Browse > Article
http://dx.doi.org/10.3837/tiis.2019.11.002

A Review of Intelligent Self-Driving Vehicle Software Research  

Gwak, Jeonghwan (Computer Information Technology, Korea National University of Transportation)
Jung, Juho (Computer Information Technology, Korea National University of Transportation)
Oh, RyumDuck (Computer Information Technology, Korea National University of Transportation)
Park, Manbok (Department of Electronic Engineering, Korea National University of Transportation)
Rakhimov, Mukhammad Abdu Kayumbek (Computer Information Technology, Korea National University of Transportation)
Ahn, Junho (Computer Information Technology, Korea National University of Transportation)
Publication Information
KSII Transactions on Internet and Information Systems (TIIS) / v.13, no.11, 2019 , pp. 5299-5320 More about this Journal
Abstract
Interest in self-driving vehicle research has been rapidly increasing, and related research has been continuously conducted. In such a fast-paced self-driving vehicle research area, the development of advanced technology for better convenience safety, and efficiency in road and transportation systems is expected. Here, we investigate research in self-driving vehicles and analyze the main technologies of driverless car software, including: technical aspects of autonomous vehicles, traffic infrastructure and its communications, research techniques with vision recognition, deep leaning algorithms, localization methods, existing problems, and future development directions. First, we introduce intelligent self-driving car and road infrastructure algorithms such as machine learning, image processing methods, and localizations. Second, we examine the intelligent technologies used in self-driving car projects, autonomous vehicles equipped with multiple sensors, and interactions with transport infrastructure. Finally, we highlight the future direction and challenges of self-driving vehicle transportation systems.
Keywords
Self-driving vehicle; Road infrastructure; Deep learning; Machine learning; Localization;
Citations & Related Records
연도 인용수 순위
  • Reference
1 USDOT Releases 2016 Fatal Traffic Crash Data
2 Howard Daniel, Dai Danielle, "Public Perceptions of Self-driving Cars: The Case of Berkeley, California," Transportation Research Board 93rd Annual Meeting, 2013.
3 Iftikhar Ahmad, Rafidah Md Noor, Ihsan Ali, Muhammad Imran, Athanasios Vasilakos, "Characterizing the role of vehicular cloud computing in road traffic management," International Journal of Distributed Sensor Networks, May 12, 2017.
4 KPMG, Center for Automotive Research, "Self-driving cars: The next revolution," New York, 2012.
5 Tesla.
6 Waymo.
7 Uber.
8 General Motors.
9 Brody Huval, Tao Wang, Sameep Tandon, Jeff Kiske, Will Song, Joel Pazhayampallil, Mykhaylo. Andriluka, Pranav Rajpurkar, Toki Migimatsu, Royce Cheng-Yue, Fernando Mujica, Adam Coates, Andrew Y. Ng, "An Empirical Evaluation of Deep Learning on Highway Driving," Computer Vision and Pattern Recognition, 17 Apr 2015.
10 Sermanet, Pierre, et al., "Overfeat: Integrated recognition, localization and detection using convolutional networks," Computer Vision and Pattern Recognition, 2014.
11 Szegedy, Christian, Alexander Toshev, and Dumitru Erhan, "Deep neural networks for object detection," Advances in Neural Information Processing Systems, 2013.
12 Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton, "Imagenet classification with deep convolutional neural networks," in Proc. of NIPS'12 Proceedings of the 25th International Conference on Neural Information Processing Systems, Volume 1, Pages 1097-1105, 2012.
13 S. Ren, K. He, R. Girshick, and J. Sun, "Faster R-CNN: Towards real-time object detection with region proposal networks," Advances in neural information processing systems, pages 91-99, 2015.
14 Joseph Redmon, Santosh Divvala, Ross Girshick , Ali Farhadi, "You Only Look Once: Unified, Real-Time Object Detection," Computer Vision and Pattern Recognition, 8 Jun 2015.
15 Jonathan Huang, Vivek Rathod, Chen Sun, Menglong Zhu, Anoop Korattikara, Alireza Fathi, Ian Fischer, Zbigniew Wojna, Yang Song, Sergio Guadarrama, Kevin Murphy, "Speed/accuracy trade-offs for modern convolutional object detectors," Computer Vision and Pattern Recognition, 25 Apr 2017.
16 Ross Girshick, Jeff Donahue, Trevor Darrell, Jitendra Malik, "Rich feature hierarchies for accurate object detection and semantic segmentation Tech report (v5)," Computer Vision and Pattern Recognition, 22 Oct 2014.
17 Ross Girshick, "Fast R-CNN," Computer Vision and Pattern Recognition, 8 Sep 2015.
18 Joseph Redmon, Ali Farhadi, "YOLO 9000: Better, Faster, Stronger," Computer Vision and Pattern Recognition, 25 Dec 2016.
19 Joseph Redmon, Ali Farhadi, "YOLOv3: An Incremental Improvement," Computer Vision and Pattern Recognition, 8 Apr 2018.
20 Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich, "Going Deeper with Convolutions," Computer Vision and Pattern Recognition, 17 Sep 2014.
21 Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jonathon Shlens, Zbigniew Wojna, "Rethinking the Inception Architecture for Computer Vision," Computer Vision and Pattern Recognition, 11 Dec 2015.
22 Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, Alex Alemi, "Inception-v4, Inception-Resnet and the Impact of Residual Connections on Learning," Computer Vision and Pattern Recognition, 23 Aug 2016.
23 M. Bosse, P. Newman, J. Leonard, M. Soika, W. Feiten, and S. Teller, "Simultaneous localization and map building in large-scale cyclic environments using the atlas framework," The International Journal of Robotics Research, 23(12), 2004.
24 Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun, "Deep Residual Learning for Image Recognition," Computer Vision and Pattern Recognition, 10 Dec 2015.
25 Tensorflow Object detection model zoo.
26 Kulkarni, R., Dhavalikar, S., & Bangar, S, "Traffic Light Detection and Recognition for Self Driving Cars Using Deep Learning," in Proc. of 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA), 2018.
27 Corovic, A., Ilic, V., Duric, S., Marijan, M., & Pavkovic, B, "The Real-Time Detection of Traffic Participants Using YOLO Algorithm," in Proc. of 2018 26th Telecommunications Forum (TELFOR), 2018.
28 T. Duckett, S. Marsland, and J. Shapiro, "Learning globally consistent maps by relaxation," in Proc. of 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065), 2000.
29 J. Folkesson and H. I. Christensen, "Robust SLAM," IFAC Proceedings Volumes, 37(8), 722-727, 2004.
30 U. Frese, P. Larsson, and T. Duckett, "A multilevel relaxation algorithm for simultaneous localization and mapping," IEEE TRANSACTIONS ON ROBOTICS, VOL. 21, NO. 2, APRIL 2005.
31 S. Thrun and M. Montemerlo, "The GraphSLAM algorithm with applications to large-scale mapping of urban structures," IJRR, 25(5/6), 403-429, 2005.
32 Francois Dion, Hesham Rakha, and Youn-Soo Kang, "Comparison of delay estimates at under-saturated and over-saturated pre-timed signalized intersections," Transportation Research Part B: Methodological, Volume 38, Issue 2, Pages 99-122, February 2004.   DOI
33 Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun, "Identity Mappings in Deep Residual Networks," Computer Vision and Pattern Recognition, 25 Jul 2016.
34 J. Levinson, M. Montemerlo, and S. Thrun, "Map-based precision vehicle localization in urban environments," In Robotics: Science and Systems III, 2007.
35 J. Levinson and S. Thrun, "Robust vehicle localization in urban environments using probabilistic maps," in Proc. of 2010 IEEE International Conference on Robotics and Automation, 2010.
36 Ryan W. Wolcott and Ryan M. Eustice, "Fast LIDAR Localization using Multiresolution Gaussian Mixture Maps," in Proc. of 2015 IEEE International Conference on Robotics and Automation (ICRA), 2015.
37 Ioan Andrei Barsan, Shenlong Wang, Andrei Pokrovsky, Raquel Urtasun, "Learning to Localize Using a LiDAR Intensity Map," in Proc. of Proceedings of the 2nd Conference on Robot Learning, PMLR, 87, 605-616, 2018.
38 Alan J Miller, "Settings for fixed-cycle traffic signals," Journal of the Operational Research Society, Volume 14, Issue 4, pp 373-386, December 1963.   DOI
39 Lior Kuyer, Shimon Whiteson, Bram Bakker, and Nikos Vlassis, "Multiagent reinforcement learning for urban traffic control using coordination graphs," ECML PKDD 2008: Machine Learning and Knowledge Discovery in Databases, pp 656-671, 2008.
40 Elise van der Pol and Frans A. Oliehoek, "Coordinated Deep Reinforcement Learners for Traffic Light Control," in Proc. of 30th Conference on Neural Information Processing Systems (NIPS 2016), 2016.
41 Marco Wiering, "Multi-agent reinforcement learning for traffic light control," in Proc. of ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning, Pages 1151-1158, 2000.
42 Hua Wei, Guanjie Zheng, Huaxiu Yao, and Zhenhui Li, "IntelliLight: A Reinforcement Learning Approach for Intelligent Traffic Light Control," in Proc. of KDD '18: The 24th ACM SIGKDD, 2018.
43 Ho, T.J, Chung, M.J, "Information-Aided Smart Schemes for Vehicle Flow Detection Enhancements of Traffic Microwave Radar Detectors," Appl. Sci., 6(7), 196, 2016.   DOI
44 Zhang, Z, Jia, L, Qin, Y, "Level-of-Service Based Hierarchical Feedback Control Method of Network-Wide Pedestrian Flow," Mathematical Problems in Engineering, Volume 2016, Article ID 9617890, 14 pages, 2016.
45 Salvo, G, Caruso, L, Scordo, A, Guido, G, Vitale, A, "Traffic data acquirement by unmanned aerial vehicle," Eur. J. Remote Sens., 50(1), 343-351, 2017.   DOI
46 Knoop, V. Daganzo, C, "The Effect of Pedestrian Crossings on Traffic Flow," EJTIR, 18(2), 145-157, 2018.
47 Zhang, H, Zhang, C, Wei, Y, Chen, F, "The effects of mobile phone use on pedestrian crossing behavior and safety at unsignalized intersections," in Proc. of the 4th International Conference on Transportation Information and Safety (ICTIS), Banff, AB, Canada, 8-10 August 2017.
48 Giovanni Pau, Tiziana Campisi , Antonino Canale, Alessandro Severino, Mario Collotta and Giovanni Tesoriere, "Smart Pedestrian Crossing Management at Traffic Light Junctions through a Fuzzy-Based Approach," Future Internet, 10(2), 15, 2018.   DOI
49 Jian Wang, Yameng Shao, Yuming Ge and Rundong Yu, "A Survey of Vehicle to Everything (V2X) Testing," Sensors (Basel), 19(2), 334, Jan 2019.   DOI
50 Meng Lu, Robbin Blokpoel, Julian Schindler, Sven Maerivoet, Evangelos Mintsis, "ICT Infrastructure for Cooperative, Connected and Automated Transport in Transition Areas," in Proc. of 7th Transport Research Arena TRA 2018, Vienna, Austria, April 16-19 2018.
51 TrafficVision.
52 swarco.
53 Li Li, Yisheng Lv, and Fei-Yue Wang, "Traffic signal timing via deep reinforcement learning," IEEE/CAA Journal of Automatica Sinica, 3(3), p247-254, 2016.   DOI
54 Nissan Is Rolling Out Cars That Talk to Each Other.
55 savari.
56 Cars: Connecting People.
57 AutoCrypt V2G : Vehicle to Grid.
58 Precision Road Map.
59 Richard Szelisk, "Computer Vision Algorithms and Applications," Springer, 2011.
60 Jun Jo, Yukito Tsunoda, Bela Stantic, Alan Wee-Chung Liew, "A Likelihood-Based Data Fusion Model for the Integration of Multiple Sensor Data: A Case Study with Vision and Lidar Sensors," Robot Intelligence Technology and Applications 4, pp 489-500, 2017.
61 Qualcomm, 2019,
62 Essys, 2019,
63 Baidu Autonomous Driving Unit.
64 Weixin Lu, Yao Zhou, Guowei Wan, Shenhua Hou, Shiyu Song, "L3-Net: Towards Learning Based LiDAR Localization for Autonomous Driving," in Proc. of The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6389-6398, 2019,
65 KT.
66 convex.