Bayesian Optimization applied for accelerated Virtual Validation of the Autonomous Driving Function

TL;DR

This paper presents a Bayesian Optimization framework to efficiently identify critical scenarios in autonomous vehicle validation, significantly reducing simulation efforts and enabling scalable, comprehensive testing of motion planners.

Contribution

It introduces a novel Bayesian Optimization-based approach for accelerated scenario discovery in autonomous driving validation, demonstrating effectiveness in complex, high-dimensional spaces.

Findings

Identifies hazardous scenarios with fewer simulations than traditional methods.

Scales effectively to higher-dimensional parameter spaces.

Detects multiple critical regions within the operational domain.

Abstract

Rigorous Verification and Validation (V&V) of Autonomous Driving Functions (ADFs) is paramount for ensuring the safety and public acceptance of Autonomous Vehicles (AVs). Current validation relies heavily on simulation to achieve sufficient test coverage within the Operational Design Domain (ODD) of a vehicle, but exhaustively exploring the vast parameter space of possible scenarios is computationally expensive and time-consuming. This work introduces a framework based on Bayesian Optimization (BO) to accelerate the discovery of critical scenarios. We demonstrate the effectiveness of the framework on an Model Predictive Controller (MPC)-based motion planner, showing that it identifies hazardous situations, such as off-road events, using orders of magnitude fewer simulations than brute-force Design of Experiments (DoE) methods. Furthermore, this study investigates the scalability of the framework in higher-dimensional parameter spaces and its ability to identify multiple, distinct critical regions within the ODD of the motion planner used as the case study .