動態避障功能是自走車系統之重要核心，此功能需結合物件檢測，障礙物距離測量與車道變換等功能，透過強健車道檢測演算法使自走車在挑戰的環境，例如:強光、黑夜和大雨等仍可順利執行動態避障。本研究將以車載行車紀錄器結合卷積神經網路檢測路徑前方之車輛與障礙物。在完成車輛偵測後，將檢測結果結合強健車道辨識以進行動態避障時，發現強健車道檢測在應用上會受到淺色車輛干擾，故本研究提出使用卷積神經網路 (YOLOv3) 從一張有6部車的圖像中找出2部在近處，2部在較遠處車輛以及2部在最遠處車輛，順利完成偵測圖像內之全數車輛，並遮擋檢測到車輛降低干擾，以提高車道辨識精準度。同時利用車載鏡頭進行距離判定，使用車牌大小檢測以減少與前車距離測量的誤差。本研究以模型自走車實驗以驗證上述分析結果。在實驗室環境中之模型自走車以正常速度下，設定安全距離為3個車身長度，提醒車輛進行車道變換完成避障。此動態避障演算法結合強健車道辨識，使車輛在完成避障後依然可以行駛在車道內。結果顯示在完成車道變換同時避開障礙物後，車道保持系統的可以控制在車道寬度的18.47%以內。透過分析與實驗顯示自走車可以有效地變換車道，避開障礙物，並繼續安全行駛在車道內。

Dynamic obstacle avoidance is important to autonomous vehicle. This algorithm shall combine object (vehicle) detection and object distance measurement for lane change. The Convolutional Neural Network (CNN) is applied in this thesis to detect vehicles/obstacles ahead by an image sensor on an autonomous vehicle. After vehicle detection, the detection results are combined with robust lane detection for lane change in dynamic obstacle avoidance. For an image having 6 vehicles with 2 nearby, 2 vehicles in the mid-range distance, and 2 at the farther locations, the CNN (YOLOv3) can success fully identify all the vehicles. This work proposes to block the detected vehicles so as to reduce interference and to improve the accuracy of lane detection in obstacle avoidance. After vehicle detection, distance measurement to the license plate of the vehicle ahead is applied to reduce measurement error. Dynamic obstacle avoidance of an autonomous vehicle is validated in laboratory environment. For the safety distance set to 3 vehicle lengths, an autonomous vehicle can complete obstacle avoidance by lane change. The algorithm proposed in this work combines the robust lane detection to keep the vehicle in lane after completing obstacle avoidance. After lane change and obstacle avoidance, the lane keeping is within 18.47% of the lane width. Results of the simulation and experiment show that the autonomous vehicle can effectively avoid obstacles by lane change and keep safely in the lane by the dynamic obstacle avoidance algorithm.

[1]b The Future of the Last-Mile Ecosystem,b  World Economic Forum, 2020.
[2]b Autonomy Delivers: An Oncoming Revolution in the Movement of Goods,b  KPMG: New York, NY, USA, 2018.
[3]b Why Self-Driving Vehicles are Going to Deliver Pizzas before People,b  Fortune: Somerset, NJ, USA, March 14, 2018.
[4]b Global Status Report on Road Safety 2018,b  World Health Organization: Geneva, 2018.
[5]Das, S. K.; Ning, Z.; Kong, X.; Ullah, N.; Rahim, A.; Wanga, J.; Xia, F. b Vehicular Social Networks: A Survey,b  Pervasive and Mobile Computing, 14, pp. 96-113, 2018.
[6]b SUV Hits Freeway Maintenance Truck at Full Speed: Driver and Passenger Pulled out before Car Bursts into Flames,b  Taiwan English News, April 13, 2021.
[7]b Tesla in Taiwan Crashes Directly into Overturned Truck, Ignores Pedestrian, with Autopilot on,b  Forbes, July 2, 2020.
[8]Musk, E. b On Cameras vs LiDAR for Self Driving and Autonomous Cars,b  Complete Investor Conference Event, April 27, 2019.
[9]b 2020 Disengagement Reports from California,b  The Last License Holder, 2021.
[10]Liu, Y. H. b A Robust Lane Detection Algorithm Based on Binary Line Segment Filter for Lane Keeping in Autonomous Vehicles,b  Master Thesis in National Cheng Kung University, 2019.
[11]b Automated Driving-Levels of Driving Automation are Defined in New SAE International Standard J3016_202104,b  SAE International, 2021.
[12]Krafcik, J. b A New Phase for Waymo and the History of this Technology,b  Web Summit: Lisbon, 2017.
[13]b Trial Project for Uncrewed Vehicles with Innovative Technology,b  Ministry of Economic Affairs, Taiwan R.O.C., 2019.
[14]Antone, M.; Huang, A. S.; Moore, D.; Olson, E.; Teller, S. b Finding Multiple Lanes in Urban Road Networks with Vision and LiDAR,b  Autonomous Robots, 26, pp. 103-122, 2009.
[15]Li, S.; Liu, P.; Tang, J. b A Review of Lane Detection Methods Based on Deep Learning,b  Pattern Recognition, 111, pp.1-15, 2021.
[16]Dai, Y.; Lee, S.-G. b Perception, Planning and Control for Self-Driving System Based on On-Board Sensors,b  Advances in Mechanical Engineering, 12, pp.1-13, 2020.
[17]Fu, M.; Li, Y.; Song, W.; Wang, M.; Yang, Y. b Lane Detection and Classification for Forward Collision Warning System Based on Stereo Vision,b  IEEE Sensors Journal, 18, pp. 5151-5163, 2018.
[18]Bevly, D. M.; Clanton, J. M.; Hodel, A. S. b A Low-Cost Solution for an Integrated Multisensor Lane Departure Warning System,b  IEEE Transactions on Intelligent Transportation Systems, 10, pp. 47-59, 2009.
[19]Bonnifait, P.; Fremont, V.; Ibanez-Guzman, J.; Tao, Z. b Mapping and Localization Using GPS, Lane Markings and Proprioceptive Sensors,b  IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 406-412, 2013.
[20]Chen, S.; Lin, C.; Wang, J. G. b Applying Fuzzy Method to Vision-Based Lane Detection and Departure Warning System,b  Expert Systems with Applications, 37, pp. 113-126, 2010.
[21]Gaikwad, V.; Lokhande, S. b Lane Departure Identification for Advanced Driver Assistance,b  IEEE Transactions on Intelligent Transportation Systems, 16, pp. 910-918, 2015.
[22]Kim, Z. b Robust Lane Detection and Tracking in Challenging Scenarios,b  IEEE Transactions on Intelligent Transportation Systems, 9, pp. 16-26, 2008.
[23]Li, Q.; Liu, H.; Xuan, H.; Yuan, J. b Robust Lane-Mark Extraction for Autonomous Driving under Complex Real Conditions,b  IEEE Access, 6, pp. 5749-5765, 2017.
[24]Bertozzi, M.; Broggi, A. b Real-Time Lane and Obstacle Detection on the GOLD System,b  Proceedings of Conference on Intelligent Vehicles, 1996.
[25]Aly, M. b Real Time Detection of Lane Markers in Urban Streets,b  IEEE Intelligent Vehicles Symposium, pp. 7-12, 2008.
[26]Charbonnier, P.; Nicolle, P.; Tarel, J.-P.; Veit, T. b Evaluation of Road Marking Feature Extraction,b  Proceedings of the 11th International IEEE Conference on Intelligent Transportation Systems, pp. 174-181, 2008.
[27]Foucher, P.; Sebsadji, Y.; Tarel, J.P.; Charbonnier, P. b Robust Road Marking Extraction in Urban Environments Using Stereo Images,b  IEEE Intelligent Vehicles Symposium, pp. 394-400, 2010.
[28]Niu, J.; Lu, J.; Xu, M.; Lv, P.; Zhao, X. b Robust Lane Detection Using Two-Stage Feature Extraction with Curve Fitting,b  Pattern Recognition, 59, pp. 225-233, 2016.
[29]Haloi, M.; Jayagopi, D.B. b A Robust Lane Detection and Departure Warning System,b  IEEE Intelligent Vehicles Symposium, pp. 126-131, 2015.
[30]Byun, H.; Choi, M.; Hong, Y.; Ki, M.; Lim, K. b Vision-Based Recognition of Road Regulation for Intelligent Vehicle,b  IEEE Intelligent Vehicles Symposium, pp. 1418-1425, 2018.
[31]Ozgunalp, U. b Combination of the Symmetrical Local Threshold and the Sobel Edge Detector for Lane Feature Extraction,b  9th International Conference on Computational Intelligence and Communication Networks, pp. 24-28, 2017.
[32]Deng, J.; Han, Y. b A Real-Time System of Lane Detection and Tracking Based on Optimized RANSAC B-Spline Fitting,b  Research in Applied Computation Symposium, pp. 157-164, 2013.
[33]Piao, J.; Shin, H. b Robust Hypothesis Generation Method Using Binary Blob Analysis for Multi-Lane Detection,b  IET Image Processing, 11, pp. 1210-1218, 2017.
[34]Kuo, C.Y.; Lu, Y.R.; Yang, S.M. b On the Image Sensor Processing for Lane Detection and Control in Vehicle Lane Keeping Systems,b  Sensors, 19, pp.1-10, 2019.
[35]Divvala, S.; Farhadi, A.; Girshick, R.; Redmon, J. b You Only Look Once: Unified, Real-Time Object Detection,b  IEEE Conference on Computer Vision and Pattern Recognition, pp.779-788, 2016.
[36]Anguelov, D.; C. Berg, A.; Erhan, D.; Fu, C.-Y.; Liu, W.; Reed, S.; Szegedy, C. b SSD: Single Shot MultiBox Detector,b  European Conference on Computer Vision, pp.21-37, 2016.
[37]Darrell, T.; Donahue, J.; Girshick, R.; Malik, J. b Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation,b  IEEE Conference on Computer Vision and Pattern Recognition, pp. 580-587, 2014.
[38]Girshick, R. b Fast R-CNN,b  IEEE International Conference on Computer Vision, pp. 1440-1448, 2015.
[39]Girshick, R.; He, K.; Ren, S b Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks,b  International Conference on Neural Information Processing Systems, 39, pp. 91-99, 2015.
[40]Berg, A.C.; Fu, C.-Y.; Liu, W.; Tyagi, A.; Ranga, A. b DSSD: Deconvolutional Single Shot Detector,b  arXiv, 2017.
[41]Farhadi, A.; Redmon, J. b YOLOv3: An Incremental Improvement,b  arXiv, pp.1-6, 2018.
[42]Ai, Y.B.; Sun, C.; Wang, S.; Zhang, W.D. b Mask-Guided SSD for Small-Object Detection,b  Applied Intelligence, pp.3311-3322, 2020.
[43]Cao, Z.G.; Chen, Z.H.; Li, C.S.; Liao, T.B.; Song, W. b Detecting the Shuttlecock for a Badminton Robot: A YOLO Based Approach,b  Expert Systems with Applications, 164, 2021.
[44]Chen, X.R.; Duan, Q.W.; Mao, Y.; Peng, D.Z.; Yang, K.; Zeng, Y.N. b UAV Data: A Dataset for Unmanned Aerial Vehicle Detection,b  Soft Computing, pp. 5385-5393, 2021.
[45]He, Y.X.; Liu, D.; Wu, Y.J.; Qin, L.; Zheng, B.C. b Multi-Object Detection of Chinese License Plate in Complex Scenes,b  Computer Systems Science and Engineering, 36, pp. 145-156, 2021.
[46]Han, X.F.; Jiang, T.; Lei, Z.T.; Zhao, Z.F. b Research on Remote Sensing Image Target Recognition Based on Deep Convolution Neural Network,b  International Journal of Pattern Recognition and Artificial Intelligence, 34, 2020.
[47]Cao, C.T.; Gao, F.; Sun, C.Y.; Wang, R.L.; Yu, Y.; Yu, Y.; Zhang, J.; b Automatic Gastric Polyp Detection by Using Convolutional Neural Networks,b  Journal of Medical Imaging and Health Informatics, 11, pp. 1079-1086, 2021.
[48]Bochkovskiy, A.; Liao, H.b Y M.; Wang, C.-Y. b YOLOv4: Optimal Speed and Accuracy of Object Detection,b  arXiv, 2020.
[49]Lin, T.-Y.; Maire, M.; Belongie, S.; Hays, J.; Perona, P.; Ramanan, D.; DollB4ar, P.; Zitnick, C. L. b Microsoft COCO: Common Objects in Context,b  European Conference on Computer Vision, pp. 740-755, 2014.
[50]Mano, O.; Stein, G.P.; Shashua, A. b Vision-Based ACC with a Single Camera: Bounds on Range and Range Rate Accuracy,b  IEEE Intelligent Vehicles Symposium. Proceedings, 2003.
[51]Alland, S. W.; Bauson, W. A.; Widman, G. R. b Development of Collision Avoidance Systems at Delphi Automotive Systems,b  IEEE International Conference on Intelligent Vehicles, 2, pp. 353-358, 1998.
[52]Saneyoshi, K. b Drive Assist System Using Stereo Image Recognition,b  Proceedings of Conference on Intelligent Vehicles, 1996.
[53]Piatek, S.S.; Li, J. b LIDAR: A Photonics Guide to the Autonomous Vehicle Market,b  LaserFocusWorld, 2017.
[54]Fu, M.Y.; Lai, J.J.; Li, H.J.; Xu, D.L.; Zhang, X.X. b 2D Lidar-Based SLAM and Path Planning for Indoor Rescue Using Mobile Robots,b  Journal of Advanced Transportation, 2020, pp.1-14, 2020.
[55]Alexa, F.; Dughir, C.; Miclea, R.-C.; Sandru, F.; Silea, I. b Laser and LIDAR in a System for Visibility Distance Estimation in Fog Conditions,b  Sensors, 20, pp.1-22, 2020.
[56]Kim, T.; Park, T.-H. b Extended Kalman Filter (EKF) Design for Vehicle Position Tracking Using Reliability Function of Radar and Lidar,b  Sensors, 20, pp.1-18, 2020.
[57]Ma, B.L.; Yan, K. b Mapless Navigation Based on 2D LiDAR in Complex Unknown Environments,b  Sensors, 20, pp.1-16, 2020.
[58]Albayrak, A.; Bayir, R.; Matli, M. b Development of a Mecanum-Wheeled Mobile Robot for Dynamic- and Static-Obstacle Avoidance Based on Laser Range Sensor,b  International Journal of Fuzzy Logic and Intelligent Systems, 20, pp.188-200, 2020.
[59]Amamou, A.; Agbossou, K.; Ayevide, F.K.; Kelouwani, S.; Mohammed, A.S.; Zioui, N. b The Perception System of Intelligent Ground Vehicles in all Weather Conditions: A Systematic Literature Review,b  Sensors, 20, pp.1-34, 2020.
[60]Benavidez, P.; Jamshidi, M.; Kolar, P. b Survey of Datafusion Techniques for Laser and Vision Based Sensor Integration for Autonomous Navigation,b  Sensors, 20, pp.1-49, 2020.
[61]Ioannidis, K.; Kalogeiton, V. S.; Kosmatopoulos, E. B.; Sirakoulis, G.-Ch. b Real-Time Active SLAM and Obstacle Avoidance for an Autonomous Robot Based on Stereo Vision,b  Cybernetics and Systems, 50, pp. 239-260, 2019.
[62]Chavan, P. Y.; Chavan, Y. V.; Nyayanit, A.; Waydande, V. S. b Obstacle Detection and Avoidance for Automated Vehicle: A Review,b  Journal of Optics, 50, pp. 46-54, 2021.
[63]Chen, Z.W.; Li, X.P.; Wang, Z.; Zhao, X.M. b A Dynamic Cooperative Lane-Changing Model for Connected and Autonomous Vehicles with Possible Accelerations of a Preceding Vehicle,b  Expert Systems with Application, 173, pp.1-13, 2021.
[64]Anistratov, P.; Nielsen, L.; Olofsson, B. b Analysis and Design of Recovery Behaviour of Autonomous-Vehicle Avoidance Manoeuvres,b  Vehicle System Dynamics, pp.1-25, 2021.
[65]Ding, H.T.; Guo, K.H.; Liu, Y.; Zhang, B.; Zhang, J.W. b Planning Flexible and Smooth Paths for Lane-Changing Manoeuvres of Autonomous Vehicles,b  IET Intelligent Transport Systems, 12, pp. 200-212, 2021.
[66]Kiss, B.; Tihanyi, V.; Peter, G. b Vision and Odometry Based Autonomous Vehicle Lane Changing,b  ICT Express, 5, pp. 219-226, 2019.
[67]Subosits, J.K.; Gerdes, J.C. b From the Racetrack to the Road: Real-Time Trajectory Replanning for Autonomous Driving,b  IEEE Transactions on Intelligent Vehicles, 4, pp. 309-320, 2019.
[68]Shiller, Z.; Sundar, S. b Emergency Lane-Change Maneuvers of Autonomous Vehicles,b  Dynamic Systems, Measurement, and Control, 120, pp. 37-44, 1998.
[69]Arikere, A.; Yang, D.; Klomp, M.; Lidberg, M. b Integrated Evasive Manoeuvre Assist for Collision Mitigation with Oncoming Vehicles,b  Vehicle System Dynamics, 56, pp. 1577-1603, 2018.
[70]Fors, V.; Olofsson, B.; Nielsen, L. b Formulation and Interpretation of Optimal Braking and Steering Patterns Towards Autonomous Safety-Critical Manoeuvres,b  Vehicle System Dynamics, 57, pp. 1206-1223, 2019.
[71]Kanade, T.; Maravec, H.; Stentz, A.; Thorpe, C. E.; Wallace, R.; Whittaker, W. b First Results in Robot Road Following,b  Proceedings of the International Joint Conference on Artificial Intelligence, pp. 1089-1095, 1985.
[72]Armstrong, D.; Crane, C. D.; Wit, J. b Autonomous Ground Vehicle Path Tracking,b  Journal of Robotic Systems, 21, pp. 439-449, 2004.
[73]Shan, Y.; Yang, W.; Chen, C.; Li, B.; Zhou, J.; Zheng, L. b CF-Pursuit: A Pursuit Method with a Clothoid Fitting and a Fuzzy Controller for Autonomous Vehicles,b  International Journal of Advanced Robotic Systems, 12, pp. 1-13, 2015.
[74]Fiore, G.; Frazzoli, E.; How, J.; Kuwata, Y.; Karaman, S.; Teo, J. b Real-Time Motion Planning with Applications to Autonomous Urban Driving,b  IEEE Transactions on Control Systems Technology, 17, pp. 1105-1118, 2009.
[75]b Taiwan Unveils Driverless WinBus,b  Taiwan News, August 26, 2019.
[76]Han, M. H.; Lee, K. B. b Lane-Following Method for High Speed Autonomous Vehicles,b  International Journal of Automotive Technology, 9, pp. 607-613, 2008.
[77]Tsogas, M.; Polychronopoulos, A.; Amditis, A. b Using Digital Maps to Enhance Lane Keeping Support Systems,b  2007 IEEE Intelligent Vehicles Symposium, pp. 148-153, 2007.
[78]Hannan, M.; Javadi, M.; Samad, S.; Hussain, A. b A Robust Vision-Based Lane Boundaries Detection Approach for Intelligent Vehicles,b  Information Technology Journal, 11, pp. 1184-1192, 2012.
[79]Ding, Y.; Sun, K.; Xu, Z.; Zhang, Y. b Fast Lane Detection Based on Birdb s Eye View and Improved Random Sample Consensus Algorithm,b  Multimedia Tools and Applications, pp. 1-20, 2016.
[80]Nieto, M.; Laborda, J. A.; Salgado, L. b Road Environment Modeling Using Robust Perspective Analysis and Recursive Bayesian Segmentation,b  Machine Vision and Applications, 22, pp. 927-945, 2010.
[81]Yang, U.; Yoo, H.; Sohn, K. b Gradient-Enhancing Conversion for Illumination-Robust Lane Detection,b  IEEE Transactions on Intelligent Transportation Systems, 14, pp. 1083-1094, 2013.
[82]Boukerche, A.; Mammeri, A.; Tang, Z. b A Real-Time Lane Marking Localization, Tracking and Communication System,b  Computer Communications, 73, pp. 132-143, 2016.
[83]Rotaru, C.; Graf, T.; Zhang, J. b Color Image Segmentation in HSI Space for Automotive Applications,b  Journal of Real-Time Image Processing, 3, pp. 311-322, 2008.
[84]Son, J.; Yoo, H.; Kim, S.; Sohn, K. b Real-Time Illumination Invariant Lane Detection for Lane Departure Warning System,b  Expert Systems with Applications, 42, pp. 1816-1824, 2015.
[85]Choi, H.-C.; Park, J.-M.; Choi, W.-S.; Oh, S.-Y. b Vision-Based Fusion of Robust Lane Tracking and Forward Vehicle Detection in a Real Driving Environment,b  International Journal of Automotive Technology, 13, pp. 653-669, 2012.
[86]Lim, K. H.; Seng, K. P.; Ang, L.-M. b River Flow Lane Detection and Kalman Filtering-Based B-Spline Lane Tracking,b  International Journal of Vehicular Technology, pp. 1-10, 2012.
[87]LC3pez, A.; Serrat, J.; CaC1ero, C.; Lumbreras, F.; Graf, T. b Robust Lane Markings Detection and Road Geometry Computation,b  International Journal of Automotive Technology, 11, pp. 395-407, 2010.
[88]Du, X.; Tan, K.K. b Vision-Based Approach Towards Lane Line Detection and Vehicle Localization,b  Machine Vision and Applications, 27, pp. 175-191, 2015.
[89]Marino, R.; Scalzi, S.; Netto, M. b Nested PID Steering Control for Lane Keeping in Autonomous Vehicles,b  Control Engineering Practice, 19, pp. 1459-1467, 2011.
[90]Binti, M.; Hussein, M.; Samuel, M. b A Review of Some Pure-Pursuit Based Path Tracking Techniques for Control of Autonomous Vehicle,b  International Journal of Computer Applications, 135, pp. 35-38, 2016.
[91]Guizzo, E.; Ackerman, E. b Wizards of ROS: Willow Garage and the Making of the Robot Operating System,b  IEEE Spectrum: Technology, Engineering, and Science News, 7 November 2017.
[92]Jakubowicz, J.; Morel, J. M.; Randall, G.; Von Gioi, R. G. b LSD: A Line Segment Detector,b  IPOL Journal-Image Processing On Line, 2, pp. 35-55, 2010.
[93]Chen, H. J.; Hao, X. L.; Ye, Y. Y. b Lane Detection Method Based on Lane Structural Analysis and CNNs,b  IET Intelligent Transport Systems, 12, pp. 513-520, 2018.
[94]Bhujbal, P. N.; Dhane, D. M.; Narote, A. S.; Narote, S. P. b A Review of Recent Advances in Lane Detection and Departure Warning System,b  Pattern Recognition, 73, pp. 216-234, 2018.
[95]Chen, Q.; Chen, R.; Lai, T.; Li, X.; Wang, S.; Yang, C. b Weighted Feature Pyramid Networks for Object Detection,b  2019 IEEE Intl Conference on Parallel & Distributed Processing with Applications, pp. 1500-1504, 2019.
[96]Farhadi, A.; Redmon, J. b YOLO9000: Better, Faster, Strongerb  IEEE Conference on Computer Vision and Pattern Recognition, pp. 6517-6525, 2017.
[97]Redmon, J. b The Weights of Darknet-53,b  Dataset available from https://pjreddie.com/media/files/darknet53.conv.74, 2016.
[98]Liu, R.W.; Yang, M.; Nie, X. b Deep Neural Network-Based Robust Ship Detection under Different Weather Conditions,b  2019 IEEE Intelligent Transportation Systems Conference (ITSC), 2019.
[99]Girshick, R.; He, K.; Ren, S.; Sun, J. b Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks,b  IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, pp. 1137-1149, 2015.
[100] Abu-El-Haija, S.; Alldrin, N.; Belongie, S.; Cai, D.; Chechik, G.; Duerig, T.; Feng, Z.; Ferrari, V.; Gomes, V.; Gupta, A.; Kuznetsova, A.; Krasin, I.; Murphy, K.; Narayanan, D.; Popov, S.; Sun, C.; Uijlings, J.; Rom, H.; Veit, A. b Openimages: A Public Dataset for Large-Scale Multi-Label and Multi-Class Image Classification,b  Dataset available from https://github.com/openimages, 2017.
[101]Aly, M. A. El-D. b Caltech Lanes Dataset,b  Dataset available from http://www.mohamedaly.info/datasets/caltech-lanes, 2011.
[102]b ISO 26262-1:2018 Road Vehicles-Functional Safety,b  International Organization for Standardization, 2018.
[103]b Railway Applications-the Specification and Demonstration of Reliability, Availability, Maintainability and Safety (RAMS),b  European Standards, 2017.