This content is not included in
your SAE MOBILUS subscription, or you are not logged in.
Defining Fundamental Vehicle Actions for the Development of Automated Driving Systems
Technical Paper
2020-01-0712
ISSN: 0148-7191, e-ISSN: 2688-3627
This content contains downloadable datasets
Annotation ability available
Sector:
Language:
English
Abstract
Automated Driving Systems (ADSs) show great potential to improve our transport systems. Safety validation, before market launch, is challenging due to the large number of miles required to gather enough evidence for a proven in use argumentation. Hence there is ongoing research to find more effective ways of verifying and validating the safety of ADSs. It is crucial both for the design as well as the validation to have a good understanding of the environment of the ADS. A natural way of characterizing the external conditions is by modelling and analysing data from real traffic. Towards this end, we present a framework with the primary ultimate objective to completely model and quantify the statistically relevant actions that other vehicles conduct on motorways. Two categories of fundamental actions are identified by recognising that a vehicle can only move longitudinally and laterally. The fundamental actions are defined in detail to create a set that is collectively exhaustive and mutually exclusive. All physically possible combinatorial actions that can be constructed from the fundamental actions are presented. To increase the granularity of the modelling the combinatorial actions are proposed to be analysed as sequences. Further, multi-vehicle interactions, which capture correlations between actions from multiple vehicles, are discussed. The resulting modularity of the framework allows for performing statistical analysis at an arbitrary granularity to support the design of a performant ADS as well as creating applicable validation scenarios. The use of the framework is demonstrated by automatically identifying fundamental actions in field data. Identified trajectories of two types of actions are visualised and the distributions for one parameter characterising each action type are estimated.
Recommended Content
Authors
Topic
Citation
Gyllenhammar, M., Zandén, C., and Törngren, M., "Defining Fundamental Vehicle Actions for the Development of Automated Driving Systems," SAE Technical Paper 2020-01-0712, 2020, https://doi.org/10.4271/2020-01-0712.Data Sets - Support Documents
Title | Description | Download |
---|---|---|
Unnamed Dataset 1 | ||
Unnamed Dataset 2 |
Also In
References
- Kalra , N. and Paddock , S.M. Driving to Safety: How Many Miles of Driving Would It Take to Demonstrate Autonomous Vehicle Reliability? Transportation Research Part A: Policy and Practice 94 182 193 2016
- Gyllenhammar , M. et al. Towards an Operational Design Domain That Supports the Safety Argumentation of an Automated Driving System 10th European Congress of Embedded Real Time Systems Toulouse, France 2020
- Ulbrich , S. et al. Defining and Substantiating the Terms Scene, Situation, and Scenario for Automated Driving Proceedings of IEEE 18th International Conference on Intelligent Transportation Systems Las Palmas, Spain 2015
- Pütz , A. , Zlocki , A. , Bock , J. , and Eckstein , L. System Validation of Highly Automated Vehicles with a Database of Relevant Traffic Scenarios Situations 1 E5 2017
- Weber , H. et al. A Framework for Definition of Logical Scenarios for Safety Assurance of Automated Driving Traffic Injury Prevention 20 S65 S70 2018
- Elrofai , H. et al. StreetWise Scenario-Based Safety Validation of Connected and Automated Driving TNO 2018
- Wheeler , T. and Kochenderfer , M. Factor Graph Scene Distributions for Automotive Safety Analysis IEEE 19th International Conference on Intelligent Transportation Systems (ITSC) Rio de Janeiro 2016
- Koopman , P. and Fratrik , F. How Many Operational Design Domains, Objects, and Events? Safe AI 2019: AAAI Workshop on Artificial Intelligence Safety 2019
- Czarnecki , K. Operational World Model Ontology for Automated Driving Systems--Part 1: Road Structure Waterloo Waterloo Intelligent Systems Engineering Lab (WISE) Report 2018
- Czarnecki , K. Operational World Model Ontology for Automated Driving Systems--Part 2: Road Users Waterloo Waterloo Intelligent Systems Engineering Lab (WISE) Report 2018
- Bagschik , G. , Menzel , T. and Maurer , M. Ontology based Scene Creation for the Development of Automated Vehicles Proceedings of 2018 IEEE Intelligent Vehicles Symposium (IV) Changshu, China 2018
- Bach , J. , Otten , S. , and Sax , E. Model Based Scenario Specification for Development and Test of Automated Driving Functions IEEE Intelligent Vehicles Symposium (IV) Gothenburg 2016
- Queiroz , R. , Berger , T. , and Czarnecki , K. GeoScenario: An Open DSL for Autonomous Driving Scenario Representation IEEE Intelligent Vehicles Symposium (IV) Paris 2019
- Freemont , D. et al. Scenic: Language-Based Scene Generation Berkeley University of California at Berkeley 2018
- Rösener , C. , Fahrenkrog , F. , Uhlig , A. , and Eckstein , L. A Scenario-Based Assessment Approach for Automated Driving by Using Time Series Classification of Human-Driving Behaviour IEEE 19th International Conference on Intelligent Transportation Systems (ITSC) Rio de Janeiro, Brazil 2016
- Koopman , P. and Wagner , M. Toward a Framework for Highly Automated Vehicle Safety Validation SAE Technical Paper 2018-01-1071 2018 https://doi.org/10.4271/2018-01-1071
- Shalev-Shwartz , S. , Shammah , S. , and Shashua , A. October 21, 2018 https://arxiv.org/pdf/1708.06374.pdf
- SAE 2018