A Systematic Review of Edge Case Detection in Automated Driving: Methods, Challenges and Future Directions (2410.08491v1)

Abstract: The rapid development of automated vehicles (AVs) promises to revolutionize transportation by enhancing safety and efficiency. However, ensuring their reliability in diverse real-world conditions remains a significant challenge, particularly due to rare and unexpected situations known as edge cases. Although numerous approaches exist for detecting edge cases, there is a notable lack of a comprehensive survey that systematically reviews these techniques. This paper fills this gap by presenting a practical, hierarchical review and systematic classification of edge case detection and assessment methodologies. Our classification is structured on two levels: first, categorizing detection approaches according to AV modules, including perception-related and trajectory-related edge cases; and second, based on underlying methodologies and theories guiding these techniques. We extend this taxonomy by introducing a new class called "knowledge-driven" approaches, which is largely overlooked in the literature. Additionally, we review the techniques and metrics for the evaluation of edge case detection methods and identified edge cases. To our knowledge, this is the first survey to comprehensively cover edge case detection methods across all AV subsystems, discuss knowledge-driven edge cases, and explore evaluation techniques for detection methods. This structured and multi-faceted analysis aims to facilitate targeted research and modular testing of AVs. Moreover, by identifying the strengths and weaknesses of various approaches and discussing the challenges and future directions, this survey intends to assist AV developers, researchers, and policymakers in enhancing the safety and reliability of automated driving (AD) systems through effective edge case detection.

A Systematic Review of Edge Case Detection in Automated Driving: Methods, Challenges, and Future Directions

The rapid evolution of Automated Vehicles (AVs) offers significant potential to enhance transportation safety and efficiency. However, a key challenge lies in ensuring AV reliability amid real-world complexities, particularly through the identification and management of "edge cases"—rare and unusual scenarios that deviate from typical driving conditions. This paper presents a comprehensive survey of existing methods for detecting these edge cases, addressing a crucial gap in AV research by categorizing and evaluating the effectiveness of various detection techniques.

Edge Case Taxonomy and Methodologies

The investigation adopts a hierarchical classification system for edge case detection methodologies, delineating between perception-related and trajectory-related edge cases, as well as introducing the underexplored category of knowledge-driven detection methods. This dual-level categorization aims to enhance targeted research and facilitate modular testing in AV systems.

• Perception-related Edge Cases: These involve challenges with sensory data interpretation and object classification within the AV's environment. Detection methodologies are extensively categorized into reconstructive and generative approaches, confidence score-based methods, feature extraction from neural networks, and other novel techniques. Key perception datasets, alongside simulation environments, play vital roles in assessing these methods.
• Trajectory-related Edge Cases: These cases focus on motion prediction, planning, and control challenges. The survey covers detection using surrogate safety metrics, probability estimation, machine learning, and scenario generation based on system stress testing. Evaluation methods range from simulation-based validation to expert assessments.
• Knowledge-driven Approaches: These approaches leverage expert insights and predefined conditions to generate or identify potential edge cases. By formally describing these scenarios using ontologies, this category underscores the importance of integrating domain expertise to capture scenarios not evident through data alone.

Evaluation: Challenges and Approaches

A significant dimension of the paper is its exploration of evaluation techniques for edge case detection, which underscores practical and theoretical implications. The paper consolidates benchmark- and simulation-based assessments, as well as human expert evaluations, to enhance the reliability and validity of detection methods. However, challenges such as the sim2real gap, computational complexity, data quality, and validation difficulties remain critical areas requiring further research.

Implications and Future Research Directions

The paper identifies several pivotal areas for advancing the understanding and management of edge cases in AV systems:

1. Bridging the Sim2Real Gap: Enhanced simulation fidelity and integration with real-world data can reduce discrepancies between virtual and real-world scenarios, fostering more realistic and reliable edge case evaluation frameworks.
2. Leveraging Transfer and Federated Learning: These methodologies present opportunities to improve model adaptability and robustness through cross-domain knowledge transfer, optimizing detection systems without relying solely on extensive individual datasets.
3. Enhancing Interpretability: Improving the transparency of detection algorithms, particularly machine learning-based methods, is crucial for stakeholder trust and regulatory approval, facilitating more effective validation processes.
4. Standardized Evaluation Frameworks: Collaboration between industry, academia, and regulatory bodies can lead to standardized validation protocols, establishing best practices and facilitating cross-method benchmarking.

This survey provides a foundational reference for researchers, developers, and policymakers aiming to elevate AV safety through comprehensive edge case detection strategies. The insights offered elucidate the strengths, limitations, and future research potential in developing resilient AV systems capable of reliably navigating the complexities of real-world driving conditions.