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.

我们在这篇论文中开发了一个基于深度神经网络的集成回归器，用于自适应巡航控制中估计与前车的车头距离，并通过概率安全约束来实现跟踪速度和保持安全跟车距离的目标。