Abstract Simulation Scenario Generation for Autonomous Vehicle Verification

Medrano-Berumen, Christopher; Akbaş, Mustafa İlhan

doi:10.1109/southeastcon42311.2019.9020575

Cited by 17 publications

(12 citation statements)

References 7 publications

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“…Huo et al established a three-dimensional traffic scene framework based on semantic segmentation logic [26]. Berumen et al proposed an abstract simulation scenario generation framework considering both autonomous vehicle safety and computational cost [27]. Similar researches can also be found in [28,29].…”

Section: Traffic Scene Establishmentmentioning

confidence: 94%

Traffic Risk Modelling and Analysis under Airport-Like Simulation Environment

Sheng¹,

Feng

Biancardo

2022

Journal of Advanced Transportation

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Traffic safety plays a crucial role in the development of autonomous vehicles which attracts significant attention in the community. It is a challenge task to ensure autonomous vehicle safety under varied traffic environment interference, especially for airport-like closed-loop conditions. To that aim, we analyze autonomous vehicle safety at typical roadway conditions and traffic state constraints (e.g., car-following state at different speed distributions) by simulating the airport-like traffic conditions. The experimental results suggest that traffic collision risk is in a positive relationship with the speed difference and distance among adjacent vehicles. More specifically, the autonomous vehicle may collide with neighbors when the time to collision (TTC) indicator is lower than 4 s, and vice versa. The research findings can help both research community and practioners obtain additional information for improving traffic safety for autonomous vehicles.

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Section: Traffic Scene Establishmentmentioning

confidence: 94%

Traffic Risk Modelling and Analysis under Airport-Like Simulation Environment

Sheng¹,

Feng

Biancardo

2022

Journal of Advanced Transportation

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“…The simulation framework collects the test data and evaluates each run. Environmental conditions are not considered, as they don't aim at testing the full vehicle stack and focus only on testing the decision component of an autonomous vehicle [28].…”

Section: A Scenario-based Validationmentioning

confidence: 99%

“…The simulation scenario generation framework of Medrano-Berumen and Akbas [28] uses a matrix where each row represents a road piece or actor in a semantic string. To define a road piece the string uses parameters such as road piece type, road length, number of lanes, speed limit and intersection pattern.…”

Section: A Scenario-based Validationmentioning

confidence: 99%

From Traffic Sensor Data To Semantic Traffic Descriptions: The Test Area Autonomous Driving Baden-Württemberg Dataset (TAF-BW Dataset)

Zipfl

Fleck

Zofka

et al. 2020

2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)

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The verification and validation of autonomous driving vehicles remains a major challenge due to the high complexity of autonomous driving functions. Scenario-based testing is a promising method for validating such a complex system. Ontologies can be utilized to produce test scenarios that are both meaningful and relevant. One crucial aspect of this process is selecting the appropriate method for describing the entities involved. The level of detail and specific entity classes required will vary depending on the system being tested. It is important to choose an ontology that properly reflects these needs.This paper summarizes key representative ontologies for scenario-based testing and related use cases in the field of autonomous driving. The considered ontologies are classified according to their level of detail for both static facts and dynamic aspects. Furthermore, the ontologies are evaluated based on the presence of important entity classes and the relations between them.

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“…Looking at this research trend for safety evaluation of autonomous driving, Christopher et al conducted a study on generating abstract simulation scenarios for verification of autonomous vehicles [6], Mansur et al proposed an accelerated approach for testing considering the safety and efficiency of autonomous vehicles on public roads [7]. Tim et al proposed an open-source graphical user interface for generating multi-vehicle scenarios [8].…”

Section: Introductionmentioning

confidence: 99%

A Study on the Evaluation Method of Autonomous Vehicle for Fixed Targets

Kim¹,

Lee²

2021

Preprint

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Recently, the automobile industry aims to commercialize autonomous vehicles, standardization and research and development are actively underway based on the specifications of automotive electronic control systems and the verification of functional errors. Based on this, autonomous driving technology is becoming more advanced, and it is preparing for an era of full autonomous driving through V2X technology convergence. It is also, expanding the models of ADAS applied vehicles such as FCW, BSD, LCA. Based on this, research is also actively underway to secure safety of vehicles and pedestrians, such as V2X, Localization, Fail Safe, to supplement the limitations of sensor-based autonomous driving. In this regard, this study proposes a theoretical formula for longitudinal relative distance computation for autonomous vehicles evaluation method, and uses test device such as DGPS to collects and verify data. In addition, a scenario was proposed for the fixed target, based on this, four types of test were formed to conduct the actual test, and the relative distances were compared, analyzed and verified. Comparative analysis results, in the second test of the first test scenario, the avoidance test of the fixed target in driving own lane, the minimum error rate was 0.5%, and in the second test of fourth test scenario, the avoidance test of the fixed target in driving own lane, the maximum error rate was 7.4%. The main cause of error is, it was judged as an error due to sensor recognition, depending on the scenario progress method, the condition of the test path, and the weather such as sunlight. In the future, we plan to conduct an evaluation on moving target.

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Abstract Simulation Scenario Generation for Autonomous Vehicle Verification

Cited by 17 publications

References 7 publications

Traffic Risk Modelling and Analysis under Airport-Like Simulation Environment

Traffic Risk Modelling and Analysis under Airport-Like Simulation Environment

From Traffic Sensor Data To Semantic Traffic Descriptions: The Test Area Autonomous Driving Baden-Württemberg Dataset (TAF-BW Dataset)

A Study on the Evaluation Method of Autonomous Vehicle for Fixed Targets

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