基于工况识别的安全距离模型

针对传统自适应巡航控制系统安全距离模型缺乏对车辆驾驶工况变化考虑的问题，提出一种基于驾驶工况识别的安全距离模型。依据城市驾驶工况特点构建了4种典型城市工况，引入人工神经网络对车辆实时驾驶工况进行识别与预测，然后以现有安全距离模型为基础，结合工况识别结果，完成对该模型的优化。通过CarSim和Simulink联合仿真验证，结果表明：基于驾驶工况识别的安全距离模型可以更好地实现与前车的速度跟随与距离控制，提高了乘坐安全性与道路利用率。

Research on Safety Distance Model Based on Driving Condition Recognition

In view of the lack of consideration for the vehicle driving condition changes in traditional adaptive cruise control system, this paper proposed a safety distance model based on driving condition recognition. According to the characteristics of city driving conditions, four typical city conditions were constructed. The artificial neural network was introduced to identify and predict the real-time driving conditions of the vehicle, then the model was optimized by the existing safety distance model and the recognition results of the driving conditions. Through the joint simulation of CarSim and Simulink, the results show that the safety distance model based on driving condition recognition can better achieve speed following and distance control with the preceding vehicle, and improve ride safety and road utilization.