Area-wide real-world test scenarios of poor visibility for safe development of automated vehicles

Winkle, Thomas; Erbsmehl, Christian; Bengler, Klaus

doi:10.1186/s12544-018-0304-x

Cited by 12 publications

(9 citation statements)

References 5 publications

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“…The process of ensuring the safety of AVs is considered to be an interdisciplinary challenge [33], [84]. It is crucial to have a sound engineering process to be followed by contributors with diverse skills and areas of expertise.…”

Section: Verification and Validation Methodologiesmentioning

confidence: 99%

Software Verification and Validation of Safe Autonomous Cars: A Systematic Literature Review

et al. 2021

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Section: Verification and Validation Methodologiesmentioning

confidence: 99%

Software Verification and Validation of Safe Autonomous Cars: A Systematic Literature Review

et al. 2021

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“…The fourth step is to identify and extract the scenarios in the collected data using pre-defined scenarios (7x) or scenarios extracted by rule-based (28x), unsupervised (7x) or supervised approaches (5x) -or any combination of them (12x). Pre-defined scenarios [33], [37], [43], [64], [70], [71] are often extracted from accident data because every recorded accident can be considered a scenario. Rule-based approaches (see Table A1) rely on clearly defined parameter ranges or thresholds to identify scenarios and often require extracted road user specific trajectories from, e.g., video material using Convolutional Neural Networks (CNNs) [127].…”

Section: A Process Of Scenario Generationmentioning

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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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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“…Aparicio et al [2] summarized the types of conflicts between cars and cars, cars and pedestrians. Winkle et al [49] analyzed accident data in which the line of sight was blocked in different weather conditions from 2004 to 2014, and analyzed the severity of the accident.…”

Section: Dangerous Scenario Generationmentioning

confidence: 99%

A Scenario-Based Development Framework for Autonomous Driving

2020

Preprint

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This article summarizes the research progress of scenario-based testing and development technology for autonomous vehicles. We systematically analyzed previous research works and proposed the definition of scenario, the elements of the scenario ontology, the data source of the scenario, the processing method of the scenario data, and scenario-based V-Model. Moreover, we summarized the automated test scenario construction method by random scenario generation and dangerous scenario generation.

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Area-wide real-world test scenarios of poor visibility for safe development of automated vehicles

Cited by 12 publications

References 5 publications

Software Verification and Validation of Safe Autonomous Cars: A Systematic Literature Review

Software Verification and Validation of Safe Autonomous Cars: A Systematic Literature Review

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

A Scenario-Based Development Framework for Autonomous Driving

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