潜在的基于现场重建的自动驾驶汽车路径规划系统,增强稳定性
Zihao Fu1, Zhixian Liu1, Weihua Tan2,3
1Huaihua University, Huaihua, China.
PloS one
|September 30, 2025
概括
本研究介绍了一种基于现场重建的自动驾驶汽车 (AV) 路径规划系统 (PFR-BPPS) 的新潜力. 在紧急情况下,PFR-BPPS增强了稳定性和速度适应性.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 人工智能的人工智能
- 控制系统 控制系统
背景情况:
- 路线规划和跟踪对于自动驾驶汽车 (AV) 运行至关重要.
- 传统的方法在紧急情况下难以确保AV稳定性.
- 现有的系统往往优先考虑在关键情况下避免障碍,而不是动态稳定.
研究的目的:
- 为自动驾驶汽车 (AV) 提出一种新的路径规划系统,以确保在紧急情况下的稳定性.
- 解决传统路径规划方法在突发事件下保持AV稳定的局限性.
- 在关键情况下提高AV的速度适应性和路径稳定性.
主要方法:
- 开发了一个潜在的基于现场重建的路径规划系统 (PFR-BPPS).
- 该系统包括一个潜在场重建模块和一个自适应融合模块.
- 模糊的推理规则被用于潜在速度和稳定场的自适应集成.
主要成果:
- 在Matlab-Carsim共同模拟平台上进行的模拟中,PFR-BPPS表现出卓越的性能.
- 该系统有效地提高了自动驾驶汽车的速度适应性.
- 拟议的方法在紧急情况下显著提高了路径稳定性.
结论:
- PFR-BPPS为自动驾驶汽车的路线规划提供了一个强大的解决方案,特别是在紧急情况下.
- 通过模糊逻辑集成潜在速度和稳定性场,可以提高整体系统性能.
- 这种方法有助于实现更安全,更可靠的自动驾驶汽车运行.
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