使用时变势场统一无信号交叉口自动驾驶车辆的决策与轨迹规划
Unifying Decision-Making and Trajectory-Planning in Unsignalized Intersections Using Time-Varying Potential Fields
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- Dipartimento di Elettronica e Telecomunicazioni, Politecnico di Torino（电子工程与电信学院，托斯卡纳理工学院）
- Dipartimento di Automatica e Informatica, Politecnico di Torino（自动化与信息学院，托斯卡纳理工学院）
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中文总结 AI 辅助
针对无信号交叉口自动驾驶车辆，提出用有限时域最优控制问题结合时变人工势场的新框架统一决策与轨迹规划，利用短视运动预测等考虑潜在碰撞，仿真验证了该方法可生成可行安全参考轨迹。
中文摘要 AI 辅助
本文提出了一种用于无信号交叉口自动驾驶车辆的集成决策（DM）和轨迹规划（TP）的新框架。该方法利用有限时域最优控制问题（FHOCP），采用时变人工势场（TV-APF）。通过利用短视运动预测和专用冲突区占用系数，该框架在FHOCP中适当考虑了潜在碰撞。所提方法有效统一了DM和TP，确保生成可行且安全的参考轨迹。多车辆交通场景的仿真结果证明了该方法的有效性。
英文摘要
This paper presents a novel framework for integrated Decision-Making (DM) and Trajectory Planning (TP) for automated vehicles at unsignalized intersections. The approach leverages a Finite Horizon Optimal Control Problem (FHOCP) that employs Time-Varying Artificial Potential Fields (TV-APF). By utilizing short-horizon motion prediction and a dedicated conflict-zone occupancy coefficient, the framework suitably accounts for potential collisions within the FHOCP. The proposed method effectively unifies DM and TP, ensuring the generation of a feasible and safe reference trajectory. Simulation results in multi-vehicle traffic scenarios demonstrate the effectiveness of the approach.
