CFP last date
20 August 2024
Reseach Article

A Comparative Study of Recent Practices and Technologies in Advanced Driver Assistance System

by Busra Jahan Tanu, Md. Shihab Ahmed, Md. Azim Islam, Bilkis Jamal Ferdosi
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 186 - Number 28
Year of Publication: 2024
Authors: Busra Jahan Tanu, Md. Shihab Ahmed, Md. Azim Islam, Bilkis Jamal Ferdosi
10.5120/ijca2024923796

Busra Jahan Tanu, Md. Shihab Ahmed, Md. Azim Islam, Bilkis Jamal Ferdosi . A Comparative Study of Recent Practices and Technologies in Advanced Driver Assistance System. International Journal of Computer Applications. 186, 28 ( Jul 2024), 54-66. DOI=10.5120/ijca2024923796

@article{ 10.5120/ijca2024923796,
author = { Busra Jahan Tanu, Md. Shihab Ahmed, Md. Azim Islam, Bilkis Jamal Ferdosi },
title = { A Comparative Study of Recent Practices and Technologies in Advanced Driver Assistance System },
journal = { International Journal of Computer Applications },
issue_date = { Jul 2024 },
volume = { 186 },
number = { 28 },
month = { Jul },
year = { 2024 },
issn = { 0975-8887 },
pages = { 54-66 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume186/number28/a-comparative-study-of-recent-practices-and-technologies-in-advanced-driver-assistance-system/ },
doi = { 10.5120/ijca2024923796 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-07-26T23:00:21.079025+05:30
%A Busra Jahan Tanu
%A Md. Shihab Ahmed
%A Md. Azim Islam
%A Bilkis Jamal Ferdosi
%T A Comparative Study of Recent Practices and Technologies in Advanced Driver Assistance System
%J International Journal of Computer Applications
%@ 0975-8887
%V 186
%N 28
%P 54-66
%D 2024
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Road accidents present a pressing global public health concern particularly impacting low and middle-income countries like Bangladesh. Advanced Driver Assistance Systems (ADAS) can help in the reduction of risks at a significant level. There are few comprehensive reviews of different significant components of ADAS up to early 2021, highlighting strengths, weaknesses, and research gaps in this rapidly evolving field. This article offers a systematic review of high-quality research articles in the field, encompassing publications from March 2021 to December 2023. This review tends to give a clear and concise view of the key advancements in sensor technologies, machine learning techniques used in the system, qualitative assessment of the datasets available, popular performance metrics, and the projection of trends in the coming days. Cameras are found to be the most used sensor technology while working with ADAS. With the advancement of machine learning, the existing literature tends to use several benchmark models instead of sticking to one or more traditional ones. The existing datasets cover various weather scenarios, mostly sunny, rainy, and foggy weather. These datasets are mostly on urban roads and highways. Researchers tend to evaluate the performance of the systems using metrics that rely on confusion matrices. As per this study, it can be said that a completely real-time system is still a crying need. Due to the existence of a diverse range of road scenarios, a dataset covering all of them is not available. Future research can go in the direction of using hybrid sensor technology, focusing on versatile datasets, and using improved machine and deep learning technologies.

References
  1. WHO, “World Health Organization: WHO. (2022). road traffic injuries.” 2022, last accessed: 30 May, 2023. [Online]. Available: https://www.who.int/news-room/fact-sheets/detail
  2. BRTA, “Annual reports: BRTA,” 2021-2022, last accessed: 30 May, 2023. [Online]. Available: http://brta.gov.bd/site/view/annual reports/Annual-Report
  3. P. Alo, “Samsur rahman. (08 January, 2023). road accidents kill highest number of students in 2022.” 2023, last accessed: 30 May, 2023. [Online]. Available: https://en.prothomalo.com/bangladesh/accident/nr3n0krxfx
  4. T. D. Star, “Staff correspondent (8 January, 2023). road accidents eat up over 1.5pc of GDP. the daily star.” 2023, last accessed: 30 May, 2023. [Online]. Available: https://www.thedailystar.net/news/bangladesh/transport/news/road-accidents-eat-over-15pcgdp-3214976
  5. J. J. Rolison, S. Regev, S. Moutari, and A. Feeney, “What are the factors that contribute to road accidents? an assessment of law enforcement views, ordinary drivers’ opinions, and road accident records,” Accident Analysis & Prevention, vol. 115, pp. 11–24, 2018.
  6. R. Gouribhatla and S. S. Pulugurtha, “Drivers’ behavior when driving vehicles with or without advanced driver assistance systems: A driver simulator-based study,” Transportation research interdisciplinary perspectives, vol. 13, p. 100545, 2022.
  7. A. Moujahid, M. E. Tantaoui, M. D. Hina, A. Soukane, A. Ortalda, A. ElKhadimi, and A. Ramdane-Cherif, “Machine learning techniques in adas: A review,” in 2018 International Conference on Advances in Computing and Communication Engineering (ICACCE). IEEE, 2018, pp. 235–242.
  8. D. Feng, C. Haase-Sch utz, L. Rosenbaum, H. Hertlein, C. Glaeser, F. Timm, W. Wiesbeck, and K. Dietmayer, “Deep multi-modal object detection and semantic segmentation for autonomous driving: Datasets, methods, and challenges,” IEEE Transactions on Intelligent Transportation Systems, vol. 22, no. 3, pp. 1341–1360, 2020.
  9. E. Yurtsever, J. Lambert, A. Carballo, and K. Takeda, “A survey of autonomous driving: Common practices and emerging technologies,” IEEE access, vol. 8, pp. 58 443–58 469, 2020.
  10. S. Mozaffari, O. Y. Al-Jarrah, M. Dianati, P. Jennings, and A. Mouzakitis, “Deep learning-based vehicle behavior prediction for autonomous driving applications: A review,” IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 1, pp. 33–47, 2020.
  11. F. Leon and M. Gavrilescu, “A review of tracking and trajectory prediction methods for autonomous driving,” Mathematics, vol. 9, no. 6, p. 660, 2021.
  12. D. J. Yeong, G. Velasco-Hernandez, J. Barry, and J. Walsh, “Sensor and sensor fusion technology in autonomous vehicles: A review,” Sensors, vol. 21, no. 6, p. 2140, 2021.
  13. A. Gupta, A. Anpalagan, L. Guan, and A. S. Khwaja, “Deep learning for object detection and scene perception in self-driving cars: Survey, challenges, and open issues,” Array, vol. 10, p. 100057, 2021.
  14. B. Kitchenham, “Procedures for performing systematic reviews,” Keele, UK, Keele University, vol. 33, no. 2004, pp. 1–26, 2004.
  15. Z. Zhou, Z. Wu, R. Boutteau, F. Yang, C. Demonceaux, and D. Ginhac, “Rgb-event fusion for moving object detection in autonomous driving,” in 2023 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2023, pp. 7808–7815.
  16. Y. Zhang, K. Liu, H. Bao, Y. Zheng, and Y. Yang, “Pmpf: Point-cloud multiplepixel fusion-based 3d object detection for autonomous driving,” Remote Sensing, vol. 15, no. 6, p. 1580, 2023.
  17. Khoshkangini, Reza, Peyman Mashhadi, Daniel Tegnered, Jens Lundström, and Thorsteinn Rögnvaldsson. ”Predicting Vehicle Behavior Using Multi-task Ensemble Learning.” Expert systems with applications 212 (2023): 118716.
  18. Chen, Yi-Nan, Hang Dai, and Yong Ding. ”Pseudo-stereo for monocular 3d object detection in autonomous driving.” In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 887-897. 2022.
  19. El Ahmar, Wassim A., Dhanvin Kolhatkar, Farzan Erlik Nowruzi, Hamzah AlGhamdi, Jonathan Hou, and Robert Laganiere. ”Multiple Object Detection and Tracking in the Thermal Spectrum.” In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 277-285. 2022.
  20. Ding, Meng, Wen-Hua Chen, and Yun-Feng Cao. ”Thermal infrared single pedestrian tracking for advanced driver assistance system.” IEEE Transactions on Intelligent Vehicles 8, no. 1 (2022): 814-824.
  21. J. Liu, L. Bai, Y. Xia, T. Huang, B. Zhu, and Q.-L. Han, “Gnn-pmb: A simple but effective online 3d multi-object tracker without bells and whistles,” IEEE Transactions on Intelligent Vehicles, vol. 8, no. 2, pp. 1176–1189, 2022.
  22. Li, Linhui, Xin Sui, Jing Lian, Fengning Yu, and Yafu Zhou. ”Vehicle interaction behavior prediction with self-attention.” Sensors 22, no. 2 (2022): 429.
  23. Chen, Dian, and Philipp Krähenbühl. ”Learning from all vehicles.” In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 17222-17231. 2022.
  24. Fourie, Christiaan M., and Hermanus Carel Myburgh. ”An intra-vehicular wireless multimedia sensor network for smartphone-based low-cost advanced driver-assistance systems.” Sensors 22, no. 8 (2022): 3026.
  25. Paek, Dong-Hee, Seung-Hyun Kong, and Kevin Tirta Wijaya. ”K-Radar: 4D radar object detection for autonomous driving in various weather conditions.” Advances in Neural Information Processing Systems 35 (2022): 3819-3829.
  26. Li, Kaican, Kai Chen, Haoyu Wang, Lanqing Hong, Chaoqiang Ye, Jianhua Han, Yukuai Chen et al. ”Coda: A real-world road corner case dataset for object detection in autonomous driving.” In European Conference on Computer Vision, pp. 406-423. Cham: Springer Nature Switzerland, 2022.
  27. Li, Yiming, Dekun Ma, Ziyan An, Zixun Wang, Yiqi Zhong, Siheng Chen, and Chen Feng. ”V2X-Sim: Multi-agent collaborative perception dataset and benchmark for autonomous driving.” IEEE Robotics and Automation Letters 7, no. 4 (2022): 10914-10921.
  28. Cai, Yingfeng, Tianyu Luan, Hongbo Gao, Hai Wang, Long Chen, Yicheng Li, Miguel Angel Sotelo, and Zhixiong Li. ”YOLOv4-5D: An effective and efficient object detector for autonomous driving.” IEEE Transactions on Instrumentation and Measurement 70 (2021): 1-13.
  29. Shi, Yuguang, Yu Guo, Zhenqiang Mi, and Xinjie Li. ”Stereo CenterNet-based 3D object detection for autonomous driving.” Neurocomputing 471 (2022): 219-229.
  30. Luo, Chenxu, Xiaodong Yang, and Alan Yuille. ”Exploring simple 3d multiobject tracking for autonomous driving.” In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 10488-10497. 2021.
  31. Smirnov, Nikita, Yuzhou Liu, Aso Validi, Walter Morales-Alvarez, and Cristina Olaverri-Monreal. ”A game theory-based approach for modeling autonomous vehicle behavior in congested, urban lane-changing scenarios.” Sensors 21, no. 4 (2021): 1523.
  32. Ettinger, Scott, Shuyang Cheng, Benjamin Caine, Chenxi Liu, Hang Zhao, Sabeek Pradhan, Yuning Chai et al. ”Large scale interactive motion forecasting for autonomous driving: The waymo open motion dataset.” In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9710-9719. 2021.
  33. Mao, Jiageng, Minzhe Niu, Chenhan Jiang, Hanxue Liang, Jingheng Chen, Xiaodan Liang, Yamin Li et al. ”One million scenes for autonomous driving: Once dataset.” arXiv preprint arXiv:2106.11037 (2021).
  34. Xiao, Pengchuan, Zhenlei Shao, Steven Hao, Zishuo Zhang, Xiaolin Chai, Judy Jiao, Zesong Li et al. ”Pandaset: Advanced sensor suite dataset for autonomous driving.” In 2021 IEEE International Intelligent Transportation Systems Conference (ITSC), pp. 3095-3101. IEEE, 2021.
  35. Alkhorshid, Y.; et al.: Camera-Based Lane Marking Detection for ADAS and Autonomous Driving, International Conference Image Analysis and Recognition – ICIAR 2015, Springer, doi:10.1007/978-3-319-20801-557
  36. A. Petrovskaya and S. Thrun, “Model based vehicle detection and tracking for autonomous urban driving,” Autonomous Robots, vol. 26, no. 2–3, pp. 123–139, Apr. 2009, doi: https://doi.org/10.1007/s10514-009-9115-1.
  37. R. Szeliski, COMPUTER VISION: algorithms and applications. S.L.: Springer Nature, 2020.
  38. Zeng, W.: Microsoft Kinect Sensor and Its Effect, IEEE Multimedia, Univ. of Missouri, 2012.
  39. X. Mao, D. Inoue, S. Kato, and M. Kagami, “Amplitude-Modulated Laser Radar for Range and Speed Measurement in Car Applications,” IEEE Transactions on Intelligent Transportation Systems, vol. 13, no. 1, pp. 408–413, Mar. 2012, doi: https://doi.org/10.1109/tits.2011.2162627.
  40. S. Tokoro, K. Kuroda, A. Kawakubo, K. Fujita, and H. Fujinami, “Electronically scanned millimeter-wave radar for pre-crash safety and adaptive cruise control system,” in Proc. IEEE Intell. Veh. Symp., Jun. 2003, pp. 304–309.
  41. Min, K.; Choi, J.: Vehicle Positioning Technology Using Infra-based Laser Scanner Sensors for Autonomous Driving Service, Computer Science and Convergence CSA 2011 & WCC 2011 Proceedings, Springer, ISBN: 978-94- 007-2791-5
  42. J. Levinson, J. Askeland, J. Becker, J. Dolson, D. Held, S. Kammel, J. Kolter, D. Langer, O. Pink, V. Pratt, M. Sokolsky, G. Stanek, D. Stavens, A. Teichman, M. Werling, and S. Thrun, “Towards fully autonomous driving: Systems and algorithms,” in Proc. IEEE IV, Jun. 2011, pp. 163–168.
  43. S. Sato, M. Hashimoto, M. Takita, K. Takagi, and T. Ogawa, “Multilayer lidar-based pedestrian tracking in urban environments,” in Proc. IEEE IV, Jun. 2010, pp. 849–854.
  44. Chengjian Feng, Yujie Zhong, Yu Gao, Matthew R Scott, and Weilin Huang.Tood:Task-alignedone-stageobjectdetection.In2021IEEE/CVFInternationalConference on Computer Vision (ICCV), pages 3490–3499. IEEE Computer Society, 2021.
  45. Haoyang Zhang, Ying Wang, Feras Dayoub, and Niko Sunderhauf. Varifocalnet: An iou-aware dense object detector. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8514–8523, 2021.
Index Terms

Computer Science
Information Sciences

Keywords

Advanced Driver Assistance System Road Safety Machine Learning Deep Learning