基于环境复杂度的危险品运输车辆碰撞预警策略

In order to solve the problem of higher false alarm rate in the traditional vehicle collision warning system, a concept of environmental complexity was introduced into the collision warning strategy to suit the requirements of dangerous goods transport vehicles. Analyzing the influence factors of static complexity and dynamic complexity, a calculation method of risk identification index and a specific framework of early warning strategy were proposed. On this basis, combined with the domestic open truck accident reports and the environmental information of the real transportation section, a quantitative model of environmental complexity was established taking the advantage of the neural network in nonlinear relationship fitting ability. Based on real vehicle data collected from the dangerous goods transportation vehicles with common warning system in real road section, the strategy verification test was carried out. The results show that, compared with the common collision warning system on the market, the false alarm rate of the proposed warning strategy can be reduced from 41% to 8%. The classification mechanism can better adapt to the driver's judgment of danger and braking habits, and improve the safety of dangerous goods transport vehicles.