自动驾驶框架的双向规划与大型语言模型
Zhikun Ma1, Qicong Sun2, Takafumi Matsumaru1
1Graduate School of Information, Production and Systems, Waseda University, Kitakyushu 808-0135, Japan.
Sensors (Basel, Switzerland)
|October 26, 2024
概括
SafeMod通过一种全新的双向规划框架来提高自动驾驶的安全性. 它改善了复杂环境中的决策和场景管理,优于现有系统.
科学领域:
- 自主系统 自主系统
- 人工智能的人工智能
- 机器人技术 机器人技术 机器人技术
背景情况:
- 自主导航在动态环境中面临挑战,包括安全,意图预测和战略规划.
- 传统方法在适应性和安全机制方面存在局限性,以应对不可预测的场景.
研究的目的:
- 引入SafeMod,这是一个旨在提高自动驾驶安全性的新型框架.
- 提高自主导航系统中的决策和场景管理能力.
主要方法:
- SafeMod采用双向规划结构:前向规划使用大型语言模型进行行为预测和基于变压器的轨迹生成,后向规划使用Actor-Critic强化学习进行轨迹改进.
- 整合基于文本的环境描述和图像数据,以全面理解场景.
- 利用政策和价值函数,根据概率分布进行最佳行动选择.
主要成果:
- 与最近的规划系统相比,SafeMod在CARLA和nuScenes基准上表现优越.
- 在真实世界和模拟测试中,安全和决策的显著改善被观察到.
- 该框架有效地整合了安全考虑和决策过程.
结论:
- SafeMod提供了一种有前途的方法来应对自动驾驶的安全和决策挑战.
- 双向规划结构提高了复杂,动态环境中的适应性和稳定性.
- 这一框架有可能提高自主导航系统的可靠性.
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