Automated driving: Safe motion planning using positively invariant sets
Automated driving: Safe motion planning using positively invariant sets
复制标题
自动驾驶：使用正不变集的安全运动规划
DOI：
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
S. D. Cairano
中科院分区：
文献类型：
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作者：
K. Berntorp;A. Weiss;C. Danielson;I. Kolmanovsky;S. D. Cairano
本文提出了一种安全换道的方法。我们利用反馈控制和约束容许的正不变集，以保证无碰撞的闭环轨迹跟踪。从初始状态的车辆和障碍物在感兴趣的区域开始，我们的方法转向车辆所需的车道，同时满足与未来的运动相对于车辆的障碍物的约束。我们使用平衡点和相关的车辆动力学的正不变集将初始状态与所需车道连接起来，其中正不变集用于保证平衡点之间的安全过渡。一个具有滚动时域实现的自动高速公路驾驶示例表明，我们的方法能够实时生成安全的动态可行轨迹，同时考虑环境中的障碍物和建模误差。
This paper develops a method for safe lane changes. We leverage feedback control and constraint-admissible positively invariant sets to guarantee collision-free closed-loop trajectory tracking. Starting from an initial state of the vehicle and obstacles in the region of interest, our method steers the vehicle to the desired lane while satisfying constraints associated with the future motion of the obstacles with respect to the vehicle. We connect the initial state with the desired lane using equilibrium points and associated positively invariant sets of the vehicle dynamics, where the positively invariant sets are used to guarantee safe transitions between the equilibrium points. An autonomous highway-driving example with a receding-horizon implementation shows that our method is capable of generating safe dynamically feasible trajectories in real-time while accounting for obstacles in the environment and modeling errors.