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.

翻译：自动驾驶高度依赖感知系统来解析环境以进行决策。为增强此类安全关键应用的鲁棒性，本文提出一种集成保形预测的深度神经网络回归器深度集成方法，用于预测并量化不确定性。在自适应巡航控制场景中，所提方法通过RGB图像进行状态与不确定性估计，并将DNN感知不确定性传递给下游控制器。设计了一种采用保形管模型预测控制的自适应巡航控制器，以确保概率安全性。通过高保真仿真验证表明，该算法在速度跟踪与安全距离保持方面具有显著效果，包括在分布外场景下的表现。