考虑道路因素的智能车辆纵向运动决策与控制研究

针对智能车辆纵向运动时的交通道路适应性问题，考虑路面附着系数和前车运动速度等因素，研究了智能车辆纵向运动决策与控制方法。论文研究了基于车头时距的纵向运动决策方法并建立不同驾驶行为的目标车速模型，运用变论域模糊推理算法设计了目标加速度模型。基于纵向动力学模型，运用自适应反演滑模控制算法建立了驱动控制器和制动控制器。对高附着系数路面和低附着系数路面的行驶工况进行仿真试验验证，结果表明，在不同的附着系数路面和前车变速行驶条件下，智能车辆能实时、合理地决策目标车速、目标加速度，实现安全、高效、稳定的跟驰。

Research on Longitudinal Motion Decision and Control of Intelligent Vehicles Considering Road Conditions

To address the traffic road adaptability issues during the longitudinal motion of intelligent vehicles, considering the adhesion coefficient and the speed of the front vehicle, a decision-making method for longitudinal motion is developed based on time headway and the target speed models for different driving behaviors are established. Then the target acceleration model is designed utilizing the variable universe fuzzy reasoning algorithm. Based on the longitudinal dynamic models, the driving and braking controllers are established using the adaptive backstepping sliding mode control algorithm. Simulation experiments are carried out on both high and low adhesion coefficient roads. The results show that the intelligent vehicle can effectively determine the target speed and acceleration in real time under the conditions of varying adhesion coefficients of the road and fluctuating speeds of the front vehicle, ensuring safe, efficient and stable following of the preceding vehicle.