提出融合蚁群与模型预测的路径规划框架，实现自动驾驶车辆在匝道上安全高效变道。
An ACO-MPC Framework for Energy-Efficient and Collision-Free Path Planning in Autonomous Maritime Navigation
- 结合蚁群优化与模型预测控制，动态评估变道时机与轨迹
- 仿真显示零碰撞，且能根据车速选择最优变道时机
- 适合高速匝道场景的自动驾驶系统开发
自动驾驶车辆在匝道变道时需兼顾安全与效率，本文提出一种集成规划框架，采用效率不满足度指标衡量效率，基于箭头簇采样确保安全。该框架通过考虑车辆速度来识别最佳变道时机，并利用箭头簇采样评估碰撞风险，选择最优变道曲线。大量匝道场景仿真实验表明，所提方法能有效选择合适的变道时机与安全轨迹，整个变道过程未发生任何碰撞。
原文摘要 · Abstract (English)
Automated driving on ramps presents significant challenges due to the need to balance both safety and efficiency during lane changes. This paper proposes an integrated planner for automated vehicles (AVs) on ramps, utilizing an unsatisfactory level metric for efficiency and arrow-cluster-based sampling for safety. The planner identifies optimal times for the AV to change lanes, taking into account the vehicle's velocity as a key factor in efficiency. Additionally, the integrated planner employs arrow-cluster-based sampling to evaluate collision risks and select an optimal lane-changing curve. Extensive simulations were conducted in a ramp scenario to verify the planner's efficient and safe performance. The results demonstrate that the proposed planner can effectively select an appropriate lane-changing time point and a safe lane-changing curve for AVs, without incurring any collisions during the maneuver.
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