Edge Case Detection in Automated Driving: Methods, Challenges, and Future Directions

IEEE Transactions on Intelligent Transportation Systems, vol. 27, pp. 10466–10489

Abstract

Automated vehicles (AVs) promise to enhance transportation safety and efficiency. However, ensuring their reliability in real-world conditions remains challenging, particularly due to rare and unexpected situations known as edge cases. While numerous approaches exist for detecting edge cases, a comprehensive survey reviewing these techniques is lacking. This paper bridges this gap by presenting a hierarchical review and systematic classification of edge case detection and assessment methodologies. Our classification is structured on two levels: first, by AV modules, including perception and trajectory-related (encompassing prediction, planning, and control) subsystems; and second, by underlying methodologies and theories guiding these techniques. Furthermore, we introduce "knowledge-driven" approaches, which complement data-driven methods by leveraging expert insights and domain knowledge to identify cases absent in training datasets. We then examine techniques and metrics for evaluating edge case detection methods, including detection performance, practical deployment (e.g., computational overhead), and domain-specific measures (e.g., crash rates and severity analysis). We conclude by highlighting key challenges for edge case detection, including data availability and quality issues, validation and interpretability limitations, the simulation-to-real gap, and computational constraints. The hierarchical classification and review of methods and assessment techniques in this survey enable modular and targeted testing frameworks by guiding the selection of detection methods for specific AV subsystems while considering methodological principles. It also supports practical testing by facilitating scenario generation in simulation and focused subsystem validation in the real world.

Authors 8

1. Saeed Rahmani

   Delft University of Technology

   Affiliation as printed

   Delft University of Technology

2. Sabine Rieder

   Technical University of Munich

   Affiliation as printed

   Technical University of Munich

3. Erwin de Gelder

   Netherlands Organisation for Applied Scientific Research

   Affiliation as printed

   Netherlands Organization for Applied Scientific Research (TNO)

4. Marcel Sonntag Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University

5. Jorge Lorente Mallada
   Affiliation as printed

   Toyota Motor Europe

6. Sytze Kalisvaart

   Netherlands Organisation for Applied Scientific Research

   Affiliation as printed

   Netherlands Organization for Applied Scientific Research (TNO)

7. Vahid Hashemi

   Audi (Germany)

   Affiliation as printed

   Audi AG

8. Simeon C. Calvert

   Delft University of Technology

   Affiliation as printed

   Delft University of Technology