自动驾驶系统安全避让行人与骑行者，支持远程接管应急情况。
A Vehicle System for Navigating Among Vulnerable Road Users Including Remote Operation
- 基于拓扑驱动的模型预测控制生成多条避障策略。
- 在仿真中相较基线方法提升安全性和通行效率。
- 支持远程操控，适合复杂或突发路况场景。
我们提出一种可安全高效绕行脆弱道路使用者（如行人、骑行者）的车辆系统，包含环境感知、定位建图、运动规划和控制等关键模块，并集成于原型车中。核心创新在于基于拓扑驱动模型预测控制（T-MPC）的运动规划器：引导层并行生成多条轨迹，代表不同避障或不超车策略；底层轨迹优化在通用不确定性下约束与脆弱道路使用者的联合碰撞概率。针对超出自主能力的极端情况（如施工区、应急人员），系统提供远程人工操作功能，辅以视觉与触觉反馈。仿真结果显示，本规划器在安全性与效率上优于三种基线方法。我们在封闭场地的原型车测试中验证了系统在自主与远程操控模式下的有效性。
原文摘要 · Abstract (English)
We present a vehicle system capable of navigating safely and efficiently around Vulnerable Road Users (VRUs), such as pedestrians and cyclists. The system comprises key modules for environment perception, localization and mapping, motion planning, and control, integrated into a prototype vehicle. A key innovation is a motion planner based on Topology-driven Model Predictive Control (T-MPC). The guidance layer generates multiple trajectories in parallel, each representing a distinct strategy for obstacle avoidance or non-passing. The underlying trajectory optimization constrains the joint probability of collision with VRUs under generic uncertainties. To address extraordinary situations ("edge cases") that go beyond the autonomous capabilities - such as construction zones or encounters with emergency responders - the system includes an option for remote human operation, supported by visual and haptic guidance. In simulation, our motion planner outperforms three baseline approaches in terms of safety and efficiency. We also demonstrate the full system in prototype vehicle tests on a closed track, both in autonomous and remotely operated modes.
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