Optimal safety planning and driving decision-making for multiple autonomous vehicles: A learning based approach.
In the early diffusion stage of autonomous vehicle systems, the controlling of vehicles through exacting decision-making to reduce the number of collisions is a major problem. This paper offers a DRL-based safety planning decision-making scheme in an emergency that leads to both the first and multip...
| Hlavní autoři: | , , , , |
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| Médium: | Conference or Workshop Item |
| Jazyk: | English |
| Vydáno: |
IEEE
2021
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| Témata: | |
| On-line přístup: | http://umpir.ump.edu.my/id/eprint/33332/1/Optimal_Safety_Planning_and_Driving_Decision-Making_for_Multiple_Autonomous_Vehicles_A_Learning_Based_Approach.pdf |
| _version_ | 1825814188565463040 |
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| author | Abu Jafar, Md Muzahid Md. Abdur, Rahim Saydul Akbar, Murad Syafiq Fauzi, Kamarulzaman Md Arafatur, Rahman |
| author_facet | Abu Jafar, Md Muzahid Md. Abdur, Rahim Saydul Akbar, Murad Syafiq Fauzi, Kamarulzaman Md Arafatur, Rahman |
| author_sort | Abu Jafar, Md Muzahid |
| collection | UMP |
| description | In the early diffusion stage of autonomous vehicle systems, the controlling of vehicles through exacting decision-making to reduce the number of collisions is a major problem. This paper offers a DRL-based safety planning decision-making scheme in an emergency that leads to both the first and multiple collisions. Firstly, the lane-changing process and braking method are thoroughly analyzed, taking into account the critical aspects of developing an autonomous driving safety scheme. Secondly, we propose a DRL strategy that specifies the optimum driving techniques. We use a multiple-goal reward system to balance the accomplishment rewards from cooperative and competitive approaches, accident severity, and passenger comfort. Thirdly, the deep deterministic policy gradient (DDPG), a basic actor-critic (AC) technique, is used to mitigate the numerous collision problems. This approach can improve the efficacy of the optimal strategy while remaining stable for ongoing control mechanisms. In an emergency, the agent car can adapt optimum driving behaviors to enhance driving safety when adequately trained strategies. Extensive simulations show our concept’s effectiveness and worth in learning efficiency, decision accuracy, and safety. |
| first_indexed | 2024-03-06T12:55:08Z |
| format | Conference or Workshop Item |
| id | UMPir33332 |
| institution | Universiti Malaysia Pahang |
| language | English |
| last_indexed | 2024-03-06T12:55:08Z |
| publishDate | 2021 |
| publisher | IEEE |
| record_format | dspace |
| spelling | UMPir333322022-09-06T02:47:20Z http://umpir.ump.edu.my/id/eprint/33332/ Optimal safety planning and driving decision-making for multiple autonomous vehicles: A learning based approach. Abu Jafar, Md Muzahid Md. Abdur, Rahim Saydul Akbar, Murad Syafiq Fauzi, Kamarulzaman Md Arafatur, Rahman TJ Mechanical engineering and machinery In the early diffusion stage of autonomous vehicle systems, the controlling of vehicles through exacting decision-making to reduce the number of collisions is a major problem. This paper offers a DRL-based safety planning decision-making scheme in an emergency that leads to both the first and multiple collisions. Firstly, the lane-changing process and braking method are thoroughly analyzed, taking into account the critical aspects of developing an autonomous driving safety scheme. Secondly, we propose a DRL strategy that specifies the optimum driving techniques. We use a multiple-goal reward system to balance the accomplishment rewards from cooperative and competitive approaches, accident severity, and passenger comfort. Thirdly, the deep deterministic policy gradient (DDPG), a basic actor-critic (AC) technique, is used to mitigate the numerous collision problems. This approach can improve the efficacy of the optimal strategy while remaining stable for ongoing control mechanisms. In an emergency, the agent car can adapt optimum driving behaviors to enhance driving safety when adequately trained strategies. Extensive simulations show our concept’s effectiveness and worth in learning efficiency, decision accuracy, and safety. IEEE 2021 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/33332/1/Optimal_Safety_Planning_and_Driving_Decision-Making_for_Multiple_Autonomous_Vehicles_A_Learning_Based_Approach.pdf Abu Jafar, Md Muzahid and Md. Abdur, Rahim and Saydul Akbar, Murad and Syafiq Fauzi, Kamarulzaman and Md Arafatur, Rahman (2021) Optimal safety planning and driving decision-making for multiple autonomous vehicles: A learning based approach. In: 2021 Emerging Technology in Computing, Communication and Electronics (ETCCE) , 21-23 Dec. 2021 , Dhaka, Bangladesh. pp. 1-6.. ISBN 978-1-6654-8364-3 (Online); 978-1-6654-8365-0(PoD) (Published) https://ieeexplore.ieee.org/abstract/document/9689820 http://10.1109/ETCCE54784.2021.9689820 |
| spellingShingle | TJ Mechanical engineering and machinery Abu Jafar, Md Muzahid Md. Abdur, Rahim Saydul Akbar, Murad Syafiq Fauzi, Kamarulzaman Md Arafatur, Rahman Optimal safety planning and driving decision-making for multiple autonomous vehicles: A learning based approach. |
| title | Optimal safety planning and driving decision-making for multiple autonomous vehicles: A learning based approach. |
| title_full | Optimal safety planning and driving decision-making for multiple autonomous vehicles: A learning based approach. |
| title_fullStr | Optimal safety planning and driving decision-making for multiple autonomous vehicles: A learning based approach. |
| title_full_unstemmed | Optimal safety planning and driving decision-making for multiple autonomous vehicles: A learning based approach. |
| title_short | Optimal safety planning and driving decision-making for multiple autonomous vehicles: A learning based approach. |
| title_sort | optimal safety planning and driving decision making for multiple autonomous vehicles a learning based approach |
| topic | TJ Mechanical engineering and machinery |
| url | http://umpir.ump.edu.my/id/eprint/33332/1/Optimal_Safety_Planning_and_Driving_Decision-Making_for_Multiple_Autonomous_Vehicles_A_Learning_Based_Approach.pdf |
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