Generic Detection and Search-based Test Case Generation of Urban Scenarios based on Real Driving Data

Thal, Silvia; Henze, Roman; Hasegawa, Ryo; Nakamura, Hiroki; Imanaga, Hisashi; Antona‐Makoshi, Jacobo; UCHIDA, Nobuyuki

doi:10.1109/iv51971.2022.9827198

Cited by 5 publications

(4 citation statements)

References 17 publications

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“…The methods used to assess the achieved coverage of the targeted scenario space can be explorative using histograms [34]. Moreover, metric-based methods using a specific coverage metric based on scenario classes [66] or search-based methods [48] are used to assess coverage. Other methods also investigate the diversity/similarity (2x) [36], [58], the exposure to the real world [66], and the possibility of testing a cooperative ADS [45] using customized metrics.…”

Section: A Process Of Scenario Generationmentioning

confidence: 99%

“…The most common sensors for real driving data are various camera videos or images from drones [17]- [20], [45], [48], [50], [65], [74], stationary cameras [19], [20], [42], [53], [62], [63], [71], [76], or dash cams/on-board cameras [26], [36], [43], [46], [51], [62], [63], [75]. Dynamic parameters, such as GPS or velocity, represent a large proportion of the real driving data (NDS and BUS data [16], [20]- [22], [24], [25], [27], [31], [32], [39], [44], [49], [51]- [53], [57]- [59], [66], [67], [72]).…”

Section: B Categorizationmentioning

confidence: 99%

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Categorizing Data-Driven Methods for Test Scenario Generation to Assess Automated Driving Systems

Bäumler,

Linke,

Prokop

2024

IEEE Access

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This survey aims to provide an overview of various methods for generating data-driven test scenarios for assessing automated driving systems (ADS). The survey updates the overall process of scenario generation and categorizes the current methods using a systematic literature review of 64 studies identified between 2017 and 01/2023. Overall, we demonstrate that the data-driven scenario generation process should be updated by another process step, scenario fusion, leading to seven process steps: (1) scope definition, (2) primary data source selection, (3) primary data collection, (4) scenario identification, ( 5) scenario fusion, ( 6) scenario generation, and (7) scenario evaluation. "Scenario fusion" aims to fuse scenarios identified in different data sources for a better coverage of the ADSs' operational design domains (ODDs) and a more comprehensive scenario description. Moreover, we introduce an improved definition for the representativity of test scenario catalogs, which helps improve the collection of traffic data using sampling plans. Also, we show that real driving and police accident data are the most commonly used data input sources. Besides, we illustrate that the ODD is often not defined. Finally, we discuss that the standardization of test scenario generation is difficult because most methods do not address specific ADSs and test environments, and do not provide standardized interfaces. Overall, we recommend comparing existing approaches using the same input data and researching the mutual supplementation of the existing methods. Finally, pre-defined case studies, further standardized terminology, and standards for test execution and evaluation can help speed up the standardization process.INDEX TERMS Advanced driver assistance systems, autonomous driving, system validation, vehicle safety I. INTRODUCTION

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Section: A Process Of Scenario Generationmentioning

confidence: 99%

Section: B Categorizationmentioning

confidence: 99%

Categorizing Data-Driven Methods for Test Scenario Generation to Assess Automated Driving Systems

Bäumler,

Linke,

Prokop

2024

IEEE Access

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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%

“…Stepien et al [21] proposed a method to generate test cases by applying heuristics to naturalistic driving data. Thal et al [22] further developed the method in [21] by adding boundary functions and a random sampling approach to improve the criticality and coverage of the generated test cases. Khastgir et al [23] proposed a method to identify test scenarios as an extension of the systems theoretic process analysis.…”

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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Towards Realistic, Safety-Critical and Complete Test Case Catalogs for Safe Automated Driving in Urban Scenarios

Thal,

Wallis,

Henze

et al. 2023

2023 IEEE Intelligent Vehicles Symposium (IV)

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Generic Detection and Search-based Test Case Generation of Urban Scenarios based on Real Driving Data

Cited by 5 publications

References 17 publications

Categorizing Data-Driven Methods for Test Scenario Generation to Assess Automated Driving Systems

Categorizing Data-Driven Methods for Test Scenario Generation to Assess Automated Driving Systems

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

Towards Realistic, Safety-Critical and Complete Test Case Catalogs for Safe Automated Driving in Urban Scenarios

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