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在复杂的城市环境中用于自动驾驶汽车的基于时空空间联合优化的轨迹规划方法
Jianhua Guo1, Zhihao Xie1, Ming Liu2
1State Key Laboratory of Automotive Simulation and Control, Jilin University, No. 5988, Renmin Street, Nanguan District, Changchun 130022, China.
Sensors (Basel, Switzerland)
|July 27, 2024
概括
本研究提出了一种新的时空联合优化轨迹规划 (SJOTP) 方法,用于在复杂的城市环境中导航的自动驾驶汽车. 该方法通过整合多约束优化和避免碰撞算法来确保安全和高效的路径.
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
- 自主系统 自主系统
背景情况:
- 安全和高效的轨道规划对于在动态的城市环境中实现自动驾驶至关重要.
- 具有多种约束的复杂环境挑战现有的行为规划者,可能导致不安全的轨迹.
- 目前的方法在复杂的城市场景中难以保证无碰撞路径.
研究的目的:
- 为复杂的城市环境引入基于时空联合优化的轨迹规划 (SJOTP) 方法.
- 为应对多约束轨迹规划的挑战,确保安全和效率.
- 开发一款用于自动驾驶汽车导航的强大解决方案.
主要方法:
- 一个行为规划器生成初始轨迹集群.
- 在一个膨胀的地图上,以拓为导向的混合A*算法减轻了静态障碍物碰撞.
- 多限制,多目标的轨迹规划考虑了障碍物,道路附着性和车辆动态,使用差异平度和点质量模型.
- 一个时空关节优化解决方案处理纵向和横向合.
主要成果:
- 在模拟中,SJOTP方法成功生成了最佳轨迹.
- 该方法展示了安全和高效的路线规划能力.
- 在多代理模拟平台上的验证证实了该方法的有效性.
结论:
- 拟议的SJOTP方法为复杂的城市环境中安全和高效的轨迹规划提供了可行的解决方案.
- 多重约束优化和先进算法的集成增强了自动驾驶汽车的导航.
- 这项研究有助于推进可靠的自动驾驶系统.
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