Trajectory Planning and Safety Assessment of Autonomous Vehicles Based on Motion Prediction and Model Predictive Control
Trajectory Planning and Safety Assessment of Autonomous Vehicles Based on Motion Prediction and Model Predictive Control
复制标题
基于运动预测和模型预测控制的自动驾驶车辆轨迹规划和安全评估
DOI：
10.1109/tvt.2019.2930684
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发表时间：
2019-07
影响因子：
6.8
通讯作者：
Luo Xiaoyuan
中科院分区：
文献类型：
--
作者：
Wang Yijing;Liu Zhengxuan;Zuo Zhiqiang;Li Zheng;Wang Li;Luo Xiaoyuan
安全问题是自动驾驶汽车的根本问题。轨迹规划是自动驾驶车辆系统的重要组成部分，它直接影响到自动驾驶车辆的交通安全。本文考虑了其他交通参与者的运动预测。我们使用蒙特卡罗模拟来预测物体的概率占用，并给出从概率统计到实际场景的映射。通过高清晰地图和车道检测，可以获得非基于时间的参考轨迹。然后根据自动驾驶车辆的当前状态，利用模型预测控制优化参考轨迹。采用不同的预测视界和坐标变换对规划进行优化。通过这样做，约束条件可以很容易地参与，结果更直观。离线计算其他交通参与者的占用概率，然后将得到的结果用于实时应用。因此，降低了实时计算负担。提出了碰撞概率，验证了安全评估模块中实时轨迹的可行性。分析了两种典型场景：直道变道和路口转弯。仿真结果表明了该方法的有效性。
Security problem is a fundamental issue for autonomous vehicles. Trajectory planning is a significant component of autonomous vehicle system, which directly influences the automated traffic safety. In this paper, the motion prediction of other traffic participants is considered. We use Monte Carlo simulation to predict the probabilistic occupancy of the object and give a map from probability statistics to actual scenarios. The non-time-based reference trajectory can be obtained by using high-definition map and lane detection. Then model predictive control is utilized to optimize the reference trajectory according to the current state of autonomous vehicle. Different prediction horizons and coordinate transformation are adopted to optimize the planning. By doing so, the constraint conditions can be easily involved and the result is more intuitive. The probabilistic occupancy of other traffic participants are computed offline and then the obtained results are used in real-time application. Therefore, the real-time computational burden is reduced. The crash probability is put forward to verify the feasibility of real-time trajectory in safety assessment module. Two typical scenarios are analyzed: lane change on the straight road and turning at the intersection. The simulation results illustrate the efficiency of our method.
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10.1109/tits.2016.2604240
发表时间：
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影响因子：
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发表时间：
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影响因子：
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