Multi-vehicle responsive longitudinal and lateral cooperative control method for networked autonomous driving
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摘要: 为了研究网联环境下的多车协同控制，提高道路通行效率，提出了网联自动驾驶的分层协同控制框架；将网联自动驾驶协同控制分为上下两层，首先针对交通管理层确定了纵向行驶策略，采用微分博弈控制网联队列的纵向运动，以优化队列中车辆的运行状态；其次针对车辆控制层中转向机构的控制问题，提出了考虑前馈的反馈线性二次调节器(LQR)，并在此基础上使用人工势场法动态调节LQR参数矩阵，以处理不同距离的障碍物，提高车辆横向控制的精确性；然后针对网联自动驾驶车辆(CAV)汇入队列的安全问题，以虚拟队列为基础，考虑车身尺寸和临界碰撞约束来控制车辆，确保CAV汇入的安全性; 最终使用CarSim/Simulink搭建汇入场景进行仿真试验，分析验证多车纵横向协同控制可行性和有效性。仿真结果表明：微分博弈应用速度为16.67 m·s-1时，车辆队列内间距大于20 m，而22.22 m·s-1时车辆队列内间距小于40 m，证明该策略在保证安全车距的前提下提高了通行效率；与传统LQR相比，考虑势场强度的LQR在22.22 m·s-1的速度下横向误差与航向误差分别降低了6.19%、7.66%。针对网联环境下多车响应的自动驾驶协同控制问题，提出的纵横向分层协同控制框架在满足车辆队列纵向安全控制和稳定性，以及提高车辆横向控制精确性的基础上，实现了CAV汇入网联队列的多车协同和安全运行。Abstract: In order to study multi-vehicle cooperative control in connected environments and improve road traffic efficiency, a hierarchical cooperative control framework for connected autonomous driving was proposed. The framework divided cooperative control for connected autonomous driving into upper and lower layers. At the traffic management layer, the longitudinal driving strategy was determined. A differential game approach was used to control the longitudinal motion of connected vehicle platoons, aiming to optimize vehicle states within the platoon. At the vehicle control layer, to address steering control, a feedback linear quadratic regulator (LQR) considering feed-forward was proposed. Based on this, the LQR parameter matrix was dynamically adjusted using the artificial potential field method to handle obstacles at different distances and improve the vehicle's lateral control accuracy. For the safety of connected autonomous vehicles (CAV) merging into platoons, a virtual platoon was adopted as the basis, by incorporating vehicle dimensions and critical collision constraints to control the vehicle, thereby ensuring safe CAV merging behavior. CarSim/Simulink was used to build a merging scenario for simulation experiments to analyze and verify the feasibility and effectiveness of the proposed longitudinal and lateral cooperative control method for multiple vehicles. Simulation results show that the spacing within the platoon is more than 20 m when the differential game approach is applied at a speed of 16.67 m·s-1, while the spacing within the platoon is below 40 m at 22.22 m·s-1, indicating that the strategy improves traffic efficiency while ensuring safe distances between vehicles. Compared with the traditional LQR, the LQR that considers potential field strength reduces lateral error and heading error by 6.19% and 7.66%, respectively, at a speed of 22.22 m·s-1. Therefore, the proposed hierarchical longitudinal and lateral cooperative control framework achieves multi-vehicle cooperative and safe operation of CAVs merging into connected platoons, while ensuring longitudinal safety control and stability as well as improving lateral control accuracy.
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图 4 汇入车辆与周围车辆运动关系
Figure 4. Relationship between incoming and outgoing vehicle movements
图 10 在16.67 m·s-1速度下车辆队列纵向控制结果
Figure 10. Results of longitudinal control of vehicle platoon at a speed of 16.67 m·s-1
图 11 在16.67 m·s-1速度下车辆队列的速度和加速度
Figure 11. Vehicle platoon speed and acceleration at a speed of 16.67 m·s-1
图 12 16.67 m·s-1速度下车辆队列的车头间距与跟驰时距偏差
Figure 12. Vehicle head spacing and following distance deviation in a vehicle platoon at a speed of 16.67 m·s-1
图 13 16.67 m·s-1速度下车辆横向跟踪轨迹
Figure 13. Vehicle lateral tracking trajectory at a speed of 16.67 m·s-1
图 14 16.67 m·s-1速度下车辆控制量与航向角
Figure 14. Vehicle control variables and heading angle at a speed of 16.67 m·s-1
图 15 16.67 m·s-1速度下车辆控制误差信息
Figure 15. Vehicle control error information at a speed of 16.67 m·s-1
图 16 16.67 m·s-1速度下车辆汇入三维轨迹
Figure 16. Vehicle merging into a 3D trajectory at a speed of 16.67 m·s-1
图 17 22.22 m·s-1速度下车辆队列纵向控制结果
Figure 17. Results of longitudinal control of vehicle platoon at a speed of 22.22 m·s-1
图 18 22.22 m·s-1速度下车辆队列的速度和加速度
Figure 18. Vehicle platoon speed and acceleration at a speed of 22.22 m·s-1
图 19 22.22 m·s-1速度下车辆队列的车头间距与跟驰时距偏差
Figure 19. Vehicle head spacing and following distance deviation in a vehicle platoon at a speed of 22.22 m·s-1
图 20 22.22 m·s-1速度下车辆横向跟踪轨迹
Figure 20. Vehicle lateral tracking trajectory at a speed of 22.22 m·s-1
图 21 22.22 m·s-1速度下车辆控制量与航向角
Figure 21. Vehicle control variables and heading angle at a speed of 22.22 m·s-1
图 22 22.22 m·s-1速度下车辆控制误差信息
Figure 22. Vehicle control error information at a speed of 22.22 m·s-1
图 23 22.22 m·s-1速度下车辆汇入三维轨迹
Figure 23. Vehicle merging into a 3D trajectory at a speed of 22.22 m·s-1
表 1 车辆模型参数
Table 1. Parameters of vehicle model
车辆参数 参数数值 车辆质量/kg 2 020 前轴到质心的距离/m 1.265 后轴到质心的距离/m 1.682 转动惯量/(kg·m2) 4 095 轮胎有效滚动半径/m 0.353 前轮侧偏刚度/(N·rad-1) -175 016 后轮侧偏刚度/(N·rad-1) -130 634 
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