多行为预测下延迟决策提升自动驾驶安全性
Delayed-Decision Motion Planning in the Presence of Multiple Predictions
- 基于最大熵建模多种可能行为及其概率，支持延迟决策
- 实验表明延迟决策可显著提升路径规划安全性
- 适用于复杂交通场景的实时自动驾驶系统
可靠的自动驾驶技术面临多重不确定性挑战，尤其是交通参与者的行为不确定性。交通参与者常具有他人未知的意图，导致自动驾驶车辆需在多个可能行为中进行推理。本文提出一种在多种可能未来情形及对应概率下的行为规划框架。通过最大熵建模，证明在特定假设下延迟决策能提升安全性。该通用框架被转化为模型预测控制形式，以二次规划或一组二次规划求解。讨论了提升计算效率的实现细节，并在仿真环境与移动机器人上验证了其运行有效性。
原文摘要 · Abstract (English)
Reliable automated driving technology is challenged by various sources of uncertainties, in particular, behavioral uncertainties of traffic agents. It is common for traffic agents to have intentions that are unknown to others, leaving an automated driving car to reason over multiple possible behaviors. This paper formalizes a behavior planning scheme in the presence of multiple possible futures with corresponding probabilities. We present a maximum entropy formulation and show how, under certain assumptions, this allows delayed decision-making to improve safety. The general formulation is then turned into a model predictive control formulation, which is solved as a quadratic program or a set of quadratic programs. We discuss implementation details for improving computation and verify operation in simulation and on a mobile robot.
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