基于红外测距的道路前方车辆探测预警系统研究

进行道路前方车辆探测预警系统设计时，通常采用红外测距仪来获取道路前方车距信息，并以此作为前车探测的基础数据。为了消除系统状态误差和测量误差对车距信息数据精度的影响，可根据车距信息和相对车速不会突变的特性建立预测模型，基于此预测模型，应用Kalman滤波理论准确预测相对车速，并利用车距信息和相对车速计算安全距离报警阈值。试验证明该探测及预警方法可大大提高车辆探测的准确性和鲁棒性。

Research on Detecting and Pre-warning System for Vehicles Ahead of Road Based on Infrared Distance Measurement

In design of detecting and pre-warning system for vehicles ahead of road, usually infrared distance meter is adopted to acquire information on vehicle distance ahead of road as basic data for detection of front cars. In order to eliminate influences of system status error and measurement error on accuracy of distance information data, a prediction model can be established according to the characteristic that distance information and relative speed will not change suddenly, and based on this prediction model, this paper applies Kalman filter theory to predict relative speed accurately, and calculates alarm threshold of safety distance by means of distance information and relative speed. The test proves that this detecting and pre-warning method can greatly improve accuracy and robustness of vehicle detection.