Event-Chain Analysis for Automated Driving and ADAS Systems: Ensuring Safety and Meeting Regulatory Timing Requirements

TL;DR

This paper introduces an Event-Chain Modeling methodology for automated driving systems to ensure compliance with strict timing regulations, improve safety, and facilitate early verification.

Contribution

It presents a transparent, White-Box approach to derive, model, and validate timing constraints at the system architecture level, supporting regulatory compliance and safety analysis.

Findings

Early identification of compliance issues through simulation

Systematic parameter optimization for timing constraints

Quantitative evidence generation via probabilistic analysis

Abstract

Automated Driving Systems (ADS), including Advanced Driver Assistance Systems (ADAS), must fulfill not only high functional expectations but also stringent timing constraints mandated by international regulations and standards. Regulatory frameworks such as UN regulations, NCAP standards, ISO norms, and NHTSA guidelines impose strict bounds on system reaction times to ensure safe vehicle operation. This paper presents a structured, White-Box methodology based on Event-Chain Modeling to address these timing challenges. Unlike Black-Box approaches, Event-Chain Analysis offers transparent insights into the timing behavior of each functional component - from perception and planning to actuation and human interaction. This perspective is also aligned with multiple regulations, which require that homologation dossiers provide evidence that the chosen system architecture is suitable to ensure compliance with the specified requirements. Our methodology enables the derivation, modeling, and validation of end-to-end timing constraints at the architectural level and facilitates early verification through simulation. Through a detailed case study, we demonstrate how this Event-Chain-centric approach enhances regulatory compliance, optimizes system design, and supports model-based safety analysis techniques, with results showing early identification of compliance issues, systematic parameter optimization, and quantitative evidence generation through probabilistic analysis.