specular surfaces using an active 3D sensor,
WORV13(197-202)
IEEE DOI
1307
BibRef
Haberjahn, M.[Mathias],
Reulke, R.[Ralf],
Object Discrimination and Tracking in the Surroundings of a Vehicle by
a Combined Laser Scanner Stereo System,
CVVT10(225-234).
Springer DOI
1109
BibRef
Catala-Prat, A.,
Köster, F.,
Reulke, R.,
Image And Laser Scanner Processing As Confident Cues For Object
Detection In Driving Situations,
CloseRange10(xx-yy).
PDF File.
1006
BibRef
Izumi, K.,
Watanabe, K.,
Shindo, M.,
Sato, R.,
Acquisition of Obstacle Avoidance Behaviors for a Quadruped Robot Using
Visual and Ultrasonic Sensors,
ICARCV06(1-6).
IEEE DOI
0612
BibRef
Zhang, F.,
Justh, E.W.,
Krishnaprasad, P.S.,
Boundary following using gyroscopic control,
DC04(V: 5204-5209).
IEEE DOI For guidance of space objects.
BibRef
0400
Kalyan, B.,
Balasuriya, A.,
Ura, T.,
Wijesoma, S.,
Sonar and vision based navigation schemes for autonomous underwater
vehicles,
ICARCV04(I: 437-442).
IEEE DOI
0412
BibRef
Sole, A.,
Mano, O.,
Stein, G.P.,
Kumon, H.,
Tamatsu, Y.,
Shashua, A.,
Solid or not solid: vision for radar target validation,
IVS04(819-824).
IEEE DOI
0411
Radar in dense urban traffic.
Vision to validate radar.
BibRef
Sugimoto, S.,
Tateda, H.,
Takahashi, H.,
Okutomi, M.,
Obstacle detection using millimeter-wave radar and its visualization on
image sequence,
ICPR04(III: 342-345).
IEEE DOI
0409
BibRef
Song, K.T.[Kai-Tai],
Chen, C.H.[Chih-Hao],
Huang, C.H.C.[Cheng-Hsien Chiu],
Design and experimental study of an ultrasonic sensor system for
lateral collision avoidance at low speeds,
IVS04(647-652).
IEEE DOI
0411
BibRef
Hancock, J.,
Laser Intensity-Based Obstacle Detection and Tracking,
CMU-RI-TR-99-01, January, 1999.
HTML Version.
BibRef
9901
Chapter on Active Vision, Camera Calibration, Mobile Robots, Navigation, Road Following continues in
Airplane Obstacles, Collision Detection, Sense and Avoid .