Gearshifting Strategy for Robot-driven Vehicles Based on Deep Reinforcement Learning

被引：0
|
作者：
Zhou N. [1 ]
Chen G. [1 ]
机构：
[1] School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing
来源：
Qiche Gongcheng/Automotive Engineering | 2020年 / 42卷 / 11期
关键词：
Deep reinforcement learning; Gearshifting strategy; Neural network; Robot driver;
D O I：
10.19562/j.chinasae.qcgc.2020.11.004
中图分类号：
学科分类号：
摘要：
In order to improve the power performance of the robot-driven vehicle, a gearshifting strategy for robot-driven vehicles in favour of power performanceis proposed based on deep neural network reinforcement learning. Firstly the vehicle longitudinal dynamics model, the kinematics model of the robot driver and the reinforcement learning model of the gearshifting strategy for the robot-driven vehicles described with Markov process are constructed, and the state space, the action space and the reward and punishment mechanism are designed with the vehicle speed, acceleration and throttle opening as the state variables and the gear position as the action variable. Then the gearshifting strategy for the robot-driven vehicles in favour of power performance is solved by using deep neural network reinforcement learning algorithm. The comparison between the simulation and test results with the strategy proposed in this paper and those with other strategyie verifies the effectiveness of the proposed strategy. © 2020, Society of Automotive Engineers of China. All right reserved.
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页码：1473 / 1481
页数：8
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