避撞预警与控制是汽车主动安全研究的重要内容，对多车纵横向交互过程中的碰撞风险进行连续估计，构建对周边车辆总体安全性影响最小的主动避撞控制策略是研究难点。首先考虑纵向与横向耦合行车场景，通过研究在不同行车区域内车辆的加速度概率分布规律，将车辆碰撞判别条件采用全域连续型碰撞概率模型进行量化表征，实现对复杂动态交通场景下车辆碰撞概率的连续表达；分析人-车-路-环境系统状态因素对碰撞事故严重程度的作用机理，设计考虑车辆碰撞概率和事故严重程度两类特征的车辆碰撞风险指标，实现对不同车辆空间分布形式下碰撞总风险的有效度量；以行车域内多车间碰撞总风险最小化为优化目标，结合车辆动力学与运动学约束条件，根据模型预测控制原理对避撞算法进行建模和求解，构建基于制动与转向协同的主动避撞策略，通过仿真和实车实验进行有效性验证。为研究复杂动态场景下的车辆主动安全智能决策方法提供思路，进一步推进智能汽车安全技术的应用。

Early warning and collision avoidance control constitute an important part of vehicle active safety research. Research difficulties lie in the continuous estimation of collision risk during longitudinal and lateral interaction of multiple vehicles, and the construction of an active collision avoidance control strategy that has the least impact on the overall safety of surrounding vehicles. First consider driving scenarios coupling longitudinal and lateral motions, by studying the acceleration probability distribution rules of vehicles in different driving areas, the vehicle collision determination conditions are quantitatively characterized by a global continuous collision probability model, which provides a continuous expression of vehicle collision probability in complex dynamic traffic scenarios. By analyzing the impact mechanism of human-vehicle-road-environment system state factors on the severity of collision outcomes, a vehicle collision risk indicator is designed taking into account two types of characteristics including vehicle collision probability and accident severity, and an effective measurement of the total risk of collision is achieved under different spatial distribution of vehicles. Taking minimizing the total collision risk of multiple vehicles within the driving domain as an optimization goal, along with constraints by vehicle dynamics and kinematic, a collision avoidance algorithm is modeled and solved based on Model Predictive Control theory, with an active collision avoidance control strategy proposed based on braking and steering coordination. Finally, the control strategy is verified by simulation tests and vehicle road experiments. Results provide insight into the research of intelligent decision-making strategies for vehicle active safety in complex dynamic scenarios, and further helps promoting the real-world application of safety technologies for intelligent vehicles.