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Synthesizing Follow-Up Drive Data for Enhanced Road Safety in Intelligent Driving Function Systems

Nico Schick, Franjo Čičak

TL;DR

The paper tackles the data scarcity problem in safety-critical driving by proposing a synthetic data-generation framework for follow-up-drive scenarios, modeled as multivariate time series with realistic driver reaction times. It combines kinematic vehicle dynamics for two interacting vehicles with gamma-distributed reaction times and a DSS-based safety metric to classify and validate safety-critical events. Key contributions include explicit motion dynamics for leading and following vehicles, a data-generation algorithm to produce diverse time-series scenarios, and a DSS-based validation workflow demonstrated on a representative follow-up drive. This approach provides expanded test coverage and objective safety assessment to strengthen the validation of driver assistance and autonomous driving systems in high-risk edge cases.

Abstract

This study underscores the vital importance of intelligent driving functions in enhancing road safety and driving comfort. Central to our research is the challenge of obtaining sufficient test data for evaluating these functions, especially in high-risk, safety-critical driving scenarios. Such scenarios often suffer from a dearth of available data, primarily due to their inherent complexity and the risks involved. Addressing this gap, our research introduces a novel methodology designed to create a wide array of diverse and realistic safety-critical driving scenarios. This approach significantly broadens the testing spectrum for driver assistance systems and autonomous vehicle functions. We particularly focus on the follow-up drive scenario due to its high relevance in practical applications. Here, vehicle movements are intricately modeled using kinematic equations, incorporating factors like driver reaction times. We vary parameters to generate a spectrum of plausible driving scenarios. The utilization of the Difference Space Stopping (DSS) metric is a pivotal element in our research. This metric plays a crucial role in the safety evaluation of follow-up drives, facilitating a more thorough and comprehensive validation process. By doing so, our methodology enhances the reliability and safety assessment of driver assistance and autonomous driving systems, specifically tailored for the most challenging and safety-critical scenarios.

Synthesizing Follow-Up Drive Data for Enhanced Road Safety in Intelligent Driving Function Systems

TL;DR

The paper tackles the data scarcity problem in safety-critical driving by proposing a synthetic data-generation framework for follow-up-drive scenarios, modeled as multivariate time series with realistic driver reaction times. It combines kinematic vehicle dynamics for two interacting vehicles with gamma-distributed reaction times and a DSS-based safety metric to classify and validate safety-critical events. Key contributions include explicit motion dynamics for leading and following vehicles, a data-generation algorithm to produce diverse time-series scenarios, and a DSS-based validation workflow demonstrated on a representative follow-up drive. This approach provides expanded test coverage and objective safety assessment to strengthen the validation of driver assistance and autonomous driving systems in high-risk edge cases.

Abstract

This study underscores the vital importance of intelligent driving functions in enhancing road safety and driving comfort. Central to our research is the challenge of obtaining sufficient test data for evaluating these functions, especially in high-risk, safety-critical driving scenarios. Such scenarios often suffer from a dearth of available data, primarily due to their inherent complexity and the risks involved. Addressing this gap, our research introduces a novel methodology designed to create a wide array of diverse and realistic safety-critical driving scenarios. This approach significantly broadens the testing spectrum for driver assistance systems and autonomous vehicle functions. We particularly focus on the follow-up drive scenario due to its high relevance in practical applications. Here, vehicle movements are intricately modeled using kinematic equations, incorporating factors like driver reaction times. We vary parameters to generate a spectrum of plausible driving scenarios. The utilization of the Difference Space Stopping (DSS) metric is a pivotal element in our research. This metric plays a crucial role in the safety evaluation of follow-up drives, facilitating a more thorough and comprehensive validation process. By doing so, our methodology enhances the reliability and safety assessment of driver assistance and autonomous driving systems, specifically tailored for the most challenging and safety-critical scenarios.
Paper Structure (21 sections, 8 equations, 4 figures, 1 table, 3 algorithms)

This paper contains 21 sections, 8 equations, 4 figures, 1 table, 3 algorithms.

Table of Contents

  1. Motivation
  2. Objective
  3. Time Series
  4. Safety-critical Driving Scenarios
  5. Implicit Scenarios
  6. Explicit Scenarios
  7. Challenges and Opportunities
  8. Follow-Up Drive
  9. Illustration
  10. Dynamics of Vehicle Motion
  11. Kinematics of the Leading Vehicle (L)
  12. Kinematics of the Trailing Vehicle (F)
  13. Effective Distance between Leading Vehicles (L) and Following Vehicle (F)
  14. Driver's Reaction Time
  15. Algorithm
  16. ...and 6 more sections

Figures (4)

  • Figure 1: Taxonomy of safety-critical driving scenarios
  • Figure 2: Follow-Up Drive: Illustration
  • Figure 3: Follow-Up Drive: Importance of vehicle length
  • Figure 4: Validation: Follow-Up Drive (Illustration)