Driver Behavior Modeling via Inverse Reinforcement Learning Based on Particle Swarm Optimization
| Autor: | Zeng-Jie Liu, Huai-Ning Wu |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Mathematical optimization
Computer simulation Computer science Iterative method 010401 analytical chemistry Particle swarm optimization 02 engineering and technology 021001 nanoscience & nanotechnology Optimal control 01 natural sciences 0104 chemical sciences Vehicle dynamics Quadratic equation Inverse reinforcement learning 0210 nano-technology |
| Zdroj: | 2020 Chinese Automation Congress (CAC). |
| DOI: | 10.1109/cac51589.2020.9327174 |
| Popis: | In this paper, an inverse reinforcement learning method based on particle swarm optimization (PSO) is proposed to model driver’s steering behavior. Initially, the vehicle dynamics is represented by a Takagi-Sugeno (T-S) fuzzy model which provides a method of approximating Q-function. Then the driver behavior model is described as an optimal control policy with decision-making model which illustrates the driving style. Subsequently, the Q-function is approximated by a quadratic polynomial-in-memberships form and the PSO algorithm is used to obtain the decision-making model from the driving data. And the corresponding optimal control policy is obtained by using the Q-learning policy iteration method. Finally, a numerical simulation is carried to show the effectiveness of the proposed method. |
| Databáze: | OpenAIRE |
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