Left-turn motion planning model of autonomous driving based on approximate grid risk assessment of vehicle-non-motor conflict
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摘要: 为了提高自动驾驶车辆在复杂机非混行交叉口行车安全性、舒适性和效率，提出了一种基于机非冲突近似网格风险评估的自动驾驶左转运动规划模型，并进行模型泛化；设定静态离散序列交叉口网格区域的划分规则，根据多状态通行行为概率转换关系，预测非机动车在细分网格中的运动状态，并动态评估机非冲突区域的风险等级；在此基础上，采用模型预测方法设计自动驾驶车辆的横纵向控制算法，通过自适应调节航向与速度实现跟踪期望轨迹并同步规避网格冲突区域；结合车辆动力学与外部交互环境等约束条件，开发交叉口四相位信号控制交通仿真平台，采用模型在环测试的方式，从效率优度、舒适性优度、实际规划路径与参考路径的偏移量等方面，验证了对左转机非冲突区域运动规划的有效性。研究结果表明：所提出模型能够有效动态提取和预测网格风险信息，确保自动驾驶车辆与驶入交叉口非机动车的安全交互、高效通行与驾驶舒适性，其规划路径的偏移量与同类算法相比最大可降低17.1%，通行效率最大可提高26.6%，舒适性优度最大可提高39.3%，实际路径跟踪表现出高效通过交叉口机非冲突区域和规划路径占用空间低的明显优势。Abstract: In order to improve the driving safety, reliability and efficiency of autonomous vehicles at complex vehicle-non-motor mixed intersections, a left-turn motion planning model of autonomous driving based on approximate grid risk assessment of vehicle-non-motor conflict was proposed and generalized. The division rules of static discrete sequence intersection grid area were set.According to the probability conversion relationship of traffic behaviors in multi traffic states, the motion state of non-motor vehicle in the subdivision grid was predicted, and the risk level of the conflict area between motor vehicles and non-motor vehicles was dynamically evaluated. On this basis, the model prediction method was used to design the lateral and longitudinal control algorithms of autonomous vehicle, the desired trajectory could be tracked by adaptively adjusting the heading and speed, and the grid conflict area could be avoided synchronously. Combined with the constraints of vehicle dynamics and external interaction environment, a traffic simulation platform of intersection four-phase signal control was developed. From the aspects of efficiency optimization, comfort optimization, and the offset between actual planned path and reference path, the model-in-the-loop test was used to verify the effectiveness of the left-turn motion planning in the vehicle-non-motor conflict area. Research results show that the proposed model can effectively extract and predict grid risk information dynamically, and improves the safe interaction, efficient traffic and driving comfort between autonomous vehicles and surrounding non-motor vehicles. Compared with similar algorithms, the offset of the planned path can reduce by 17.1%, the traffic efficiency can increase by 26.6% and the comfort can increase by 39.3% at most. Therefore, the proposed model has obvious advantages in the efficient passage through vehicle-non-motor conflict area and low space occupation by the planned path. 2 tabs, 14 figs, 32 refs.
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图 8 场景1自动驾驶车辆与非机动车实际行驶路径
Figure 8. Actual driving paths of autonomous and non-motor vehicles in Scene 1
图 12 场景2自动驾驶车辆与非机动车实际行驶路径
Figure 12. Actual driving paths of autonomous and non-motor vehicles in Scene 2
表 1 非机动车不同状态下的速度划分
Table 1. Speed distribution in different non-motor vehicle states
状态 平均速度/ (m·s-1) 标准差 最小速度/ (m·s-1) 最大速度/ (m·s-1) 停止 0.00 0.10 0.0 0.1 低速行驶 1.74 0.55 0.4 3.4 高速行驶 5.20 0.57 3.7 6.9 
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表 2 三种非机动车离散状态下的方向变化概率
Table 2. Direction change probabilities in three discrete states of non-motor vehicles
方向变化 停止 低速行驶 高速行驶 左转 0.20 0.15 0.05 直行 0.60 0.70 0.90 右转 0.20 0.15 0.05
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