Vehicle Lateral Collision Warning Based on Grey Prediction and Kalman Filter
Vehicle collision warning is the core technology of vehicle active safety control.It is often necessary to use accurate vehicle state information to precisely predict the motion trajectory,and then determine whether there is a collision risk within the safe collision warning time by the motion trajectory prediction module.To improve the accuracy of collision warning,a state estimation method using constant turning rate and acceleration model as the state transition equation,and square root volume Kalman filter as the estimation algorithm is established first,which is beneficial to improving the estimation accuracy of the relative motion state of the target vehicle.A relative motion trajectory prediction method integrated with grey prediction is then proposed.The measured variables are predicted in multiple steps through the grey prediction model and are corrected by the square root volume Kalman filter to improve the prediction accuracy of the relative motion trajectory of the target vehicle.Considering the influence of road adhesion coefficient on the safe collision warning time,a vehicle lateral collision warning method is proposed at last.The numerical simulation results show that the warning methods proposed can accurately predict the collision time of the vehicle under different road conditions,and the early warning time is greater than the safe collision warning time,so as to ensure that the driver or the active obstacle avoidance control system can control the vehicle timely and improve the safety of vehicle driving.
万方数据
维普
