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...

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Main Authors: Abu Jafar, Md Muzahid, Md. Abdur, Rahim, Saydul Akbar, Murad, Syafiq Fauzi, Kamarulzaman, Md Arafatur, Rahman
Format: Conference or Workshop Item
Language:English
Published: IEEE 2021
Subjects:
Online Access: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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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.
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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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