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Updated: May 31, 2025

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基于改进的受约束代式LQRR的自动驾驶汽车的时空空间联合轨迹规划
Qin Li1, Hongwen He1, Manjiang Hu2
1National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China.
Sensors (Basel, Switzerland)
|January 25, 2025
概括
这项研究通过改进受约束的代线性方程调节器 (CILQR) 来提高自主驾驶轨迹规划,以更好地与人类相似的驾驶和交通效率. 这种新方法显著提高了性能,并减少了计算时间.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 控制系统 控制系统
- 人工智能的人工智能
背景情况:
- 自动驾驶需要复杂的时空联合轨迹规划.
- 传统方法与复杂的场景作斗争,突出了顺序解的局限性.
- 有约束的代线性正方体调节器 (CILQR) 是有前途的,但需要提高效率和适应性.
研究的目的:
- 为了加强自主驾驶轨迹规划的受约束代线性二次调节器 (CILQR).
- 为了提高计算效率,场景适应性和驾驶舒适性.
- 为了实现更人性化的驾驶和提高交通效率.
主要方法:
- 实施了细分屏障功能截断策略,并使用动态放松因子来保持稳定性.
- 引入了适应性重量参数调整,用于加速和曲率规划.
- 集成混合A*算法以优化初始轨迹并提高代效率.
主要成果:
- 在与人类相似的驾驶性能方面表现出显著的改进 (16.35%的增长).
- 实现了交通效率的平均提高12.65%.
- 减少了39.29%的计算时间,同时保持了驾驶舒适度.
结论:
- 改进的CILQR方法在自动驾驶轨迹规划方面取得了重大进展.
- 这些改进带来了更高效,更适应性,更类似人类的自主导航.
- 通过模拟和真实车辆测试进行验证,证明其实际适用性.
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