多控制模型嵌入的智能汽车防撞虚拟仿真

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...

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

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.