Autonomous driving depends on perception systems to understand the environment and to inform downstream decision-making. While advanced perception systems utilizing black-box Deep Neural Networks (DNNs) demonstrate human-like comprehension, their unpredictable behavior and lack of interpretability may hinder their deployment in safety critical scenarios. In this paper, we develop an Ensemble of DNN regressors (Deep Ensemble) that generates predictions with quantification of prediction uncertainties. In the scenario of Adaptive Cruise Control (ACC), we employ the Deep Ensemble to estimate distance headway to the lead vehicle from RGB images and enable the downstream controller to account for the estimation uncertainty. We develop an adaptive cruise controller that utilizes Stochastic Model Predictive Control (MPC) with chance constraints to provide a probabilistic safety guarantee. We evaluate our ACC algorithm using a high-fidelity traffic simulator and a real-world traffic dataset and demonstrate the ability of the proposed approach to effect speed tracking and car following while maintaining a safe distance headway. The out-of-distribution scenarios are also examined.

翻译：自动驾驶依赖于感知系统来理解环境并为下游决策提供信息。尽管利用黑箱深度神经网络的先进感知系统展现出类人的理解能力，但其不可预测的行为和缺乏可解释性可能阻碍其在安全关键场景中的部署。本文开发了一种深度神经网络回归器集成（深度集成），可生成带有预测不确定性量化的预测结果。在自适应巡航控制场景中，我们采用深度集成从RGB图像中估计与前车的距离车头时距，并使下游控制器能够考虑估计的不确定性。我们开发了一种自适应巡航控制器，利用基于机会约束的随机模型预测控制提供概率安全性保证。我们通过高保真度交通模拟器和真实世界交通数据集评估所提出的自适应巡航控制算法，并展示了该方法在跟踪速度和跟驰过程中保持安全距离车头时距的能力。此外，还评估了分布外场景下的性能。