【摘要】 车辆碰撞预警是车辆主动安全控制关键技术，常需要利用准确的车辆状态信息进行运动轨迹的高精度预测，再由运动轨迹预测模块判断安全碰撞预警时间内是否存在碰撞风险。为提高碰撞预警精度，建立以恒定转弯率和加速度模型为状态转移方程，平方根容积卡尔曼滤波器为估计算法的状态估计方法，提高目标车相对运动状态的估计精度；提出融合灰色预测的车辆运动轨迹预测方法，通过灰色预测模型对量测变量进行多步预测，再由平方根容积卡尔曼滤波器进行校正，提高目标车相对运动轨迹的预测精度；最后，考虑路面附着系数对安全碰撞预警时间的影响，提出一种车辆侧向碰撞预警方法。数字仿真结果表明，在不同路面附着条件下，提出的车辆侧向碰撞预警方法都准确预测车辆的碰撞时间，提前的预警时间大于安全碰撞预警时间，确保驾驶员或主动避障控制系统能及时操控车辆，提高车辆行驶的安全性。更多还原

【Abstract】 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.更多还原