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在线集中式MPC用于车队车道合并
Shila Alizadehghobadi1, Mukesh Singhal2, Reza Ehsani1
1Department of Mechanical Engineering, University of California, Merced, CA 95343, USA.
Sensors (Basel, Switzerland)
|September 13, 2025
概括
一个新的集中式模型预测控制 (MPC) 框架使自动驾驶汽车队伍的在线轨迹跟踪成为可能. 这种方法优化了车道合并,减少了高达40%的能源消耗,并提高了适应动态条件的适应性.
科学领域:
- 机器人和控制系统 机器人和控制系统
- 自动驾驶汽车技术自动驾驶汽车技术
- 交通工程是交通工程.
背景情况:
- 自动驾驶汽车的有效和安全的车道融合对于减少交通拥堵和提高道路安全至关重要.
- 现有的模型预测控制 (MPC) 方法通常依赖于离线规划,限制其适应动态场景的适应性.
- 需要灵活的,在线控制策略,在合并机动期间多车队排队是显而易见的.
研究的目的:
- 提出一个集中式的MPC框架,用于在车道合并期间在线追踪多车队的轨迹.
- 调查预测地平线和中队规模对合并演习的可行性和效率的影响.
- 证明框架能够处理动态约束和干扰以实现无碰撞运行的能力.
主要方法:
- 开发一个集中式的模型预测控制 (MPC) 框架,用于在线轨迹优化.
- 预测地平线和中队规模对合并机动可行性和效率的影响的分析.
- 在动态约束和干扰下对算法的性能进行评估.
主要成果:
- 确定了最佳预测地平线,最大限度地减少制动和加速,从而节省了35-40%的能源.
- 超出可行性要求的预测地平线可以改变车辆排序,这是离线方法缺乏的能力.
- 较大的排队大小观察到最低可行的预测地平线的显著增加.
结论:
- 与线下方法相比,拟议的在线MPC框架为自动驾驶车道合并提供了更大的灵活性和适应性.
- 该研究强调了预测地平线在优化能源效率和实现动态车辆重新排序方面的关键作用.
- 这些发现强调了与更大的自动驾驶汽车车队相关的可扩展性挑战和控制复杂性的增加.
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