用深度集成与置信预测提升自动驾驶巡航安全性
Safe Adaptive Cruise Control Under Perception Uncertainty: A Deep Ensemble and Conformal Tube Model Predictive Control Approach
- 通过深度集成+置信预测量化感知不确定性
- 在分布外场景下仍能保持安全跟车距离
- 适合对安全性要求高的自动驾驶决策系统
自动驾驶高度依赖感知系统来解析环境以做出决策。为增强此类高安全关键应用的鲁棒性，本文提出将深度神经网络回归器的深度集成与置信预测相结合，用于预测和量化不确定性。在自适应巡航控制场景中，该方法从RGB图像中进行状态与不确定性估计，并向下游控制器传递DNN感知不确定性信息。设计了基于置信管模型预测控制的自适应巡航控制器，以确保概率安全性。在高保真仿真器上的评估表明，该算法在速度跟踪和安全距离维持方面均有效，包括在分布外（Out-of-Distribution）场景下。
原文摘要 · Abstract (English)
Autonomous driving heavily relies on perception systems to interpret the environment for decision-making. To enhance robustness in these safety critical applications, this paper considers a Deep Ensemble of Deep Neural Network regressors integrated with Conformal Prediction to predict and quantify uncertainties. In the Adaptive Cruise Control setting, the proposed method performs state and uncertainty estimation from RGB images, informing the downstream controller of the DNN perception uncertainties. An adaptive cruise controller using Conformal Tube Model Predictive Control is designed to ensure probabilistic safety. Evaluations with a high-fidelity simulator demonstrate the algorithm's effectiveness in speed tracking and safe distance maintaining, including in Out-Of-Distribution scenarios.
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