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International Journal of Automotive Technology > Volume 23(3); 2022 > Article
International Journal of Automotive Technology 2022;23(3): 613-622.
doi: https://doi.org/10.1007/s12239-022-0056-4
Alireza Rafiei 1, Amirhossein Oliaei Fasakhodi 1, Farshid Hajati 2
1University of Tehran
2Victoria University Sydney
PDF Links Corresponding Author.  Farshid Hajati  , Email. farshid.hajati@vu.edu.au
The use of intelligent systems to prevent accidents and safety enhancement in vehicles is becoming a requirement. Besides, the development of autonomous cars is progressing every day. One of the main challenges in transportation is the high mortality rate of vehicles colliding with pedestrians. This issue becomes severe due to various and abnormal situations. This paper proposes a new intelligent algorithm for pedestrian collision avoidance based on deep reinforcement learning. A deep Q-network (DQN) is designed to discover an optimal driving policy for pedestrian collision avoidance in diverse environments and conditions. The algorithm interacts with the vehicle and the pedestrian agents and uses a specific reward function to train the model. We have used Car Learning to Act (CARLA), an open-source autonomous driving simulator, for training and verifying the model in various conditions. Applying the proposed algorithm to a simulated environment reduces vehicles and pedestrians’ collision by about 64 %, depending on the environment. Our findings offer an early-warning solution to mitigate the risk of a crash of vehicles and pedestrians in the real world.
Key Words: Pedestrian collision avoidance, Autonomous driving, Deep reinforcement learning, Car Learning to Act (CARLA), Deep Q- Network (DQN)
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