多控制模型嵌入的智能汽车防撞虚拟仿真

仝秋红; 柴国庆; 赵华东; 高越; 张勇; 任锦涛; 冯明明

doi:10.19818/j.cnki.1671-1637.2022.01.023

多控制模型嵌入的智能汽车防撞虚拟仿真

doi: 10.19818/j.cnki.1671-1637.2022.01.023

长安大学汽车学院，陕西西安 710064

基金项目:

国家重点研发计划 2017YFC0803903

国家重点研发计划 2019YFB1600502

国家虚拟仿真实验教学项目 300103691901

详细信息

作者简介:
仝秋红(1963-)，女，陕西西安人，长安大学教授，工学博士，从事新能源智能汽车、无人驾驶和智能交通研究

中图分类号: U469.722
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出版历程
- 收稿日期: 2021-09-03
- 刊出日期: 2022-02-25

Collision avoidance virtual simulation of intelligent vehicle embedded with multiple control models

School of Automobile, Chang'an University, Xi'an 710064, Shaanxi, China

Funds:

National Key Research and Development Program of China 2017YFC0803903

National Key Research and Development Program of China 2019YFB1600502

National Virtual Simulation Experiment Teaching Project 300103691901

More Information

Author Bio:
TONG Qiu-hong(1963-), female, professor, PhD, tongqh5063@163.com

摘要

摘要: 针对智能汽车行驶安全距离监测与防撞试验高成本、高危险性以及试验结果难以观察的问题，研究了智能汽车安全距离监测与防撞的虚拟仿真；应用Visual Studio 2015、3Dmax、Unity3D等虚拟仿真技术在虚拟制动控制系统中进行了可嵌入多控制模型的自动驾驶汽车行车安全距离监测和防撞虚拟仿真试验，测试了不同制动模型的防撞应用效果；以电动汽车动力性能和制动力学特征为基础建立了虚拟整车模型及其制动系统模型，根据路面附着系数和不同道路材质建立了虚拟试验道路模型和试验场景等虚拟环境，开发了试验电子设备仿真模型，实现了多虚拟控制器嵌入，研究了基础模型和反向传播(BP)神经网络模型的嵌入与仿真效果；通过设计接口将虚拟软硬件设计效果与仿真试验过程相关联，并采用动画渲染直观展现，同时应用内存优化实现了在网络版访问服务器中进行虚拟仿真试验；通过实车试验验证了仿真系统，并对比了实车试验数据与2种模型的仿真数据。研究结果表明：在低速情况下，基础模型计算的安全距离与实车试验所测安全距离的相对误差为2.49%，BP神经网络模型预测的安全距离与实车试验所测安全距离的相对误差为2.07%；在高速情况下，因为传感器不稳定的原因，基础模型计算的安全距离与实车试验所测安全距离的相对误差为10.03%，BP神经网络模型预测的安全距离与实车试验所测安全距离的相对误差为10.35%。由此可见，该仿真系统可使高风险的碰撞试验在虚拟环境下完成。
- 智能汽车 /
- 虚拟仿真 /
- 防撞 /
- 安全距离 /
- 制动系统 /
- 附着系数 /
- BP神经网络
Abstract: In view of the high cost and risk involved in the driving safety distance monitoring and collision avoidance tests of intelligent vehicles and the difficulty in visualizing the test results, virtual simulations were conducted for the safety distance monitoring and collision avoidance of an intelligent vehicle. The virtual simulation technology based on the Visual Studio 2015, 3Dmax, and Unity3D was used to conduct the driving safety distance monitoring and collision avoidance virtual simulation tests on an autonomous car equipped with multiple control models in the virtual braking control system. The collision avoidance effects of different braking models were tested. Models for a virtual full vehicle and its braking system were established based on the dynamic performance and braking dynamics characteristics of electric vehicles. A virtual environment including a virtual test road model and a test scene was established based on the pavement adhesion coefficient and different road materials. Simulation models were developed for the test electronic devices to realize the multiple virtual controllers embedding. The embedding and simulation effects of the basic model and a back propagation (BP) neural network were studied. The design effects of the virtual software and hardware were correlated with the simulation test process through the design interface, and the animation rendering was used to directly display visually. Meanwhile, the memory optimization was applied to enable the virtual simulation test to be conducted in the server through the web access. Actual vehicle tests were conducted to verify the simulation system, and the actual vehicle test data were compared with the simulation results using the two models. Research results indicate that, at a low velocity, the relative error between the safety distance calculated by the basic model and that measured by the actual vehicle test is 2.49%, while the relative error between the safety distance calculated by the BP neural network and that measured by the actual vehicle test is 2.07%. At a high velocity, because of the sensor instability, the relative error between the safety distance calculated by the basic model and that measured in the actual vehicle test is 10.03%, while the relative error between the safety distance calculated by the BP neural network and that measured in the actual vehicle test is 10.35%. Therefore, the simulation system can enable a high-risk collision test to be conducted in a virtual environment. 6 tabs, 16 figs, 32 refs.
- intelligent vehicle /
- virtual simulation /
- collision avoidance /
- safety distance /
- braking system /
- adhesion coefficient /
- BP neural network

HTML全文

图 1 SDMCA硬件系统架构

Figure 1. SDMCA hardware system architecture

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图 2 SDMCAVS系统架构

Figure 2. System architecture for SDMCAVS

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图 3 小型电动乘用车和道路模型

Figure 3. Small electric passenger vehicle and road models

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图 4 制动力与制动踏板力的关系曲线

Figure 4. Curves of relationship between brake force and brake pedal force

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图 5 部分仿真界面

Figure 5. Partial simulation interfaces

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图 6 汽车的制动过程

Figure 6. Automobile braking process

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图 7 BP神经网络结构

Figure 7. BP neural network structure

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图 8 BP神经网络训练过程

Figure 8. BP neural network training process

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图 9 训练好的BP神经网络预测结果

Figure 9. Prediction results of trained BP neural networks

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图 10 部分场景、用车和传感器安装

Figure 10. Some experimental scenarios, vehicles and installation of sensors

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图 11 虚拟仿真试验设备安装

Figure 11. Installation of experimental equipment during virtual simulation

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图 12 观察试验前视

Figure 12. Foresight of observation experiment

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图 13 观察试验俯视

Figure 13. Top view of observation experiment

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图 14 观察试验侧视

Figure 14. Side view of observation experiment

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图 15 不同车速下实车与虚拟仿真模型的安全距离

Figure 15. Safety distances of real vehicle and virtual simulation model at different vehicle speeds

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图 16 两种虚拟仿真模型的安全距离与现实的相对误差

Figure 16. Relative errors of safety distances between two virtual simulation models and reality

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表 1 车轮碰撞器属性

Table 1. Wheel collider properties

属性名	含义
Mass	车轮质量
Radius	车轮半径
Wheel Damping Rate	车轮旋转阻尼
Suspension Distance	悬挂高度，可提高车辆稳定性，不小于0且方向垂直向下
Force App Point Distance	悬挂力作用点
Center	基于模型坐标系的车轮碰撞器中心点
Spring (Suspension Spring)	达到目标中心的弹力，越大到达中心越快(悬挂弹簧参数)
Damper (Suspension Spring)	悬浮速度阻尼，越大车轮归位耗时越长
Target Position (Suspension Spring)	悬挂中心
Extremum Slip (Forward Friction)	前向摩擦曲线滑动极值(车轮前向摩擦力)
Extremum Point (Forward Friction)	前向摩擦曲线极值点
Asymptote Slip (Forward Friction)	前向渐近线滑动值
Asymptote Point (Forward Friction)	前向曲线渐近线点
Stiffness (Forward Friction)	刚度，控制前向摩擦曲线倍数
Extremum Slip (Sideways Friction)	侧向摩擦曲线滑动极值(车轮侧向摩擦力)
Extremum Point (Sideways Friction)	侧向摩擦曲线极值点
Asymptote Slip (Sideways Friction)	侧向渐近线滑动值
Asymptote Point (Sideways Friction)	侧向曲线渐近线点
Stiffness (Sideways Friction)	刚度，控制侧向摩擦曲线倍数

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表 2 不同路面材质的附着系数

Table 2. Adhesion coefficients of different pavement materials

路面类型	状态	附着系数
路面类型	状态	高压轮胎	低压轮胎	越野轮胎
沥青或混凝土路面	干燥	0.50~0.70	0.70~0.80	0.70~0.80
	潮湿	0.35~0.45	0.45~0.55	0.50~0.60
	染污	0.25~0.45	0.25~0.40	0.25~0.45
卵石路面	干燥	0.45~0.50	0.50~0.55	0.60~0.70
碎石路面	干燥	0.50~0.60	0.60~0.70	0.60~0.70
碎石路面	潮湿	0.30~0.40	0.40~0.50	0.40~0.55
土路	干燥	0.40~0.50	0.50~0.60	0.50~0.60
	湿润	0.20~0.40	0.30~0.45	0.35~0.50
	泥泞	0.15~0.25	0.15~0.25	0.20~0.30
积雪荒土	松软	0.20~0.30	0.20~0.40	0.20~0.40
积雪荒土	压实	0.15~0.20	0.20~0.25	0.30~0.50
结冰路面	零下气温	0.08~0.15	0.10~0.20	0.05~0.10

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表 3 物理材质参数

Table 3. Physical material parameters

属性名	含义	属性名	含义
Dynamic Friction	滑动摩擦力	Static Friction	静摩擦力
Bounciness	表面弹性	Friction Combine	碰撞体摩擦力混合方式
Bounce Combine	表面弹性混合方式	Friction Direction 2	各向异向的方向
Dynamic Friction 2	作用于Friction Direction 2方向的滑动摩擦力	Static Friction 2	作用于Friction Direction 2方向的静摩擦力

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表 4 直线车道上测得的试验数据

Table 4. Experimental data measured on straight lane

车速/ (km·h^-1)	安全距离/ m	报警距离/ m	制动距离/ m	停车后距障碍物距离/m
20	12.87	12.29	7.49	4.80
20	12.87	11.00	6.60	4.40
30	20.66	21.26	12.86	8.40
30	20.66	27.00	22.40	4.60
40	31.56	24.60	17.85	6.75
40	31.56	29.00	21.55	7.45
50	38.95	40.10	30.70	9.40
50	38.95	38.00	27.90	10.10
60	49.44	52.07	40.17	11.90
60	49.44	48.00	39.60	8.40
60	49.44	49.00	40.15	8.85

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表 5 高速公路跟车数据

Table 5. Vehicle following test data on highway

车速/ (km·h^-1)	安全距离/ m	显示报警距离/m	前车大小	行驶场景	是否报警
60	49.44	15	小车	换道插入	报警
100	100.44	93	小车	平稳跟车	报警
100	100.44	92	小车	远离前车	不报警
105	107.78	98	小车	换道插入	报警
100	100.44	56	小车	接近前车	报警
100	100.44	20	小车	换道离开	报警
80	73.14	75	小车	换道离开	不报警

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表 6 仿真数据和现实数据对比

Table 6. Comparison between simulation and reality datum

车速/(km·h^-1)	现实安全距离/m	基础模型安全距离/m	BP神经网络安全距离/m	基础模型与现实的相对误差/%	BP神经网络模型与现实的相对误差/%
20	12	12.87	11.82	7.25	1.50
30	22	20.66	20.41	6.09	7.23
40	28	29.35	30.19	4.82	7.82
50	39	38.95	40.98	0.13	5.08
60	48	49.44	52.43	3.00	9.23
70	58	60.64	64.38	4.55	11.00
80	75	73.14	76.55	2.49	2.07
100	91	100.44	101.71	10.37	11.77
105	98	107.83	108.14	10.03	10.35

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