Technical Evaluation and Impact Assessment of Automated Driving

Fahrenkrog, Felix; Rösener, Christian; Zlocki, Adrian; Eckstein, Lutz

doi:10.1007/978-3-319-40503-2_19

Cited by 5 publications

(5 citation statements)

References 6 publications

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“…Within different research projects and publications, different definitions of the term scenario are used due to different scopes of research. Fahrenkrog et al (2016) described a driving situation as a specific driving maneuver with detailed parameters that can be analyzed and simulated and a driving scenario as the general abstract description of a driving situation without any specification of the related parameters. A similar understanding has been established in Elrofai et al (2018), although using the terms scenario and scenario class.…”

Section: Scenario-based Validationmentioning

confidence: 99%

“…However, most scenarios within the projects are being defined based on experience. As, for instance, in Fahrenkrog et al (2016), scenarios are defined by factors that may be represented in any layer of the 6-layer model. Therefore, scenarios may not be disjunct or certain situations cannot be assigned to scenarios without ambiguity.…”

Section: Scenarios From Field Datamentioning

confidence: 99%

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A framework for definition of logical scenarios for safety assurance of automated driving

Weber

Bock

Klimke

et al. 2019

Traffic Injury Prevention

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Objective: In order to introduce automated vehicles on public roads, it is necessary to ensure that these vehicles are safe to operate in traffic. One challenge is to prove that all physically possible variations of situations can be handled safely within the operational design domain of the vehicle. A promising approach to handling the set of possible situations is to identify a manageable number of logical scenarios, which provide an abstraction for object properties and behavior within the situations. These can then be transferred into concrete scenarios defining all parameters necessary to reproduce the situation in different test environments. Methods: This article proposes a framework for defining safety-relevant scenarios based on the potential collision between the subject vehicle and a challenging object, which forces the subject vehicle to depart from its planned course of action to avoid a collision. This allows defining only safety-relevant scenarios, which can directly be related to accident classification. The first criterion for defining a scenario is the area of the subject vehicle with which the object would collide. As a second criterion, 8 different positions around the subject vehicle are considered. To account for other relevant objects in the scenario, factors that influence the challenge for the subject vehicle can be added to the scenario. These are grouped as action constraints, dynamic occlusions, and causal chains. Results: By applying the proposed systematics, a catalog of base scenarios for a vehicle traveling on controlled-access highways has been generated, which can directly be linked to parameters in accident classification. The catalog serves as a basis for scenario classification within the PEGASUS project. Conclusions: Defining a limited number of safety-relevant scenarios helps to realize a systematic safety assurance process for automated vehicles. Scenarios are defined based on the point of the potential collision of a challenging object with the subject vehicle and its initial position. This approach allows defining scenarios for different environments and different driving states of the subject vehicle using the same mechanisms. A next step is the generation of logical scenarios for other driving states of the subject vehicle and for other traffic environments. ARTICLE HISTORY

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Section: Scenario-based Validationmentioning

confidence: 99%

Section: Scenarios From Field Datamentioning

confidence: 99%

A framework for definition of logical scenarios for safety assurance of automated driving

Weber

Bock

Klimke

et al. 2019

Traffic Injury Prevention

Self Cite

View full text Add to dashboard Cite

show abstract

“…Furthermore, test-driven approaches will gain importance, requiring to start testing at the beginning of the life cycle. This supports efficient processes, in which one get maximum amount of [12], [13], [15] Test automation [9], [10], [15], [17] Standards and certification [8], [10], [13] Communication (e.g., V2I)…”

Section: Results: Challenges Of Testing Automated Vehicles a Idenmentioning

confidence: 79%

“…Furthermore, test-driven approaches will gain importance, requiring to start testing at the beginning of the life cycle. This supports efficient processes, in which one get maximum amount of Virtual testing and simulation [8]- [13], [15]-[17] Safety, reliability, and quality [8], [10]- [14], [17] Sensors and their models [8], [10]- [13], [15] Scenario complexity [10], [12], [15], [16] Handover/change of responsibility [8]- [11] Field and proving ground tests [9]- [11] Toolchain integration [12], [13], [15] Test automation [9], [10], [15], [17] Standards and certification [8], [10], [13] Communication (e.g., V2I)…”

Section: Results: State-of-the-practice Of Testing Active Safety Systemsmentioning

confidence: 97%

Paving the roadway for safety of automated vehicles: An empirical study on testing challenges

Knauss

Schröder

Berger

et al. 2017

2017 IEEE Intelligent Vehicles Symposium (IV)

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Abstract-The technology in the area of automated vehicles is gaining speed and promises many advantages. However, with the recent introduction of conditionally automated driving, we have also seen accidents. Test protocols for both, conditionally automated (e.g., on highways) and automated vehicles do not exist yet and leave researchers and practitioners with different challenges. For instance, current test procedures do not suffice for fully automated vehicles, which are supposed to be completely in charge for the driving task and have no driver as a back up. This paper presents current challenges of testing the functionality and safety of automated vehicles derived from conducting focus groups and interviews with 26 participants from five countries having a background related to testing automotive safety-related topics. We provide an overview of the state-of-practice of testing active safety features as well as challenges that needs to be addressed in the future to ensure safety for automated vehicles. The major challenges identified through the interviews and focus groups, enriched by literature on this topic are related to 1) virtual testing and simulation, 2) safety, reliability, and quality, 3) sensors and sensor models, 4) required scenario complexity and amount of test cases, and 5) handover of responsibility between the driver and the vehicle.

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“…Commonly used scenario building software include PreScan 2021.1.0 [21], VTD 2021.4 [22] and Carmaker 10.0 [23]. PreScan represents an integration of scenario building and control modeling capabilities within the realm of autonomous driving simulations.…”

Section: Introductionmentioning

confidence: 99%

Automatic Generation System for Autonomous Driving Simulation Scenarios Based on PreScan

Cao,

Feng,

Liu

et al. 2024

Applied Sciences

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The advancement of autonomous driving technology has urgently necessitated enhanced safety testing measures. Traditional road testing methods face significant challenges due to their high costs and prolonged durations. In response to the inefficiencies of manual scenario construction and the difficulties in selecting effective scenarios using common scenario generation methods in autonomous driving safety testing, this study proposes an innovative automatic SG system based on PreScan2021.1.0. The SG process is significantly simplified by this system’s capability to swiftly and accurately generate a vast array of specific scenarios through the input of scene parameters. The results indicate that this system achieves SG at a rate 2.5-fold faster than manual methods, alongside substantial improvements in accuracy. This system introduces a novel approach to virtual simulation, which is vital for the progress of autonomous driving safety. It offers a new paradigm for quickly and precisely generating test scenarios for autonomous driving.

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Technical Evaluation and Impact Assessment of Automated Driving

Cited by 5 publications

References 6 publications

A framework for definition of logical scenarios for safety assurance of automated driving

A framework for definition of logical scenarios for safety assurance of automated driving

Paving the roadway for safety of automated vehicles: An empirical study on testing challenges

Automatic Generation System for Autonomous Driving Simulation Scenarios Based on PreScan

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