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0402
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Advance Path Measurement for Automotive Radar Applications,
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0609
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Jimenez Ruiz, A.R.,
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A Short-Range Ship Navigation System Based on Ladar Imaging and Target
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0903
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Zhao, H.,
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A Laser-Scanner-Based Approach Toward Driving Safety and Traffic Data
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Laser scanner data. Use both for obstacles and collecting general data.
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Yamamoto, H.[Hiroshi],
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Development and Evaluation of Roadside/Obstacle Detection Method Using
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Kim, T.[Taewung],
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A Novel Algorithm for Crash Detection Under General Road Scenes Using
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1412
Monte Carlo methods
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Lehtomaki, M.,
Jaakkola, A.,
Hyyppa, J.,
Lampinen, J.,
Kaartinen, H.,
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Object Classification and Recognition From Mobile Laser Scanning
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GeoRS(54), No. 2, February 2016, pp. 1226-1239.
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1601
Accuracy
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Kellner, D.,
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Klappstein, J.,
Dickmann, J.,
Dietmayer, K.,
Tracking of Extended Objects with High-Resolution Doppler Radar,
ITS(17), No. 5, May 2016, pp. 1341-1353.
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1605
Doppler radar
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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
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.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
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.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-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
Zhang, X.D.[Xu-Dong],
Wang, F.Z.[Fang-Zhou],
Li, H.B.[Hong-Bin],
An Efficient Method for Cooperative Multi-Target Localization in
Automotive Radar,
SPLetters(29), 2022, pp. 16-20.
IEEE DOI
2202
Sensors, Delays, Location awareness, Radar, Receiving antennas,
Automotive engineering, Radar signal processing, 2-D FFT
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.[Yangfan],
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
Li, Y.W.[Yao-Wen],
Li, G.[Gang],
Liu, Y.[Yu],
Zhang, X.P.[Xiao-Ping],
He, Y.[You],
A Hybrid SVSF Algorithm for Automotive Radar Tracking,
ITS(23), No. 9, September 2022, pp. 15028-15042.
IEEE DOI
2209
Uncertainty, Radar tracking, Measurement uncertainty,
Smoothing methods, Switches, Noise measurement, Gain measurement,
radar target tracking
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
Muńoz-Bańón, 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
Lisowski, J.[Józef],
Artificial Intelligence Methods in Safe Ship Control Based on Marine
Environment Remote Sensing,
RS(15), No. 1, 2023, pp. xx-yy.
DOI Link
2301
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
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, Pattern recognition,
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
And:
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
Wang, G.M.[Guang-Ming],
Wu, X.R.[Xin-Rui],
Liu, Z.[Zhe],
Wang, H.S.[He-Sheng],
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.
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Chapter on Active Vision, Camera Calibration, Mobile Robots, Navigation, Road Following continues in
Road Scene, General Analysis .