Autonomous Driving With Perception Uncertainties: Deep-Ensemble Based Adaptive Cruise Control
This addresses safety concerns in autonomous driving by improving reliability in perception for downstream control, though it is incremental as it builds on existing methods like stochastic MPC.
The paper tackled the problem of unpredictable behavior in perception systems for autonomous driving by developing a Deep Ensemble to quantify uncertainties in distance headway estimation, resulting in an adaptive cruise controller that maintained safe distances with probabilistic safety guarantees in simulations and real-world data.
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.