|Table of Contents|

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

《交通运输工程学报》[ISSN:1671-1637/CN:61-1369/U]

Issue:
2022年01期
Page:
273-284
Research Field:
交通信息工程及控制
Publishing date:

Info

Title:
Collision avoidance virtual simulation of intelligent vehicle embedded with multiple control models
Author(s):
TONG Qiu-hong;  CHAI Guo-qing;  ZHAO Hua-dong;  GAO Yue;  ZHANG Yong;  REN Jin-tao;  FENG Ming-ming
(School of Automobile, Chang'an University, Xi'an 710064, Shaanxi, China)
Keywords:
intelligent vehicle;  virtual simulation;  collision avoidance;  safety distance;  braking system;  adhesion coefficient;  BP neural network
PACS:
U469.722
DOI:
10.19818/j.cnki.1671-1637.2022.01.023
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.

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Last Update: 2022-03-20