统一决策与控制框架，提升城市自动驾驶安全性与合规性
UDMC: Unified Decision-Making and Control Framework for Urban Autonomous Driving with Motion Prediction of Traffic Participants
- 将决策与控制整合为统一最优控制问题，考虑交通参与者动态交互
- 在CARLA仿真中实现高效实时执行，安全性能优于多个基线模型
- 适合研究城市自动驾驶系统集成与安全控制的开发者与研究人员
当前自动驾驶系统在复杂城市环境中常难以平衡决策与运动控制，导致效率低下和安全隐患。现有方法因功能分离而表现不足。为此，我们提出UDMC——一种可解释的、面向L4级的城市自动驾驶统一框架。该框架将决策与运动控制融合为单一最优控制问题（OCP），综合考虑周围车辆、行人、车道线及交通信号的动态交互。通过创新设计的势函数建模交通参与者与规则，并引入专用运动预测模块，显著提升道路安全性和规则遵守度。集成架构支持多样化驾驶场景下的实时灵活操作。在CARLA平台的高保真仿真中，UDMC展现出卓越的计算效率、鲁棒性与安全性，驾驶性能超越多种基线模型。项目开源地址：https://github.com/henryhcliu/udmc_carla.git。
原文摘要 · Abstract (English)
Current autonomous driving systems often struggle to balance decision-making and motion control while ensuring safety and traffic rule compliance, especially in complex urban environments. Existing methods may fall short due to separate handling of these functionalities, leading to inefficiencies and safety compromises. To address these challenges, we introduce UDMC, an interpretable and unified Level 4 autonomous driving framework. UDMC integrates decision-making and motion control into a single optimal control problem (OCP), considering the dynamic interactions with surrounding vehicles, pedestrians, road lanes, and traffic signals. By employing innovative potential functions to model traffic participants and regulations, and incorporating a specialized motion prediction module, our framework enhances on-road safety and rule adherence. The integrated design allows for real-time execution of flexible maneuvers suited to diverse driving scenarios. High-fidelity simulations conducted in CARLA exemplify the framework's computational efficiency, robustness, and safety, resulting in superior driving performance when compared against various baseline models. Our open-source project is available at https://github.com/henryhcliu/udmc_carla.git.
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