Collection of Requirements and Model-based Approach for Scenario Description

Braun, Thilo; Ries, Lennart; Körtke, Franziska; Turner, Lara Ruth; Otten, Stefan; Sax, Eric

doi:10.5220/0010478706340645

Cited by 2 publications

(4 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Approximately 68% of the data used in the reviewed methods were different types of real driving data that were used as inputs for the generation process. 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%

“…A rarely used data source is a dataset of pre-crafted scenarios or trajectories [20], [23], [68], [73]. Accident databases, as data sources, share approximately 26% of the identified methods, which, in most cases, originate from a set of police accident reports [19], [24], [28]- [30], [33], [35], [43], [46], [47], [55], [61], [67], [69], [70], [75], [76].…”

Section: B Categorizationmentioning

confidence: 99%

See 1 more Smart Citation

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

Bäumler,

Linke,

Prokop

2024

IEEE Access

View full text Add to dashboard Cite

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

show abstract

Section: B Categorizationmentioning

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

View full text Add to dashboard Cite

show abstract

“…This is also the understanding dominating in literature. According to Braun et al [31], maneuvers can be categorized based on their direction, the presence of interaction with other traffic participants, their start and end conditions as well as their hierarchical level (see below). Start conditions can be either triggered or implicit, end conditions self-terminating or indefinite.…”

Section: Introductionmentioning

confidence: 99%

“…• Which start and end conditions to select? For the sake of flexibility, an advantageous choice of maneuvers could be a set of atomic behaviors from which every higher-level maneuver can be derived as proposed in [30], [31] and [34]. This would partially answer the first question.…”

Section: Introductionmentioning

confidence: 99%

Literature Review on Maneuver-Based Scenario Description for Automated Driving Simulations

Neis,

Beyerer

2023

2023 IEEE Intelligent Vehicles Symposium (IV)

View full text Add to dashboard Cite

The increasing complexity of automated driving functions and their growing operational design domains imply more demanding requirements on their validation. Classical methods such as field tests or formal analyses are not sufficient anymore and need to be complemented by simulations. For simulations, the standard approach is scenario-based testing, as opposed to distance-based testing primarily performed in field tests. Currently, the time evolution of specific scenarios is mainly described using trajectories, which limit or at least hamper generalizations towards variations. As an alternative, maneuverbased approaches have been proposed. We shed light on the state of the art and available foundations for this new method through a literature review of early and recent works related to maneuver-based scenario description. It includes related modeling approaches originally developed for other applications. Current limitations and research gaps are identified.Index Terms-automated driving, AI, maneuver-based, simulation, scenario-based testing *This publication was written in the context of the AVEAS research project (www.aveas.org), funded by the German Federal Ministry for Economic Affairs and Climate Action (BMWK) within the program "New Vehicle and System Technologies".

show abstract

Collection of Requirements and Model-based Approach for Scenario Description

Cited by 2 publications

References 0 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

Literature Review on Maneuver-Based Scenario Description for Automated Driving Simulations

Contact Info

Product

Resources

About