A Method for the Runtime Validation of AI-based Environment Perception in Automated Driving System

TL;DR

This paper introduces a runtime validation method for AI-based environment perception in autonomous driving, focusing on monitoring camera and LiDAR sensor data to enhance safety and reliability.

Contribution

It proposes a novel function monitor for real-time validation of AI perception systems, addressing the lack of comprehensive specifications in AI-based automotive safety standards.

Findings

Effective in detecting perception anomalies in controlled tests

Provides insights into monitor performance and real-world applicability

Supports safety assurance for AI-based perception in autonomous vehicles

Abstract

Environment perception is a fundamental part of the dynamic driving task executed by Autonomous Driving Systems (ADS). Artificial Intelligence (AI)-based approaches have prevailed over classical techniques for realizing the environment perception. Current safety-relevant standards for automotive systems, International Organization for Standardization (ISO) 26262 and ISO 21448, assume the existence of comprehensive requirements specifications. These specifications serve as the basis on which the functionality of an automotive system can be rigorously tested and checked for compliance with safety regulations. However, AI-based perception systems do not have complete requirements specification. Instead, large datasets are used to train AI-based perception systems. This paper presents a function monitor for the functional runtime monitoring of a two-folded AI-based environment perception for ADS, based respectively on camera and LiDAR sensors. To evaluate the applicability of the function monitor, we conduct a qualitative scenario-based evaluation in a controlled laboratory environment using a model car. The evaluation results then are discussed to provide insights into the monitor's performance and its suitability for real-world applications.