A Comprehensive Review on Creating Autonomous Car Systems
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Autonomous vehicles are around us and are finding trillions of recent developed applications, starting from driverless cars to automatically observation in critical areas. Continual progress in these technologies within the earlier decades makes these inventions possible. However, the planning of these technologies which must be surmounted, to apply efficient, useful supremely important and safe tasks of those independent units are daunting as well as numerous. Over the past decade, many researches are published within the domain of autonomous vehicles. Yet, most of them concentrate only on a selected technological area, such as vehicle control, visual environment perception ... etc. During this paper we present a brief yet comprehensive overview on the most significant key ingredients of autonomous cars, it covers almost all bases, from available models and types, their functions, importance, in addition to the most important related work.
S. Liu, J. Tang, S. Wu, & J. Gaudiot. Creating autonomous vehicle systems. Synthesis Lectures on Computer Science. 2020; 8 (2): i-216.
Moody, Joanna, Nathaniel Bailey, and Jinhua Zhao. Public perceptions of autonomous vehicle safety: An international comparison. Safety science. 2020; 121:634. http://dx.doi.org/10.1016/j.ssci.2019.07.022
S. Hanky. Introduction to Self-Driving Vehicle Technology. Chapman and Hall/CRC; 2019.
Sumit Ranjan, and S. Senthamilarasu. Applied Deep Learning and Computer Vision for Self-Driving Cars. Packt Publishing Ltd; 2020.
L. Reddy Cenkeramaddi, J. Bhatia, A. Jha, S. Kumar Vishkarma and J. Soumya. A Survey on Sensors for Autonomous Systems. 15th Conference on Industrial Electronics and Applications (ICIEA) 2020,pp.1182-118,IEEE. http://dx.doi.org/10.1109/ICIEA48937.2020.9248282
Kuutti, Sampo, et al. A survey of the state-of-the-art localization techniques and their potentials for autonomous vehicle applications. IEEE Internet of Things Journal. 2018; 5(2):829-846. http://dx.doi.org/10.1109/JIOT.2018.2812300
上條俊介, 古艶磊, and 許立達. Autonomous vehicle technologies: Localization and mapping. 電子情報通信学会 基礎・境界ソサイエティ. Fundamentals Review. 2015; 9 (2):131-141.
Fernández-Madrigal, Juan-Antonio, and José Luis Blanco Claraco. Simultaneous Localization and Mapping for Mobile Robots: Introduction and Methods. IGI Global; 2013. http://dx.doi.org/10.4018/978-1-4666-2104-6
T. Bailey, & H. Durrant-Whyte. Simultaneous localization and mapping (SLAM): Part II. IEEE robotics & automation magazine. 2006; 13(3):108-117. http://dx.doi.org/10.1109/MRA.2006.1678144
Bengtsson, Thomas, Peter Bickel, and Bo Li. Curse-of-dimensionality revisited: Collapse of the particle filter in very large scale systems. Probability and statistics: Essays in honor of David A.Freedman,2008;2:316-334. http://dx.doi.org/10.1214/193940307000000518
Doucet, Arnaud, et al. Rao-Blackwellised particle filtering for dynamic Bayesian networks. arXiv preprint arXiv. 2013; 1301.3853.
Cadena, Cesar, et al. Past, present, and future of simultaneous localization and mapping: Toward the robust-perception age. IEEE Transactions on robotics.2016;32(6):1309-1332. http://dx.doi.org/10.1109/TRO.2016.2624754
F. Gustafsson. Particle filter theory and practice with positioning applications. IEEE Aerospace and Electronic Systems Magazine. 2010, 25(7):53–82. http://dx.doi.org/10.1109/MAES.2010.5546308
John H Halton. Sequential monte carlo techniques for solving non-linear systems. Monte Carlo Methods and Applications MCMA. 2006; 12(2):113–141. http://dx.doi.org/10.1515/156939606777488879
E. Mouragnon, M. Lhuillier, M. Dhome, F. Dekeyser, and P. Sayd. Real time localization and 3D reconstruction in Computer Vision and Pattern Recognition. Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06) 2006, 363-370, IEEE.
C. Stachniss, John J. Leonard, and S. Thrun. Simultaneous Localization and Mapping. Springer International Publishing. Cham; 2016 pp. 1153–1176. 2016. http://dx.doi.org/10.1007/978-3-319-32552-1_46
Grissett, Giorgio, et al. A tutorial on graph-based SLAM. IEEE Intelligent Transportation Systems Magazine.2010;2(4):31-43. http://dx.doi.org/10.1109/MITS.2010.939925
B. Triggs, Philip F. McLauchlan, Richard I. Hartley, and Andrew W. Fitzgibbon. Bundle adjustment - A modern synthesis. In Proceedings of the International Workshop on Vision Algorithms: Theory and Practice, ICCV ’99, London, UK, 2000,298–372,Springer-Verlag. http://dx.doi.org/10.1007/3-540-44480-7_21
G. Yanlei, Li-Ta Hsu, and Shunsuke Kamijo. Vehicle localization based on global navigation satellite system aided by three-dimensional map. Transportation Research Record. 2017; 2621(1):55-61.
Meng, Xiaoli, Heng Wang, and Bingbing Liu. A robust vehicle localization approach based on gnss/imu/dmi/lidar sensor fusion for autonomous vehicles. Sensors. 2017; 17(9):2140-2159. http://dx.doi.org/10.3390/s17092140
Guang, Xingxing, et al. An autonomous vehicle navigation system based on inertial and visual Sensors. Sensors. 2018; 18(9):2952. http://dx.doi.org/10.3390/s18092952
L. Khaoula and P. Bonnifait. Cooperative localization for autonomous vehicles sharing GNSS measurements. Cooperative Localization and Navigation. CRC Press; 2019 pp. 521-546.
Santos, Giovanni A., et al. Improved localization framework for autonomous vehicles via tensor and antenna array based GNSS receivers. Workshop on Communication Networks and Power Systems 2020,pp.1-6,IEEE. http://dx.doi.org/10.1109/WCNPS50723.2020.9263757
Onyekpe, Uche, Vasile Palade, and Stratis Kanarachos. Learning to localise automated vehicles in challenging environments using Inertial Navigation Systems (INS). Applied Sciences. 2021;11(3):1270. http://dx.doi.org/10.3390/app11031270
Chen, Xieyuanli, et al. Range Image-based LiDAR Localization for Autonomous Vehicles. International Conference on Robotics and Automation (ICRA) 2021, 5802-5808, IEEE. http://dx.doi.org/10.1109/ICRA48506.2021.9561335
Luca Venturi, Krishtof Korda. Hands-On Vision and Behavior for Self-Driving Cars. Packt Publishing Ltd; 2021.
David G Lowe. Distinctive image features from scale-invariant key- points. International Journal of Computer Vision. 2004; 60 (2):91–110.
N. Dalal and B. Triggs. Histograms of oriented gradients for human detection. In 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR'05) 2005 (pp. 886–893). IEEE. http://dx.doi.org/10.1109/CVPR.2005.177
Matas, Jiri, et al. Robust wide-baseline stereo from maximally stable extremal regions. Image and vision computing.2004;22(10):761-767. http://dx.doi.org/10.1016/j.imavis.2004.02.006
B. Leo. Random forests. Machine learning. 2001; 45(1): 5-32.
J. Giebel, D. Gavrila, and C. Schnörr. A Bayesian framework for multi-cue 3d object tracking. Computer Vision-ECCV.2004;pp.241–252. http://dx.doi.org/10.1007/978-3-540-24673-2_20
Breitenstein, M.D., Reichlin, F., Leibe, B., Koller-Meier, E., and Van Gool, L. Online multiperson tracking-by-detection from a single, uncalibrated camera. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2011; 33(9):1820–1833. http://dx.doi.org/10.1109/TPAMI.2010.232
Harshitha, R., and J. Manikandan. Design of a real-time pedestrian detection system for autonomous vehicles. In Region 10 Symposium (TENSYMP) 2017(pp.1-4).IEEE. http://dx.doi.org/10.1109/TENCONSpring.2017.8069981
Hu, Chaowei, et al. Embedding CNN-based fast obstacles detection for autonomous vehicles. SAE Technical Paper, 2018; No. 2018-01-1622. http://dx.doi.org/10.4271/2018-01-1622
Jhung, Junekyo, et al. End-to-end steering controller with cnn-based closed-loop feedback for autonomous vehicles. In IEEE intelligent vehicles symposium (IV) 2018, 617-622, IEEE. http://dx.doi.org/10.1109/IVS.2018.8500440
Gu, Xinping, et al. Vehicle lane change decision model based on random forest. International Conference on Power, Intelligent Computing and Systems (ICPICS) 2019, 115-120, IEEE. http://dx.doi.org/10.1109/ICPICS47731.2019.8942520
Valiente, Rodolfo, et al. Controlling steering angle for cooperative self-driving vehicles utilizing cnn and lstm-based deep networks. In 2019 IEEE intelligent vehicles symposium (IV), 2019, 2423-2428,IEEE. http://dx.doi.org/10.1109/IVS.2019.8814260
Garcia Cuenca, Laura, et al. Machine learning techniques for undertaking roundabouts in autonomous driving. Sensors. 2019; 19(10):2386. http://dx.doi.org/10.3390/s19102386
Liu, Yonggang, et al. A novel lane change decision-making model of autonomous vehicle based on support vector machine. IEEE Access. 2019,7:26543-26550. http://dx.doi.org/10.1109/ACCESS.2019.2900416
Hbaieb, Amal, Jihene Rezgui, and Lamia Chaari. Pedestrian detection for autonomous driving within cooperative communication system. Wireless Communications and Networking Conference (WCNC) 2019, 1-6, IEEE. http://dx.doi.org/10.1109/WCNC.2019.8886037
Pranav, K. B., and J. Manikandan. Design and evaluation of a real-time pedestrian detection system for autonomous vehicles. Zooming Innovation in Consumer Technologies Conference (ZINC) 2020, 155-159, IEEE. http://dx.doi.org/10.1109/ZINC50678.2020.9161768
Gadepally, Vijay, Ashok Krishnamurthy, and Ümit Özgüner. A framework for estimating long term driver behavior. Journal of advanced transportation. 2017; Vol. 2017. Article ID 3080859. http://dx.doi.org/10.1155/2017/3080859
N. Odey, and A. Marhoon. Feature Deep Learning Extraction Approach for Object Detection in Self-Driving Cars. Iraqi Journal for Electrical And Electronic Engineering. 2023; 19 (2): 62-69. https://doi.org/10.37917/ijeee.19.2.8
S. Bonnin, T. Weisswange, F. Kummert, and J. Schmuedderich. General behavior prediction by a combination of scenario-specific models. IEEE Transactions on Intelligent Transportation Systems. 2014;15(4):1478–1488. http://dx.doi.org/10.1109/TITS.2014.2299340
Kumar, P., Perrollaz, M., Lefevre, S., and Laugier, C. Learning-based approach for online lane change intention prediction. In Proceedings of the IEEE Intelligent Vehicles Symposium 2013, 797–802. IEEE. http://dx.doi.org/10.1109/IVS.2013.6629564
Geisberger, Robert, et al. Exact routing in large road networks using contraction hierarchies. Transportation Science.2012;46(3):388-404. http://dx.doi.org/10.1287/trsc.1110.0401
Junqing Wei, Jarrod M Snider, Tianyu Gu, John M Dolan, and Bakhtiar Litkouhi. A behavioral planning framework for autonomous driving. Intelligent Vehicles Symposium Proceedings, 2014, 458–464,IEEE. http://dx.doi.org/10.1109/IVS.2014.6856582
Christos Katrakazas, Mohammed Quddus, Wen-Hua Chen, and Lipika Deka. Real-time motion planning methods for autonomous on-road driving: State-of-the-art and future research directions. Transportation Research Part C: Emerging Technologies. 2015; 60: 416–442.
Shin, Seho, Joonwoo Ahn, and Jaeheung Park. Desired orientation rrt (do-rrt) for autonomous vehicle in narrow cluttered spaces. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2016, 4736-4741, IEEE. http://dx.doi.org/10.1109/IROS.2016.7759696
Kiss, Domokos, and Dávid Papp. Effective navigation in narrow areas: A planning method for autonomous cars. 15th International Symposium on Applied Machine Intelligence and Informatics (SAMI) 2017, 423-430, IEEE. http://dx.doi.org/10.1109/SAMI.2017.7880346
Latip, Nor Badariyah Abdul, Rosli Omar, and Sanjoy Kumar Debnath. Optimal path planning using equilateral spaces oriented visibility graph method. International Journal of Electrical and Computer Engineering. 2017; 7(6): pp. 3046. http://dx.doi.org/10.11591/ijece.v7i6.pp3046-3051
Lamini, Chaymaa, Said Benhlima, and Ali Elbekri. Genetic algorithm based approach for autonomous mobile robot path planning. Procedia Computer Science.2018;127:180-189. http://dx.doi.org/10.1016/j.procs.2018.01.113
Özcan, Melih, and Mustafa Mert Ankarali. Feedback motion planning for a dynamic car model via random sequential composition. International conference on systems, man and cybernetics (SMC). 2019, 4239-4244, IEEE. http://dx.doi.org/10.1109/SMC.2019.8913917
Chen, Yuying, Haoyang Ye, and Ming Liu. Hierarchical Trajectory Planning for Autonomous Driving in Low-speed Driving Scenarios Based on RRT and Optimization. arXiv preprint arXiv. 2019; 1904:2606. https://doi.org/10.48550/arXiv.1904.02606
Y. Xiang, A. Alahi, and S. Savarese, Learning to track: Online multi-object tracking by decision making. In Proceedings of International Conference on Computer Vision 2015, 4705–4713, IEEE. http://doi.org/10.1109/ICCV.2015.534
L. S. Liu, J. Lin, J. Yao, D. He, J. Zheng, J. Huang, & P. Shi. Path planning for smart car based on Dijkstra algorithm and dynamic window approach. Wireless Communications and Mobile Computing. 2021; Article ID 8881684. http://dx.doi.org/10.1155/2021/8881684