15.3.26.6 Obstacles, Objects on the Road Using Radar, Sonar, LiDAR, Active Vision

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
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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

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

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

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

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

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

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

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.Z.[Wen-Zhong], Chen, J.W.[Jia-Wei], Wang, W.P.[Wei-Peng], Luo, H.[Huan], Wang, S.P.[Shi-Ping],
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

Zhang, L.W.[Li-Wen], 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.X.[Xi-Xia], Zhang, X.D.[Xu-Dong], Bai, R.[Rui],
Obstacle detection and tracking algorithm based on multi-LiDARs 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.H.[Ya-Hong],
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

Sun, X.B.[Xue-Bin], Wang, S.[Sukai], Liu, M.[Ming],
A Novel Coding Architecture for Multi-Line LiDAR Point Clouds Based on Clustering and Convolutional LSTM Network,
ITS(23), No. 3, March 2022, pp. 2190-2201.
IEEE DOI 2203
Image coding, Laser radar, Redundancy, Sensors, Prediction algorithms, Heuristic algorithms, LiDAR, convolutional LSTM BibRef

Yuan, Z.X.[Zhen-Xun], Song, X.[Xiao], Bai, L.[Lei], Wang, Z.[Zhe], Ouyang, W.L.[Wan-Li],
Temporal-Channel Transformer for 3D Lidar-Based Video Object Detection for Autonomous Driving,
CirSysVideo(32), No. 4, April 2022, pp. 2068-2078.
IEEE DOI 2204
Object detection, Feature extraction, Laser radar, Correlation, Decoding, Head, Lidar-based video, 3D object detection, transformer, temporal-channel attention BibRef

Mothershed, D.M.[David Michael], Lugner, R.[Robert], Afraj, S.[Shahabaz], Sequeira, G.J.[Gerald Joy], Schneider, K.[Kilian], Brandmeier, T.[Thomas], Soloiu, V.[Valentin],
Comparison and Evaluation of Algorithms for LiDAR-Based Contour Estimation in Integrated Vehicle Safety,
ITS(23), No. 5, May 2022, pp. 3925-3942.
IEEE DOI 2205
Safety, Estimation, Laser radar, Accidents, Shape, Cameras, Sensors, Contour estimation, curve similarity, integrated safety, light detection and ranging (LiDAR) BibRef

Zhang, J.X.[Jia-Xing], Xiao, W.[Wen], Mills, J.P.[Jon P.],
Optimizing Moving Object Trajectories from Roadside Lidar Data by Joint Detection and Tracking,
RS(14), No. 9, 2022, pp. xx-yy.
DOI Link 2205
BibRef

Li, Y.J.[Yu-Jie], Yang, S.[Shuo], Zheng, Y.C.[Yu-Chao], Lu, H.M.[Hui-Min],
Improved Point-Voxel Region Convolutional Neural Network: 3D Object Detectors for Autonomous Driving,
ITS(23), No. 7, July 2022, pp. 9311-9317.
IEEE DOI 2207
Feature extraction, Proposals, Training, Detectors, Object detection, Convolution, 3D object detection, region proposal method, point cloud data processing BibRef

Solomitckii, D.[Dmitrii], Heino, M.[Mikko], Buddappagari, S.[Sreehari], Hein, M.A.[Matthias A.], Valkama, M.[Mikko],
Radar Scheme With Raised Reflector for NLOS Vehicle Detection,
ITS(23), No. 7, July 2022, pp. 9037-9045.
IEEE DOI 2207
Solid modeling, Radar, Automobiles, Radar cross-sections, Backscatter, Radar detection, Radar cross section, non-line-of-sight BibRef

Zou, X.F.[Xiao-Feng], Li, K.[Kenli], Li, Y.F.[Yang-Fan], Wei, W.[Wei], Chen, C.[Cen],
Multi-Task Y-Shaped Graph Neural Network for Point Cloud Learning in Autonomous Driving,
ITS(23), No. 7, July 2022, pp. 9568-9579.
IEEE DOI 2207
Point cloud compression, Task analysis, Feature extraction, Multitasking, Graph neural networks, Semantics, Y-shaped architecture BibRef

Zhu, B.[Bing], Sun, Y.H.[Yu-Hang], Zhao, J.[Jian], Zhang, S.[Sumin], Zhang, P.X.[Pei-Xing], Song, D.J.[Dong-Jian],
Millimeter-Wave Radar in-the-Loop Testing for Intelligent Vehicles,
ITS(23), No. 8, August 2022, pp. 11126-11136.
IEEE DOI 2208
Testing, Radar, Intelligent vehicles, Millimeter wave radar, Radar cross-sections, Mathematical model, Meteorology, radar in-the-loop test BibRef

Iqbal, H.[Hafsa], Campo, D.[Damian], Marin-Plaza, P.[Pablo], Marcenaro, L.[Lucio], Gómez, D.M.[David Martín], Regazzoni, C.[Carlo],
Modeling Perception in Autonomous Vehicles via 3D Convolutional Representations on LiDAR,
ITS(23), No. 9, September 2022, pp. 14608-14619.
IEEE DOI 2209
Laser radar, Feature extraction, Point cloud compression, Autonomous vehicles, Cameras, Solid modeling, hierarchical generalize dynamic Bayesian network BibRef

Fu, H.[Hao], Xue, H.Z.[Han-Zhang], Xie, G.L.[Guang-Lei],
MapCleaner: Efficiently Removing Moving Objects from Point Cloud Maps in Autonomous Driving Scenarios,
RS(14), No. 18, 2022, pp. xx-yy.
DOI Link 2209
BibRef

Duy, L.H.[Loc Hoang], Kim, G.W.[Gon-Woo],
AEC3D: An Efficient and Compact Single Stage 3D Multiobject Detector for Autonomous Driving,
ITS(23), No. 12, December 2022, pp. 23422-23432.
IEEE DOI 2212
Point cloud compression, Feature extraction, Detectors, Object detection, Laser radar, Real-time systems, convolutional neural network BibRef

Muñoz-Bañón, M.Á.[Miguel Ángel], Velasco-Sánchez, E.[Edison], Candelas, F.A.[Francisco A.], Torres, F.[Fernando],
OpenStreetMap-Based Autonomous Navigation With LiDAR Naive-Valley-Path Obstacle Avoidance,
ITS(23), No. 12, December 2022, pp. 24428-24438.
IEEE DOI 2212
Roads, Location awareness, Autonomous robots, Laser radar, Costs, Navigation, Collision avoidance, Autonomous navigation, LiDAR point cloud BibRef

Abbasi, R.[Rashid], Bashir, A.K.[Ali Kashif], Alyamani, H.J.[Hasan J.], Amin, F.[Farhan], Doh, J.[Jaehyeok], Chen, J.W.[Jian-Wen],
Lidar Point Cloud Compression, Processing and Learning for Autonomous Driving,
ITS(24), No. 1, January 2023, pp. 962-979.
IEEE DOI 2301
Image coding, Laser radar, Real-time systems, Safety, Point cloud compression, Vehicular ad hoc networks, deep learning BibRef

He, Q.[Qingdong], Wang, Z.[Zhengning], Zeng, H.[Hao], Zeng, Y.[Yi], Liu, Y.J.[Yi-Jun], Liu, S.C.[Shuai-Cheng], Zeng, B.[Bing],
Stereo RGB and Deeper LIDAR-Based Network for 3D Object Detection in Autonomous Driving,
ITS(24), No. 1, January 2023, pp. 152-162.
IEEE DOI 2301
Point cloud compression, Feature extraction, Proposals, Object detection, Laser radar, Semantics, 3D object detection, deeper LIDAR features BibRef

Ci, W.[Wenyan], Xu, T.[Tie], Lin, R.[Runze], Lu, S.[Shan], Wu, X.[Xialai], Xuan, J.Y.[Jia-Yin],
A Novel Method for Obstacle Detection in Front of Vehicles Based on the Local Spatial Features of Point Cloud,
RS(15), No. 4, 2023, pp. xx-yy.
DOI Link 2303
BibRef

Bauer, P.[Péter], Hiba, A.[Antal], Nagy, M.[Mihály], Simonyi, E.[Erno], Kuna, G.I.[Gergely István], Kisari, Á.[Ádám], Drotár, I.[István], Zarándy, Á.[Ákos],
Encounter Risk Evaluation with a Forerunner UAV,
RS(15), No. 6, 2023, pp. 1512.
DOI Link 2304
Downward-looking camera flying in front of the emergency ground vehicles to look for danger. BibRef

Gao, A.[Aqi], Cao, J.[Jiale], Pang, Y.W.[Yan-Wei], Li, X.L.[Xue-Long],
Real-Time Stereo 3D Car Detection With Shape-Aware Non-Uniform Sampling,
ITS(24), No. 4, April 2023, pp. 4027-4037.
IEEE DOI 2304
Feature extraction, Automobiles, Proposals, Object detection, Point cloud compression, Detectors BibRef

Chandrasegar, V.[Vasantha], Koh, J.W.[Jinh-Wan],
Estimation of Azimuth Angle Using an Ultrasonic Sensor for Automobile,
RS(15), No. 7, 2023, pp. 1837.
DOI Link 2304
BibRef

Zhuang, G.H.[Geng-Hang], Bing, Z.S.[Zhen-Shan], Yao, X.T.[Xiang-Tong], Huang, Y.H.[Yu-Hong], Huang, K.[Kai], Knoll, A.[Alois],
Toward Intelligent Sensing: Optimizing Lidar Beam Distribution for Autonomous Driving,
ITS(24), No. 8, August 2023, pp. 8386-8392.
IEEE DOI 2308
Laser radar, Autonomous vehicles, Optimization, Sensors, Task analysis, Object detection, LiDAR sensor, LiDAR optimization, autonomous driving BibRef

Kim, T.L.[Taek-Lim], Arshad, S.[Saba], Park, T.H.[Tae-Hyoung],
Adaptive Feature Attention Module for Robust Visual-LiDAR Fusion-Based Object Detection in Adverse Weather Conditions,
RS(15), No. 16, 2023, pp. 3992.
DOI Link 2309
BibRef

Han, C.Y.[Chong-Yang], Wu, W.B.[Wei-Bin], Luo, X.[Xiwen], Li, J.[Jiehao],
Visual Navigation and Obstacle Avoidance Control for Agricultural Robots via LiDAR and Camera,
RS(15), No. 22, 2023, pp. 5402.
DOI Link 2311
BibRef

Gong, B.[Bowen], Sun, J.[Jinghang], Lin, C.[Ciyun], Liu, H.C.[Hong-Chao], Sun, G.[Ganghao],
Louvain-Based Traffic Object Detection for Roadside 4D Millimeter-Wave Radar,
RS(16), No. 2, 2024, pp. 366.
DOI Link 2402
BibRef

Thanh, P.T.H.[Phan Thi Huyen], Bui, M.Q.V.[Minh Quan Viet], Nguyen, D.D.[Duc Dung], Pham, T.V.[Tran Vu], Duy, T.V.T.[Truong Vinh Truong], Naotake, N.[Natori],
Transfer multi-source knowledge via scale-aware online domain adaptation in depth estimation for autonomous driving,
IVC(141), 2024, pp. 104871.
Elsevier DOI 2402
Monocular depth estimation, Multi-source domain adaptation, Meta-learning, Online domain adaptation, Virtual-to-real, Autonomous driving BibRef

Wang, L.Y.[Lu-Yang], Lan, J.H.[Jin-Hui], Li, M.[Min],
AFRNet: Anchor-Free Object Detection Using Roadside LiDAR in Urban Scenes,
RS(16), No. 5, 2024, pp. 782.
DOI Link 2403
BibRef

Liu, J.Q.[Jian-Qi], Zhao, J.G.[Jian-Guo], Guo, J.F.[Jun-Feng], Zou, C.F.[Cai-Feng], Yin, X.W.[Xiu-Wen], Cheng, X.C.[Xiao-Chun], Khan, F.[Fazlullah],
Automatic Background Filtering for Cooperative Perception Using Roadside LiDAR,
ITS(25), No. 7, July 2024, pp. 6964-6977.
IEEE DOI 2407
Filtering, Laser radar, Sensors, Laser beams, Real-time systems, Task analysis, Automatic background filtering, frame selection, space division BibRef

Zhang, T.Y.T.[Tian-Ya Terry], Ge, Y.[Yi], Chen, A.[Anjiang], Sartipi, M.[Mina], Jin, P.J.[Peter J.],
Hash-Based Gaussian Mixture Model (HGMM) for Roadside LiDAR Smart Infrastructure Applications,
ITS(25), No. 10, October 2024, pp. 12968-12979.
IEEE DOI 2410
Laser radar, Laser beams, Point cloud compression, Object detection, Data models, Solid modeling, Hashing algorithm, smart infrastructure application BibRef

Du, R.H.[Rong-Hua], Feng, R.[Rongying], Gao, K.[Kai], Zhang, J.[Jinlai], Liu, L.H.[Lin-Hong],
Self-Supervised Point Cloud Prediction for Autonomous Driving,
ITS(25), No. 11, November 2024, pp. 17452-17467.
IEEE DOI 2411
Point cloud compression, Trajectory, Predictive models, Prediction algorithms, Solid modeling, Estimation, trajectory BibRef

Huang, Z.W.[Zi-Wei], Bai, L.[Lu], Sun, M.[Mingran], Cheng, X.[Xiang],
A LiDAR-Aided Channel Model for Vehicular Intelligent Sensing-Communication Integration,
ITS(25), No. 12, December 2024, pp. 20105-20119.
IEEE DOI 2412
LiDAR locates scatters that affect communication. Sensors, Vehicle dynamics, Laser radar, Channel models, Point cloud compression, Radio frequency, LiDAR-aided channel model BibRef

Salguero-Luna, S.A.[Sergio Alberto], Ramírez-Gutiérrez, K.A.[Kelsey Alejandra], Martínez-Cruz, A.[Alfonso],
A State-of-the-Art Review on Attacks and Defense Mechanisms for LiDAR on Autonomous Vehicles,
ITS(26), No. 1, January 2025, pp. 22-42.
IEEE DOI 2501
Laser radar, Sensors, Autonomous vehicles, Surveys, Cameras, Photodetectors, Global Positioning System, Reviews, Sensor systems, security BibRef

Loc, H.D.[Hoang Duy], Son, L.A.[Le Anh], Nang, H.X.[Ho Xuan],
Accurate 3D Multi-Object Detection and Tracking on Vietnamese Street Scenes Based on Sparse Point Cloud Data,
ITS(26), No. 1, January 2025, pp. 92-101.
IEEE DOI 2501
Point cloud compression, Laser radar, Accuracy, Feature extraction, Benchmark testing, Detectors, Sensors, Proposals, Meteorology, convolutional neural network BibRef

Yue, Y.[Ye], Qi, H.G.[Hong-Gang], Deng, Y.Q.[Yong-Qiang], Li, J.J.[Juan-Juan], Liang, H.[Hao], Miao, J.[Jun],
Infrastructure-Side Point Cloud Object Detection via Multi-Frame Aggregation and Multi-Scale Fusion,
ITS(26), No. 1, January 2025, pp. 703-713.
IEEE DOI 2501
Point cloud compression, Feature extraction, Object detection, Laser radar, Detectors, Autonomous vehicles, Accuracy, infrastructure-side point cloud BibRef

Khan, A.A.[Abdullah Aman], Shao, J.[Jie], Rao, Y.[Yunbo], She, L.[Lei], Shen, H.T.[Heng Tao],
LRDNet: Lightweight LiDAR Aided Cascaded Feature Pools for Free Road Space Detection,
MultMed(27), 2025, pp. 652-664.
IEEE DOI 2502
Roads, Laser radar, Visualization, Feature extraction, Point cloud compression, Space vehicles, Road detection, KITTI road BibRef

Chakravarthy, A.S.[Anirudh S.], Ganesina, M.R.[Meghana Reddy], Hu, P.Y.[Pei-Yun], Leal-Taixé, L.[Laura], Kong, S.[Shu], Ramanan, D.[Deva], Osep, A.[Aljosa],
Lidar Panoptic Segmentation in an Open World,
IJCV(133), No. 3, March 2025, pp. 1153-1174.
Springer DOI 2502
BibRef

Anand, V.[Vivek], Lohani, B.[Bharat], Pandey, G.[Gaurav], Mishra, R.[Rakesh],
Advancing LiDAR Intensity Simulation Through Learning With Novel Physics-Based Modalities,
ITS(26), No. 5, May 2025, pp. 6493-6502.
IEEE DOI 2505
Laser radar, Reflectivity, Sensors, Surface emitting lasers, Semantics, Point cloud compression, Surface treatment, Libraries, autonomous vehicles BibRef

Hao, Y.[Yiduo], Madani, S.[Sohrab], Guan, J.F.[Jun-Feng], Alloulah, M.[Mohammed], Gupta, S.[Saurabh], Hassanieh, H.[Haitham],
Bootstrapping Autonomous Driving Radars with Self-Supervised Learning,
CVPR24(15012-15023)
IEEE DOI Code:
WWW Link. 2410
Training, Heating systems, Meteorological radar, Radar detection, Radar, Object detection, Radar imaging BibRef

Yataka, R.[Ryoma], Wang, P.[Pu], Boufounos, P.[Petros], Takahashi, R.[Ryuhei],
SIRA: Scalable Inter-Frame Relation and Association for Radar Perception,
CVPR24(15024-15034)
IEEE DOI 2410
Scalability, Radar detection, Radar, Object detection, Radar tracking, Transformers, Reflection, autonomous driving, ADAS, deep neural network BibRef

Ma, J.[Junyi], Chen, X.Y.L.[Xie-Yuan-Li], Huang, J.W.[Jia-Wei], Xu, J.Y.[Jing-Yi], Luo, Z.[Zhen], Xu, J.T.[Jin-Tao], Gu, W.H.[Wei-Hao], Ai, R.[Rui], Wang, H.S.[He-Sheng],
Cam4DOcc: Benchmark for Camera-Only 4D Occupancy Forecasting in Autonomous Driving Applications,
CVPR24(21486-21495)
IEEE DOI Code:
WWW Link. 2410
Protocols, Estimation, Training data, Benchmark testing, Predictive models, Spatiotemporal phenomena, occupancy forecasting BibRef

Ouasfi, A.[Amine], Boukhayma, A.[Adnane],
Unsupervised Occupancy Learning from Sparse Point Cloud,
CVPR24(21729-21739)
IEEE DOI 2410
Point cloud compression, Training, Uncertainty, Shape, Measurement uncertainty, Entropy BibRef

Ma, Q.H.[Qi-Hang], Tan, X.[Xin], Qu, Y.[Yanyun], Ma, L.Z.[Li-Zhuang], Zhang, Z.Z.[Zhi-Zhong], Xie, Y.[Yuan],
COTR: Compact Occupancy TRansformer for Vision-Based 3D Occupancy Prediction,
CVPR24(19936-19945)
IEEE DOI Code:
WWW Link. 2410
Geometry, Semantics, Performance gain, Transformers, Decoding BibRef

Agro, B.[Ben], Sykora, Q.[Quinlan], Casas, S.[Sergio], Gilles, T.[Thomas], Urtasun, R.[Raquel],
UnO: Unsupervised Occupancy Fields for Perception and Forecasting,
CVPR24(14487-14496)
IEEE DOI 2410
Point cloud compression, Roads, Semantics, Predictive models, Data models, Trajectory, Spatiotemporal phenomena, Unsupervised BibRef

Yang, Z.[Zetong], Chen, L.[Li], Sun, Y.[Yanan], Li, H.Y.[Hong-Yang],
Visual Point Cloud Forecasting Enables Scalable Autonomous Driving,
CVPR24(14673-14684)
IEEE DOI 2410
Point cloud compression, Visualization, Scalability, Semantics, Predictive models, Rendering (computer graphics) BibRef

Kälble, J.[Jonas], Wirges, S.[Sascha], Tatarchenko, M.[Maxim], Ilg, E.[Eddy],
Accurate Training Data for Occupancy Map Prediction in Automated Driving Using Evidence Theory,
CVPR24(5281-5290)
IEEE DOI 2410
Training, Geometry, Point cloud compression, Laser radar, Accuracy, Evidence theory, Occupancy Prediction, Automated Driving BibRef

Yang, P.[Pengwan], Snoek, C.G.M.[Cees G. M.], Asano, Y.M.[Yuki M.],
Self-Ordering Point Clouds,
ICCV23(15767-15776)
IEEE DOI 2401
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Jiang, W.T.[Wen-Tao], Xiang, H.[Hao], Cai, X.Y.[Xin-Yu], Xu, R.S.[Run-Sheng], Ma, J.Q.[Jia-Qi], Li, Y.[Yikang], Lee, G.H.[Gim Hee], Liu, S.[Si],
Optimizing the Placement of Roadside LiDARs for Autonomous Driving,
ICCV23(18335-18344)
IEEE DOI 2401
BibRef

Rong, Y.[Yao], Wei, X.Y.[Xiang-Yu], Lin, T.W.[Tian-Wei], Wang, Y.Y.[Yue-Yu], Kasneci, E.[Enkelejda],
DynStatF: An Efficient Feature Fusion Strategy for LiDAR 3D Object Detection,
E2EAD23(3238-3247)
IEEE DOI 2309
BibRef

Nesti, T.[Tommaso], Boddana, S.[Santhosh], Yaman, B.[Burhaneddin],
Ultra-Sonic Sensor based Object Detection for Autonomous Vehicles,
WAD23(210-218)
IEEE DOI 2309
BibRef

Li, Y.J.[Yu-Jhe], Hunt, S.[Shawn], Park, J.[Jinhyung], O'Toole, M.[Matthew], Kitani, K.[Kris],
Azimuth Super-Resolution for FMCW Radar in Autonomous Driving,
CVPR23(17504-17513)
IEEE DOI 2309
BibRef

Lee, S.C.[Sheng-Cheng], Lu, V.[Victor], Wang, C.C.[Chieh-Chih], Lin, W.C.[Wen-Chieh],
LiDAR-Based Localization on Highways Using Raw Data and Pole-Like Object Features,
WAD23(230-237)
IEEE DOI 2309
BibRef

He, Y.H.[Yu-Hang], Chen, L.[Lin], Xie, J.[Junkun], Chen, L.[Long],
Learning 3d Semantics from Pose-Noisy 2D Images with Hierarchical Full Attention Network,
AVVision22(726-742).
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Meyer, M.[Michael], Unzueta, M.[Marc], Kuschk, G.[Georg], Tomforde, S.[Sven],
Ego-motion Compensation of Range-beam-doppler Radar Data for Object Detection,
AVVision22(697-708).
Springer DOI 2304
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Ibrahim, Y.[Yahya], Nagy, B.[Balázs], Benedek, C.[Csaba],
Multi-view Based 3D Point Cloud Completion Algorithm for Vehicles,
ICPR22(2121-2127)
IEEE DOI 2212
Point cloud compression, Visualization, Shape, Urban areas, Lasers, Sensors BibRef

Liang, Y.S.[Ying-Shuo], Zhu, X.Y.[Xing-Yu], Zhang, J.L.[Jian-Lei],
Maanu-Net: Multi-Level Attention and Atrous Pyramid Nested U-Net for Wrecked Objects Segmentation in Forward-Looking Sonar Images,
ICIP22(736-740)
IEEE DOI 2211
Remotely guided vehicles, Image resolution, Fuses, Sonar applications, Object segmentation, Interference, U-Net BibRef

Li, P.Z.[Pei-Zhao], Wang, P.[Pu], Berntorp, K.[Karl], Liu, H.F.[Hong-Fu],
Exploiting Temporal Relations on Radar Perception for Autonomous Driving,
CVPR22(17050-17059)
IEEE DOI 2210
Radar, Object detection, Radar imaging, Sensors, Object recognition, Object tracking, Navigation and autonomous driving BibRef

Zhang, Y.[Yanan], Chen, J.X.[Jia-Xin], Huang, D.[Di],
CAT-Det: Contrastively Augmented Transformer for Multimodal 3D Object Detection,
CVPR22(898-907)
IEEE DOI 2210
Laser radar, Navigation, Object detection, Benchmark testing, Transformers, Recognition: detection, categorization, retrieval, Navigation and autonomous driving BibRef

Sautier, C.[Corentin], Puy, G.[Gilles], Gidaris, S.[Spyros], Boulch, A.[Alexandre], Bursuc, A.[Andrei], Marlet, R.[Renaud],
Image-to-Lidar Self-Supervised Distillation for Autonomous Driving Data,
CVPR22(9881-9891)
IEEE DOI 2210
Point cloud compression, Image segmentation, Solid modeling, Laser radar, Semantics, Object detection, Self- semi- meta- 3D from multi-view and sensors BibRef

Hu, H.J.[Han-Jiang], Liu, Z.[Zuxin], Chitlangia, S.[Sharad], Agnihotri, A.[Akhil], Zhao, D.[Ding],
Investigating the Impact of Multi-LiDAR Placement on Object Detection for Autonomous Driving,
CVPR22(2540-2549)
IEEE DOI 2210
Measurement, Training, Sensor placement, Laser radar, Correlation, Object detection, Vision applications and systems, RGBD sensors and analytics BibRef

Nakamura, R.[Ryota], Enokida, S.[Shuichi],
Robust 3D Object Detection for Moving Objects Based on PointPillars,
Hazards22(611-617)
IEEE DOI 2202
Point cloud compression, Deep learning, Laser radar, Conferences, Lighting, Object detection BibRef

Sagar, A.[Abhinav],
DMSANet: Dual Multi Scale Attention Network,
CIAP22(I:633-645).
Springer DOI 2205
BibRef
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AA3DNet: Attention Augmented Real Time 3D Object Detection,
Hazards22(628-635)
IEEE DOI 2202
Point cloud compression, Training, Measurement, Neural networks, Object detection, Computer architecture BibRef

Mehtab, S.[Sabeeha], Yan, W.Q.[Wei Qi], Narayanan, A.[Ajit],
3D Vehicle Detection Using Cheap LiDAR and Camera Sensors,
IVCNZ21(1-6)
IEEE DOI 2201
Point cloud compression, Image sensors, Laser radar, Roads, Estimation, Sensor phenomena and characterization, LiDAR, fusion 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

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
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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

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

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
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Wang, Y.[Yan], Chao, W.L.[Wei-Lun], Garg, D.[Divyansh], Hariharan, B.[Bharath], Campbell, M.[Mark], Weinberger, K.Q.[Kilian Q.],
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CVPR19(8437-8445).
IEEE DOI 2002
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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

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

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

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

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
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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).
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Catala-Prat, A., Köster, F., Reulke, R.,
Image And Laser Scanner Processing As Confident Cues For Object Detection In Driving Situations,
CloseRange10(xx-yy).
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Izumi, K., Watanabe, K., Shindo, M., Sato, R.,
Acquisition of Obstacle Avoidance Behaviors for a Quadruped Robot Using Visual and Ultrasonic Sensors,
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Zhang, F., Justh, E.W., Krishnaprasad, P.S.,
Boundary following using gyroscopic control,
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Kalyan, B., Balasuriya, A., Ura, T., Wijesoma, S.,
Sonar and vision based navigation schemes for autonomous underwater vehicles,
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Sole, A., Mano, O., Stein, G.P., Kumon, H., Tamatsu, Y., Shashua, A.,
Solid or not solid: vision for radar target validation,
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Sugimoto, S., Tateda, H., Takahashi, H., Okutomi, M.,
Obstacle detection using millimeter-wave radar and its visualization on image sequence,
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IEEE DOI 0409
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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,
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Chapter on Active Vision, Camera Calibration, Mobile Robots, Navigation, Road Following continues in
Road Scene, General Analysis .