CriSGen: Constraint-Based Generation of Critical Scenarios for Autonomous Vehicles

Nonnengart, Andreas; Klusch, Matthias; Müller, Christian

doi:10.1007/978-3-030-54994-7_17

Cited by 11 publications

(7 citation statements)

References 15 publications

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“…Several studies have proposed methods to identify critical concrete scenarios [15][16][17][18][19][20][21][22][23][24][25][26]. Ponn et al [15] and Paardekooper et al [16] proposed similar methods using actual driving data.…”

Section: Related Workmentioning

confidence: 99%

Identification of Critical Scenario Components Based on Driving Database Analysis for Safety Assessment of Automated Driving Systems

Yoshitake,

Shino

2023

Applied Sciences

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A thorough safety assessment of an automated driving system (ADS) is necessary before its introduction into the market and practical application. Scenario-based assessments have received significant attention in research. However, identifying sufficient critical scenarios for ADSs is a major challenge, especially for complex urban environments with a variety of road geometries, traffic rules, and traffic participants. To identify the critical scenarios in this complex environment, it is essential to understand the environmental factors that lead to safety-critical events (e.g., accidents and near-miss incidents). Thus, this study proposes a method for identification of critical scenario components by analyzing near-miss incident data and extracting environmental factors that induce driver errors. In this study, we applied the proposed method to a scenario, in which an ego vehicle makes a right turn at a signalized intersection with an oncoming vehicle approaching the intersection in left-hand traffic, as a case study. The proposed method identified two components (dynamic occlusion caused by oncoming right-turn vehicles and change in traffic lights) that were both critical and challenging for ADSs. The case study results showed the usefulness of the identified components and the validity of the proposed method, which can extract critical scenario components explicitly.

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Section: Related Workmentioning

confidence: 99%

Identification of Critical Scenario Components Based on Driving Database Analysis for Safety Assessment of Automated Driving Systems

Yoshitake,

Shino

2023

Applied Sciences

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“…Criticality metrics can be used complementary in the definition of scenario classes, e.g. to constrain a class to a certain measured aspect of criticality [54,71].…”

Section: Scenario Elicitation (B2)mentioning

confidence: 99%

Criticality Metrics for Automated Driving: A Review and Suitability Analysis of the State of the Art

Westhofen

Neurohr

Koopmann

et al. 2022

Arch Computat Methods Eng

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The large-scale deployment of automated vehicles on public roads has the potential to vastly change the transportation modalities of today’s society. Although this pursuit has been initiated decades ago, there still exist open challenges in reliably ensuring that such vehicles operate safely in open contexts. While functional safety is a well-established concept, the question of measuring the behavioral safety of a vehicle remains subject to research. One way to both objectively and computationally analyze traffic conflicts is the development and utilization of so-called criticality metrics. Contemporary approaches have leveraged the potential of criticality metrics in various applications related to automated driving, e.g. for computationally assessing the dynamic risk or filtering large data sets to build scenario catalogs. As a prerequisite to systematically choose adequate criticality metrics for such applications, we extensively review the state of the art of criticality metrics, their properties, and their applications in the context of automated driving. Based on this review, we propose a suitability analysis as a methodical tool to be used by practitioners. Both the proposed method and the state of the art review can then be harnessed to select well-suited measurement tools that cover an application’s requirements, as demonstrated by an exemplary execution of the analysis. Ultimately, efficient, valid, and reliable measurements of an automated vehicle’s safety performance are a key requirement for demonstrating its trustworthiness.

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“…This method also sets auxiliary goals for NPCs to avoid generating unrealistic scenarios. [60] proposes CriSGen that uses constraint-based optimization and [57] uses BO.…”

Section: Adversarial Policymentioning

confidence: 99%

A Survey on Safety-Critical Driving Scenario Generation -- A Methodological Perspective

Ding¹,

Xu²,

Arief³

et al. 2022

Preprint

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Autonomous driving systems have witnessed a significant development during the past years thanks to the advance in machine learning-enabled sensing and decision-making algorithms. One critical challenge for their massive deployment in the real world is their safety evaluation. Most existing driving systems are still trained and evaluated on naturalistic scenarios collected from daily life or heuristically-generated adversarial ones. However, the large population of cars, in general, leads to an extremely low collision rate, indicating that the safety-critical scenarios are rare in the collected real-world data. Thus, methods to artificially generate scenarios become crucial to measure the risk and reduce the cost. In this survey, we focus on the algorithms of safety-critical scenario generation in autonomous driving. We first provide a comprehensive taxonomy of existing algorithms by dividing them into three categories: data-driven generation, adversarial generation, and knowledge-based generation. Then, we discuss useful tools for scenario generation, including simulation platforms and packages. Finally, we extend our discussion to five main challenges of current works -fidelity, efficiency, diversity, transferability, controllability -and research opportunities lighted up by these challenges.

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CriSGen: Constraint-Based Generation of Critical Scenarios for Autonomous Vehicles

Cited by 11 publications

References 15 publications

Identification of Critical Scenario Components Based on Driving Database Analysis for Safety Assessment of Automated Driving Systems

Identification of Critical Scenario Components Based on Driving Database Analysis for Safety Assessment of Automated Driving Systems

Criticality Metrics for Automated Driving: A Review and Suitability Analysis of the State of the Art

A Survey on Safety-Critical Driving Scenario Generation -- A Methodological Perspective

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