research-article

GeoScenario: An Open DSL for Autonomous Driving Scenario Representation

Authors:

Rodrigo Queiroz,

Thorsten Berger,

Krzysztof CzarneckiAuthors Info & Claims

2019 IEEE Intelligent Vehicles Symposium (IV)

Pages 287 - 294

https://doi.org/10.1109/IVS.2019.8814107

Published: 09 June 2019 Publication History

Abstract

Automated Driving Systems (ADS) require extensive evaluation to assure acceptable levels of safety before they can operate in real-world traffic. Although many tools are available to perform such tests in simulation, the lack of a language to formally capture test scenarios that cover the complexity of road traffic situations hinders the reproducibility of tests and impairs the exchangeability between tools. We propose GeoScenario as a Domain-Specific Language (DSL) for scenario representation to substantiate test cases in simulation. By adopting GeoScenario in the simulation infrastructure of a self-driving car project, we use the language in practice to test an autonomy stack in simulation. The language was built on top of the well-known Open Street Map standard, and designed to be simple and extensible.

References

[1]

Java Open Street Map Editor. https://josm.openstreetmap.de.

[2]

Open Street Map (OSM). https://www.openstreetmap.org.

[3]

OpenDRIVE. https://www.opendrive.com.

[4]

OpenScenario. https://www.asam.net/standards/detail/openscenario.

[5]

Robot Operating System (ROS). https://www.ros.org/.

[6]

Virtual Test Drive (VTD). https://vires.com/vtd-vires-virtual-test-drive.

[7]

R. Ben Abdessalem, S. Nejati, L. C. Briand, and T. Stifter. Testing advanced driver assistance systems using multi-objective search and neural networks. In 2016 31st IEEE/ACM International Conference on Automated Software Engineering (ASE), pages 63–74, Sep. 2016.

[8]

Raja Ben Abdessalem, Shiva Nejati, Lionel C. Briand, and Thomas Stifter. Testing vision-based control systems using learnable evolutionary algorithms. In Proceedings of the 40th International Conference on Software Engineering, ICSE '18, pages 1016–1026. ACM, 2018.

[9]

M. Althoff, M. Koschi, and S. Manzinger. Commonroad: Composable benchmarks for motion planning on roads. In 2017 IEEE Intelligent Vehicles Symposium (IV), pages 719–726, June 2017.

[10]

Gerrit Bagschik, Till Menzel, and Markus Maurer. Ontology based scene creation for the development of automated vehicles. In 2018 IEEE Intelligent Vehicles Symposium (IV), pages 1813–1820. IEEE, 2018.

Digital Library

[11]

Michael Behrisch, Laura Bieker, Jakob Erdmann, and Daniel Kra-jzewicz. Sumo-simulation of urban mobility: An overview. In SIMUL 2011, The Third International Conference on Advances in System Simulation, pages 63–68, 2011.

[12]

P. Bender, J. Ziegler, and C. Stiller. Lanelets: Efficient map representation for autonomous driving. In 2014 IEEE Intelligent Vehicles Symposium Proceedings, pages 420–425, June 2014.

[13]

Oliver Bhler and Joachim Wegener. Automatic testing of an autonomous parking system using evolutionary computation. SAE Technical Papers, 03 2004.

[14]

Krzysztof Czarnecki. Operational world model ontology for automated driving systems-part 1: Road structure. Technical report, Waterloo Intelligent Systems Engineering Lab (WISE) Report, 2018.

[15]

Alexey Dosovitskiy, German Ros, Felipe Codevilla, Antonio Lopez, and Vladlen Koltun. CARLA: An open urban driving simulator. In Proceedings of the 1st Annual Conference on Robot Learning, pages 1–16, 2017.

[16]

S. Geyer, M. Baltzer, B. Franz, S. Hakuli, M. Kauer, M. Kienle, S. Meier, T. Weissgerber, K. Bengler, R. Bruder, F. Flemisch, and H. Winner. Concept and development of a unified ontology for generating test and use-case catalogues for assisted and automated vehicle guidance. IET Intelligent Transport Systems, 8 (3):183–189, 2014.

[17]

Kentaro Go and John M. Carroll. The blind men and the elephant: Views of scenario-based system design. Interactions, 11 (6):44–S3, November 2004.

Digital Library

[18]

ISO/FDIS 26262:1994. Road vehicles-Functional safety. ISO, Geneva, Switzerland, 2011.

[19]

Title = Road vehicles-Safety of the intended functionality year = 2019 ISO/PAS-21448:2019, publisher = ISO, Geneva, Switzerland.

[20]

Matthias Jarke, X. Tung Bui, and John M. Carroll. Scenario management: An interdisciplinary approach. Requirements Engineering, 3 (3): 155–173, Mar 1998.

[21]

V. G. Kovvali, V. Alexiadis, and L. Zhang. Video-based vehicle trajectory data collection. In Proc. of the Transportation Research Board 86th Annual Meeting, 2007.

[22]

Till Menzel, Gerrit Bagschik, and Markus Maurer. Scenarios for development, test and validation of automated vehicles. In 2018 IEEE Intelligent Vehicles Symposium, IV 2018, Changshu, Suzhou, China, June 26-30, 2018, pages 1821–1827, 2018.

[23]

W. G. Najm, John D. Smith, and Mikio Yanagisawa. Pre-Crash Scenario Topology for Crash Avoidance Research. Technical report, U. S. Department of Transportation, NHTSA, April 2007.

[24]

W. G. Najm, S. Toma, and J. Brewer. Depiction of Priority Light-Vehicle Pre-Crash Scenarios for Safety Applications Based on Vehicle-to-Vehicle Communications. Technical report, U. S. Department of Transportation, NHTSA, April 2013.

[25]

Vincenzo Punzo, Maria Teresa Borzacchiello, and Biagio Ciuffo. On the assessment of vehicle trajectory data accuracy and application to the next generation simulation (NGSIM) program data. Transportation Research Part C: Emerging Technologies, 19 (6): 1243–1262, 2011.

[26]

E. Rohmer, S. P. N. Singh, and M. Freese. V-rep: A versatile and scalable robot simulation framework. In 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, pages 1321–1326, 2013.

[27]

SAE. Taxonomy and definitions for terms related to on-road motor vehicle automated driving systems (sae j3016). Technical report, SAE International, 2014.

[28]

SAE. Operational definitions of driving performance measures and statistics (sae j2944). Technical report, SAE International, 2015.

[29]

J. Sayer, D. LeBlanc, S. Bogard, D. Funkhouser, S. Bao, M. L. Buonarosa, and A. Blankespoor. Integrated Vehicle-Based Safety Systems Field Operational Test Final Program Report. Technical report, The University of Michigan Transportation Research Institute (UMTRI), June 2011.

[30]

Chris Schwarz. On computing time-to-collision for automation scenarios. Transportation Research Part F: Traffic Psychology and Behaviour, 27:283–294, 2014.

[31]

Shital Shah, Debadeepta Dey, Chris Lovett, and Ashish Kapoor. Airsim: High-fidelity visual and physical simulation for autonomous vehicles. CoRR, 2017.

[32]

S. Ulbrich, T. Menzel, A. Reschka, F. Schuldt, and M. Maurer. Defining and substantiating the terms scene, situation, and scenario for automated driving. In 2015 IEEE 18th International Conference on Intelligent Transportation Systems, pages 982–988, Sept 2015.

[33]

UMTRI. Safety Pilot Model Deployment. Technical report, The University of Michigan Transportation Research Institute (UMTRI), (Accessed: 20Dez2017).

[34]

Richard van der horst and Jeroen Hogema. Time-to-collision and collision avoidance systems. 01 1994.

[35]

Wenshuo Wang and Ding Zhao. Extracting traffic primitives directly from naturalistically logged data for self-driving applications. IEEE Robotics and Automation Letters, 2017.

Cited By

Dai JGao BLuo MHuang ZLi ZZhang YYang MRoychoudhury APaiva AAbreu RStorey M(2024)SCTrans: Constructing a Large Public Scenario Dataset for Simulation Testing of Autonomous Driving SystemsProceedings of the IEEE/ACM 46th International Conference on Software Engineering10.1145/3597503.3623350(1-13)Online publication date: 20-May-2024
https://dl.acm.org/doi/10.1145/3597503.3623350
Guo AFeng YCheng YChen Z(2024)Semantic-guided fuzzing for virtual testing of autonomous driving systemsJournal of Systems and Software10.1016/j.jss.2024.112017212:COnline publication date: 1-Jun-2024
https://dl.acm.org/doi/10.1016/j.jss.2024.112017
Entekhabi SMostowski WMousavi M(2023)Automated and Efficient Test-Generation for Grid-Based Multiagent Systems: Comparing Random Input Filtering versus Constraint SolvingACM Transactions on Software Engineering and Methodology10.1145/362473633:1(1-32)Online publication date: 23-Nov-2023
https://dl.acm.org/doi/10.1145/3624736
Show More Cited By

Index Terms

GeoScenario: An Open DSL for Autonomous Driving Scenario Representation
1. Computing methodologies
  1. Modeling and simulation
    1. Simulation support systems
2. Software and its engineering

Index terms have been assigned to the content through auto-classification.

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cover image Guide Proceedings

2019 IEEE Intelligent Vehicles Symposium (IV)

Jun 2019

2358 pages

Copyright © 2019.

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IEEE Press

Publication History

Published: 09 June 2019

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Cited By

Dai JGao BLuo MHuang ZLi ZZhang YYang MRoychoudhury APaiva AAbreu RStorey M(2024)SCTrans: Constructing a Large Public Scenario Dataset for Simulation Testing of Autonomous Driving SystemsProceedings of the IEEE/ACM 46th International Conference on Software Engineering10.1145/3597503.3623350(1-13)Online publication date: 20-May-2024
https://dl.acm.org/doi/10.1145/3597503.3623350
Guo AFeng YCheng YChen Z(2024)Semantic-guided fuzzing for virtual testing of autonomous driving systemsJournal of Systems and Software10.1016/j.jss.2024.112017212:COnline publication date: 1-Jun-2024
https://dl.acm.org/doi/10.1016/j.jss.2024.112017
Entekhabi SMostowski WMousavi M(2023)Automated and Efficient Test-Generation for Grid-Based Multiagent Systems: Comparing Random Input Filtering versus Constraint SolvingACM Transactions on Software Engineering and Methodology10.1145/362473633:1(1-32)Online publication date: 23-Nov-2023
https://dl.acm.org/doi/10.1145/3624736
Tang SZhang ZZhang YZhou JGuo YLiu SGuo SLi YMa LXue YLiu Y(2023)A Survey on Automated Driving System Testing: Landscapes and TrendsACM Transactions on Software Engineering and Methodology10.1145/357964232:5(1-62)Online publication date: 24-Jul-2023
https://dl.acm.org/doi/10.1145/3579642
Li BDu DChen SWei MZheng CZhang X(2022)SML4ADS: An Open DSML for Autonomous Driving Scenario Representation and GenerationProceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering10.1145/3551349.3561169(1-3)Online publication date: 10-Oct-2022
https://dl.acm.org/doi/10.1145/3551349.3561169
Shah TLepird JHartnett ADolan J(2021)A Simulation-Based Benchmark for Behavioral Anomaly Detection in Autonomous Vehicles2021 IEEE International Intelligent Transportation Systems Conference (ITSC)10.1109/ITSC48978.2021.9565042(2074-2081)Online publication date: 19-Sep-2021
https://dl.acm.org/doi/10.1109/ITSC48978.2021.9565042
Xinxin ZFei LXiangbin W(2020)CSG: Critical Scenario Generation from Real Traffic Accidents2020 IEEE Intelligent Vehicles Symposium (IV)10.1109/IV47402.2020.9304609(1330-1336)Online publication date: 19-Oct-2020
https://dl.acm.org/doi/10.1109/IV47402.2020.9304609

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