Preceding vehicle following algorithm with human driving characteristics

Author:

Pan Feng1ORCID,Bao Hong2

Affiliation:

1. College of Information Science & Technology, Beijing University of Chemical Technology, Beijing, People’s Republic of China

2. Beijing Key Laboratory of Information Service Engineering, Beijing Union University, Beijing, People’s Republic of China

Abstract

This paper proposes a new approach of using reinforcement learning (RL) to train an agent to perform the task of vehicle following with human driving characteristics. We refer to the ideal of inverse reinforcement learning to design the reward function of the RL model. The factors that need to be weighed in vehicle following were vectorized into reward vectors, and the reward function was defined as the inner product of the reward vector and weights. Driving data of human drivers was collected and analyzed to obtain the true reward function. The RL model was trained with the deterministic policy gradient algorithm because the state and action spaces are continuous. We adjusted the weight vector of the reward function so that the value vector of the RL model could continuously approach that of a human driver. After dozens of rounds of training, we selected the policy with the nearest value vector to that of a human driver and tested it in the PanoSim simulation environment. The results showed the desired performance for the task of an agent following the preceding vehicle safely and smoothly.

Funder

National Nature Science Foundation of China

Academic Human Resources Development in Beijing Union University

beijing municipal science and technology commission

Publisher

SAGE Publications

Subject

Mechanical Engineering,Aerospace Engineering

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1. Lateral Control of Autonomous Vehicles with Data Dropout via an Enhanced Data-driven Model-free Adaptive Control Algorithm;Journal of Highway and Transportation Research and Development (English Edition);2024-03

2. Research on reinforcement learning based on PPO algorithm for human-machine intervention in autonomous driving;Electronic Research Archive;2024

3. Autonomous On-ramp Merge Strategy Using Deep Reinforcement Learning in Uncertain Highway Environment;2022 IEEE International Conference on Unmanned Systems (ICUS);2022-10-28

4. Engine-in-the-Loop Analysis of the Influence of Manual Gearshift Duration on Vehicle Consumption and Emissions;SAE International Journal of Passenger Vehicle Systems;2022-09-20

5. Design and off-line tuning of a longitudinal driver model for EiL applications;Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering;2021-08-30

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