结合信念函数理论与随机模型预测控制处理自动驾驶中的多模态不确定性
Combining Belief Function Theory and Stochastic Model Predictive Control for Multi-Modal Uncertainty in Autonomous Driving
AI总结：
针对自动驾驶中交通参与者意图不确定带来的多模态不确定性问题，提出结合信念函数理论与随机模型预测控制的轨迹规划方法，通过信念转概率转换和约束收紧机制平衡安全与保守性，仿真验证优于现有方法。
摘要：
在自动驾驶领域，预测并适配其他交通参与者的不确定未来运动是一项挑战，尤其是在交通参与者高层意图难以预测的非结构化环境中。我们必须考虑交通参与者多种可能的不确定未来行为，由此产生了多模态不确定性。我们提出一种将信念函数理论（Belief Function Theory）与随机模型预测控制（Stochastic Model Predictive Control）相结合的新方法，用于在交通参与者意图估计存在显著不确定性的场景下，为自动驾驶车辆进行轨迹规划。对交通参与者意图的误判可能引发危险状况，同时也必须避免过度保守。因此，我们将信念函数理论提供的估计可靠性度量用于避碰安全约束的设计，尤其是在交通参与者意图不明确时提升安全性。我们探讨了两种利用信念函数理论的方法：一是提出一种新颖的信念到概率的转换方法，该方法在信息不确定时不会低估低概率事件；二是一种利用估计可靠性的约束收紧机制。我们通过仿真将所提方法与当前最优方法进行对比，对方案进行了评估。
英文摘要：
In automated driving, predicting and accommodating the uncertain future motion of other traffic participants is challenging, especially in unstructured environments in which the high-level intention of traffic participants is difficult to predict. Several possible uncertain future behaviors of traffic participants must be considered, resulting in multi-modal uncertainty. We propose a novel combination of Belief Function Theory and Stochastic Model Predictive Control for trajectory planning of the autonomous vehicle in presence of significant uncertainty about the intention estimation of traffic participants. A misjudgment of the intention of traffic participants may result in dangerous situations. At the same time, excessive conservatism must be avoided. Therefore, the measure of reliability of the estimation provided by Belief Function Theory is used in the design of collision-avoidance safety constraints, in particular to increase safety when the intention of traffic participants is not clear. We discuss two methods to leverage on Belief Function Theory: we introduce a novel belief-to-probability transformation designed not to underestimate unlikely events if the information is uncertain, and a constraint tightening mechanism using the reliability of the estimation. We evaluate our proposal through simulations comparing to state-of-the-art approaches.
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