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

Sebastian Dingler, Philip Rehkop, Florian Mayer, Ralf Muenzenberger

TL;DR

The paper tackles the challenge of meeting stringent regulatory timing requirements for ADS/ADAS by introducing a White-Box Event-Chain Analysis (ECA) that links regulatory text to architecture and enables end-to-end timing verification from perception to actuation. It defines Event-Chains as sequences of causally related, discrete events and develops an ontology to derive formal timing requirements from regulations, allowing both Black-Box and White-Box views with budgets across subsystems. A detailed Automated Emergency Braking (AEB) case study demonstrates how probabilistic sensor models and braking dynamics can be embedded in executable Event-Chain models, with simulation-based verification using Monte Carlo runs to assess compliance and drive optimization of sensing and timing budgets. The approach yields auditable homologation evidence, supports shift-left verification, and offers practical guidance for OEMs in balancing regulatory compliance with system feasibility, as shown in industrial practice within Daimler Truck environments. Mathematical formulations for stopping distance, time-to-react, and budgeting (e.g., $t_{ ext{acq}}\, ext{and}\,t_{ ext{det}}\, ext{constraints}$, $TTR(d_o,v_{ ext{ego}})$) underpin the methodology and enable quantitative regulatory assurance through chronSIM simulations and 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.

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

TL;DR

The paper tackles the challenge of meeting stringent regulatory timing requirements for ADS/ADAS by introducing a White-Box Event-Chain Analysis (ECA) that links regulatory text to architecture and enables end-to-end timing verification from perception to actuation. It defines Event-Chains as sequences of causally related, discrete events and develops an ontology to derive formal timing requirements from regulations, allowing both Black-Box and White-Box views with budgets across subsystems. A detailed Automated Emergency Braking (AEB) case study demonstrates how probabilistic sensor models and braking dynamics can be embedded in executable Event-Chain models, with simulation-based verification using Monte Carlo runs to assess compliance and drive optimization of sensing and timing budgets. The approach yields auditable homologation evidence, supports shift-left verification, and offers practical guidance for OEMs in balancing regulatory compliance with system feasibility, as shown in industrial practice within Daimler Truck environments. Mathematical formulations for stopping distance, time-to-react, and budgeting (e.g., , ) underpin the methodology and enable quantitative regulatory assurance through chronSIM simulations and 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.

Paper Structure

This paper contains 20 sections, 11 equations, 6 figures.

Table of Contents

  1. Introduction
  2. Regulatory Background and Timing Constraints
  3. Event-Chain Engineering to Ensure Regulatory Timing Requirements
  4. Modeling Event-Chains as a Basis for Temporal System Evaluation
  5. Deriving Event-Chain Models from Regulatory Texts
  6. Transition from Black-Box to White-Box at the Functional Level
  7. Analysis via Event-Chain Models
  8. Case Study: Event-Chain Modeling for Automated Emergency Braking
  9. Regulatory Requirements
  10. Physical Preconditions and System-Level Assumptions
  11. Brake Control
  12. Trajectory Prediction
  13. Collision Assessment
  14. Warning Issuance
  15. Object Detection
  16. ...and 5 more sections

Figures (6)

  • Figure 1: Ontology for translating regulations into event-chain models.
  • Figure 2: Event-chain activity diagram for the AEB case. The chain models the timed flow from perception to actuation: Data Acquisition, Object Detection, Trajectory Prediction, Warning Assessment, Collision Assessment, and Brake Control. Decision nodes split the flow into warning versus braking actions. This formalized sequence defines the end-to-end path on which timing budgets and regulatory checks (e.g., issuing a warning at least 0.8 s before braking) are verified and simulated.
  • Figure 3: Braking scenario. a. Speed–time diagram of the ego vehicle, b. Black-boxEvent-Chain, c. White-boxEvent-Chains: EC Braking and EC Warning
  • Figure 4: Event-chain for the AEB case in chronSUITE
  • Figure 5: Histogram of requirement on budget $t_{acq}$
  • ...and 1 more figures