Machine Learning for Autonomous Vehicle Collision Prediction and Avoidance

Authors

  • Dr. Xiaoguang Wang Associate Professor of Electrical Engineering, Harbin Institute of Technology (HIT), China Author

Keywords:

LiDAR

Abstract

Teslas are equipped with cameras, radar, and, until last year, LiDAR, and also use software built by Tesla to navigate vehicles. The Autopilot system, advancements in their neural networks, and increased GPU performance have led to iterative updates to their driver's assistance software with a longer-term goal of full self-driving capability. Though these companies, primarily Tesla and Waymo, currently have the advantage over companies like GM, Ford, Toyota, and so on, in terms of technology, safety standards, and the quality of vehicles available, a new entrant into this industry without a well-developed technology in autonomous vehicles and machine learning could also potentially take the market lead. In this paper, we explore how machine learning models can improve collision prediction models for autonomous vehicles.

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References

S. Chen, Z. Tang, H. Wang, and Q. Luo, "Machine Learning-Based Collision Prediction for Autonomous Vehicles," IEEE Transactions on Intelligent Transportation Systems, vol. 20, no. 3, pp. 1044-1056, Mar. 2019.

Y. Zhang, J. Guo, and X. Liu, "Deep Reinforcement Learning for Collision Avoidance of Autonomous Vehicles," IEEE Transactions on Vehicular Technology, vol. 68, no. 9, pp. 8599-8610, Sept. 2019.

L. Li, X. Wang, T. Wu, and Y. Wu, "Real-Time Prediction and Avoidance of Collisions in Autonomous Driving," IEEE Transactions on Intelligent Vehicles, vol. 5, no. 4, pp. 674-684, Dec. 2020.

H. Zhao, P. Xu, and J. Li, "A Survey of Machine Learning-Based Collision Prediction and Avoidance Techniques for Autonomous Vehicles," IEEE Access, vol. 8, pp. 88204-88218, 2020.

F. Yang, Y. Wang, and M. Liu, "Integrated Machine Learning Framework for Collision Prediction and Avoidance in Autonomous Vehicles," IEEE Transactions on Intelligent Transportation Systems, vol. 21, no. 12, pp. 5233-5244, Dec. 2020.

J. Gao, L. Li, and H. Chen, "Machine Learning Approaches for Collision Prediction in Autonomous Driving," IEEE Transactions on Neural Networks and Learning Systems, vol. 32, no. 6, pp. 2490-2502, June 2021.

Tatineni, Sumanth. "Climate Change Modeling and Analysis: Leveraging Big Data for Environmental Sustainability." International Journal of Computer Engineering and Technology 11.1 (2020).

Vemori, Vamsi. "Towards Safe and Equitable Autonomous Mobility: A Multi-Layered Framework Integrating Advanced Safety Protocols, Data-Informed Road Infrastructure, and Explainable AI for Transparent Decision-Making in Self-Driving Vehicles." Human-Computer Interaction Perspectives 2.2 (2022): 10-41.

Venkataramanan, Srinivasan, Ashok Kumar Reddy Sadhu, and Mahammad Shaik. "Fortifying The Edge: A Multi-Pronged Strategy To Thwart Privacy And Security Threats In Network Access Management For Resource-Constrained And Disparate Internet Of Things (IOT) Devices." Asian Journal of Multidisciplinary Research & Review 1.1 (2020): 97-125.

Vemoori, Vamsi. "Comparative Assessment of Technological Advancements in Autonomous Vehicles, Electric Vehicles, and Hybrid Vehicles vis-à-vis Manual Vehicles: A Multi-Criteria Analysis Considering Environmental Sustainability, Economic Feasibility, and Regulatory Frameworks." Journal of Artificial Intelligence Research 1.1 (2021): 66-98.

M. Chen, Z. Yan, and J. Zhang, "Dynamic Collision Prediction and Avoidance for Autonomous Driving Using LSTM Networks," IEEE Transactions on Neural Networks and Learning Systems, vol. 33, no. 2, pp. 614-625, Feb. 2022.

X. Liu, J. Zhang, and F. Li, "Collision Prediction and Avoidance in Autonomous Vehicles: A Deep Learning Approach," IEEE Transactions on Intelligent Transportation Systems, vol. 22, no. 5, pp. 2829-2839, May 2021.

Y. Wang, F. Guo, and Z. Chen, "Machine Learning-Based Framework for Collision Avoidance in Autonomous Vehicles," IEEE Transactions on Vehicular Technology, vol. 70, no. 6, pp. 5620-5632, June 2021.

J. Li, H. Zhao, and P. Xu, "Collision Prediction and Avoidance in Autonomous Vehicles: A Survey," IEEE Access, vol. 9, pp. 112234-112248, 2021.

S. Huang, X. Wang, and M. Wang, "Multi-Modal Machine Learning for Collision Prediction and Avoidance in Autonomous Vehicles," IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 4, pp. 3486-3497, Apr. 2022.

K. Li, W. Xu, and X. Wang, "Collaborative Machine Learning for Collision Prediction and Avoidance in Autonomous Vehicle Networks," IEEE Transactions on Vehicular Technology, vol. 71, no. 7, pp. 6888-6899, July 2022.

Y. Yang, T. Li, and X. Zhang, "Real-Time Collision Prediction and Avoidance for Autonomous Vehicles Using Reinforcement Learning," IEEE Transactions on Intelligent Vehicles, vol. 7, no. 3, pp. 201-213, Sept. 2022.

C. Wu, J. Li, and S. Hu, "Hybrid Machine Learning Model for Collision Prediction and Avoidance in Autonomous Vehicles," IEEE Transactions on Neural Networks and Learning Systems, vol. 34, no. 1, pp. 112-123, Jan. 2023.

Z. Liu, Y. Zhao, and K. Zhang, "Enhanced Collision Prediction and Avoidance for Autonomous Vehicles Using Attention Mechanisms," IEEE Transactions on Intelligent Transportation Systems, vol. 24, no. 2, pp. 1492-1503, Feb. 2023.

M. Zhang, F. Liu, and Y. Huang, "Safe Path Planning for Autonomous Vehicles with Collision Prediction and Avoidance," IEEE Transactions on Vehicular Technology, vol. 72, no. 1, pp. 334-345, Jan. 2023.

X. Chen, S. Li, and H. Wu, "Machine Learning Techniques for Collision Prediction in Autonomous Vehicles," IEEE Transactions on Intelligent Vehicles, vol. 8, no. 2, pp. 234-246, June 2023.

Y. Liu, X. Zhang, and P. Xu, "Proactive Collision Avoidance in Autonomous Vehicles Using Deep Learning," IEEE Transactions on Neural Networks and Learning Systems, vol. 35, no. 3, pp. 1590-1602, Mar. 2023.

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Published

30-06-2023

How to Cite

[1]
Dr. Xiaoguang Wang, “Machine Learning for Autonomous Vehicle Collision Prediction and Avoidance”, J. of Artificial Int. Research and App., vol. 3, no. 1, pp. 1–25, Jun. 2023, Accessed: Dec. 23, 2024. [Online]. Available: https://aimlstudies.co.uk/index.php/jaira/article/view/84