基于REINFORCE算法和神经网络的无人驾驶车辆变道控制

闫浩; 刘小珠; 石英

doi:10.3963/j.jssn.1674-4861.2021.01.0019

基于REINFORCE算法和神经网络的无人驾驶车辆变道控制

doi: 10.3963/j.jssn.1674-4861.2021.01.0019

武汉理工大学自动化学院武汉 430070

基金项目:

国家自然科学基金项目 51805388

湖北省技术创新重大项目 2019AAA025

详细信息

作者简介:
闫浩(1996—)，硕士研究生.研究方向：强化学习、无人驾驶.E-mail：675462459@qq.com

通讯作者:
石英(1975—)，博士，教授.研究方向：深度学习、无人驾驶、大数据.E-mail：a_laly@163.com

中图分类号: U461.1
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出版历程
- 收稿日期: 2020-09-25
- 刊出日期: 2021-02-28

Lane-change Control for Unmanned Vehicle Based on REINFORCE Algorithm and Neural Network

School of Automation, Wuhan University of Technology, Wuhan 430070, China

摘要

摘要: 针对无人驾驶车辆变道超车场景，研究基于REINFORCE算法和神经网络技术的无人驾驶车辆变道控制策略。通过车辆动力学模型确定模型的反馈量、控制量和输出限幅要求; 设计神经网络控制器的结构，根据REINFORCE算法设计控制器训练方案; 分析经验池数据数值和方差过大的问题，提出1种经验池数据预处理的方法以改进控制器训练方案; 结合无人驾驶车辆运行场景，分析和研究强化学习过程中产生的奖励分布稀疏问题，并针对该问题提出1种基于对数函数的奖励塑造解决方案; 与PID控制器和LQR控制器进行对比实验验证。实验结果表明，与PID相比，该控制策略有更小的最大误差，变道过程更安全; 与LQR相比，该控制策略性能表现接近，以此证明其用于无人驾驶车辆变道控制任务的可行性。此外，记录在不同平台下该控制策略的执行时间以证明其实时性和在轻量级平台运行的可行性。
- 交通控制 /
- 无人驾驶车辆 /
- 变道控制 /
- 强化学习
Abstract: For lane change and overtaking of unmanned vehicles, the paper studies the lane change control strategy of unmanned vehicles based on the REINFORCE algorithm and neural network. The feedback, control input, and output limit requirement of the vehicle dynamics model are determined. The REINFORCE algorithm is used to design the structure of the neural network controller and the training plan of the controller. For too large data value and variance of the experience pool, a preprocessing method of the experience pool data is proposed to improve the controller training plan. Besides analyzing sparse reward distribution in the reinforcement learning process, a reward shaping solution based on logarithmic function is proposed combined with the running condition of unmanned vehicles. Compared with PID and LQR controllers, the experiment is carried out. The results show that the proposed control strategy has smaller maximum error compared with PID, with a safer lane-change process. The performance of the control strategy is similar to LQR, which proves its feasibility for the lane change control task of unmanned vehicles. Also, the execution time of the control strategy in different platforms is recorded to prove its real-time performance and feasibility in lightweight platforms.
- traffic control /
- unmanned vehicle /
- lane-change control /
- reinforcement learning

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图 1 车辆单轨模型图

Figure 1. Monorail model of vehicle

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图 2 车辆变道控制系统结构图

Figure 2. Structure of vehicle lane-change control system

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图 3 强化学习过程示意图

Figure 3. Process of reinforcement learning

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图 4 “0-1”设置例1

Figure 4. "0-1"setting in case 1

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图 5 “0-1”设置例2

Figure 5. "0-1"setting in case 2

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图 6 车速为10 m/s时对照PID实验结果图

Figure 6. Experimental result compared to PID when the vehicle speed is 10 m/s

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图 7 车速为15 m/s时对照PID实验结果图

Figure 7. Experimental result compared to PID when the vehicle speed is 15 m/s

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图 8 车速为20 m/s时对照PID实验结果图

Figure 8. Experimental result compared to PID when the vehicle speed is 20 m/s

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图 9 车速为25 m/s时对照PID实验结果

Figure 9. Experimental result compared to PID when the vehicle speed is 25 m/s

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图 10 车速为10 m/s时对照LQR实验结果图

Figure 10. Experimental result compared to LQR when the vehicle speed is 10 m/s

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图 11 车速为15 m/s时对照LQR实验结果图

Figure 11. Experimental result compared to LQR when the vehicle speed is 15 m/s

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图 12 车速为20 m/s时对照LQR实验结果图

Figure 12. Experimental result compared to LQR when the vehicle speed is 20 m/s

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图 13 车速为25 m/s时对照LQR实验结果图

Figure 13. Experimental result compared to LQR when the vehicle speed is 25 m/s

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表 1 车辆固定参数表

Table 1. Fixed parameters of vehicle

固定参数	数值
s_f	0.2
s_r	0.2
a	1.232
b	1.468
C_cf	66 900
C_cr	62 700
C_lf	66 900
C_lr	62 700
m	1 723
I_z	4 175

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表 2 神经网络参数表

Table 2. Parameters of the neural network

	第1层	第2层
输入维度	5	200
输出维度	200	51
激活函数	tanh	无

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表 3 变道完成后误差和变道过程中最大误差记录表

Table 3. Errors after lane change and the maximum error during lane change

车速和控制器	变道完成后误差/m	变道过程中最大误差/m
10 m/s，REINFORCE	0.02	0.06
10 m/s，PID	0	0.17
10 m/s，LQR	0	0.02
15 m/s，REINFORCE	0.04	0.07
15 m/s，PID	0	0.17
15 m/s，LQR	0	0.05
20 m/s，REINFORCE	0.06	0.07
20 m/s，PID	0	0.17
20 m/s，LQR	0	0.12
25 m/s，REINFORCE	0.08	0.10
25 m/s，PID	0	0.17
25 m/s，LQR	0	0.19

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表 4 神经网络控制器运行时间记录表

Table 4. Running time of the neural-network controller

平台	仿真总用时/s	仿真总步数	单步平均用时/s
计算机	2.834 99	1 202	0.002 36
TX2	3.898 25	1 202	0.003 24
Jetson nano	4.859 62	1 202	0.004 04

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