用证据深度学习提升自动驾驶控制的安全性，动态适应感知不确定性。
DRO-EDL-MPC: Evidential Deep Learning-Based Distributionally Robust Model Predictive Control for Safe Autonomous Driving
- 结合证据深度学习构建可动态调整保守性的不确定集
- 在CARLA仿真中实现高置信度下高效、低置信度下保守的控制
- 适合关注自动驾驶安全与鲁棒控制的研究者
自动驾驶运动规划中的安全性至关重要。现代自动驾驶车辆依赖神经网络进行感知，但基于这些推理结果做出控制决策会因固有不确定性带来显著安全风险。为此，我们提出一种分布鲁棒优化（DRO）框架，利用证据深度学习（EDL）同时建模感知中的随机性与认知性不确定性。方法创新性地基于证据分布构建模糊集，其保守程度随感知置信度动态调整。将该不确定性约束融入模型预测控制（MPC），提出可计算高效的DRO-EDL-MPC算法，适用于自动驾驶场景。在CARLA仿真环境中的验证表明，当感知置信度高时保持控制效率，置信度低时自动强化安全约束。
原文摘要 · Abstract (English)
Safety is a critical concern in motion planning for autonomous vehicles. Modern autonomous vehicles rely on neural network-based perception, but making control decisions based on these inference results poses significant safety risks due to inherent uncertainties. To address this challenge, we present a distributionally robust optimization (DRO) framework that accounts for both aleatoric and epistemic perception uncertainties using evidential deep learning (EDL). Our approach introduces a novel ambiguity set formulation based on evidential distributions that dynamically adjusts the conservativeness according to perception confidence levels. We integrate this uncertainty-aware constraint into model predictive control (MPC), proposing the DRO-EDL-MPC algorithm with computational tractability for autonomous driving applications. Validation in the CARLA simulator demonstrates that our approach maintains efficiency under high perception confidence while enforcing conservative constraints under low confidence.
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