DRO-EDL-MPC: Evidential Deep Learning-Based Distributionally Robust Model Predictive Control for Safe Autonomous Driving

TL;DR

This paper introduces a novel distributionally robust model predictive control framework that leverages evidential deep learning to handle perception uncertainties, enhancing safety and efficiency in autonomous vehicle motion planning.

Contribution

It proposes a new ambiguity set formulation based on evidential distributions and integrates it into MPC for improved safety under perception uncertainties.

Findings

Maintains efficiency with high perception confidence.

Enforces conservative constraints with low confidence.

Validated in CARLA simulator with promising results.

Abstract

Safety is a critical concern in motion planning for autonomous vehicles. Modern autonomous vehicles rely on neural network-based perception, but making control decisions based on these inference results poses significant safety risks due to inherent uncertainties. To address this challenge, we present a distributionally robust optimization (DRO) framework that accounts for both aleatoric and epistemic perception uncertainties using evidential deep learning (EDL). Our approach introduces a novel ambiguity set formulation based on evidential distributions that dynamically adjusts the conservativeness according to perception confidence levels. We integrate this uncertainty-aware constraint into model predictive control (MPC), proposing the DRO-EDL-MPC algorithm with computational tractability for autonomous driving applications. Validation in the CARLA simulator demonstrates that our approach maintains efficiency under high perception confidence while enforcing conservative constraints under low confidence.