This content is not included in
your SAE MOBILUS subscription, or you are not logged in.
Toward a Framework for Highly Automated Vehicle Safety Validation
Technical Paper
2018-01-1071
ISSN: 0148-7191, e-ISSN: 2688-3627
This content contains downloadable datasets
Annotation ability available
Sector:
Language:
English
Abstract
Validating the safety of Highly Automated Vehicles (HAVs) is a significant autonomy challenge. HAV safety validation strategies based solely on brute force on-road testing campaigns are unlikely to be viable. While simulations and exercising edge case scenarios can help reduce validation cost, those techniques alone are unlikely to provide a sufficient level of assurance for full-scale deployment without adopting a more nuanced view of validation data collection and safety analysis. Validation approaches can be improved by using higher fidelity testing to explicitly validate the assumptions and simplifications of lower fidelity testing rather than just obtaining sampled replication of lower fidelity results. Disentangling multiple testing goals can help by separating validation processes for requirements, environmental model sufficiency, autonomy correctness, autonomy robustness, and test scenario sufficiency. For autonomy approaches with implicit designs and requirements, such as machine learning training data sets, establishing observability points in the architecture can help ensure that vehicles pass the right tests for the right reason. These principles could improve both efficiency and effectiveness for demonstrating HAV safety as part of a phased validation plan that includes both a “driver test” and lifecycle monitoring as well as explicitly managing validation uncertainty.
Recommended Content
Citation
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.Data Sets - Support Documents
Title | Description | Download |
---|---|---|
Unnamed Dataset 1 |
Also In
References
- Koopman , P. and Wagner , M. Autonomous Vehicle Safety: An Interdisciplinary Challenge IEEE Intelligent Transportation Systems Magazine 9 1 90 96 2017
- 2017
- Carson , B. 2017
- 2017 https://goo.gl/7HUiew
- General Motors 2018 Self-Driving Safety Report 2018 https://goo.gl/ruLJvV
- http://www.sae.org/misc/pdfs/automated_driving.pdf
- Wagner and Koopman A Philosophy for Developing Trust in Self-Driving Cars Meyer G. , Beiker S. Road Vehicle Automation 2, Lecture Notes in Mobility Springer 2015 163 170
- Salay , R. , Queioz , R. , & Czarnecki , K. https://arxiv.org/pdf/1709.02435.pdf
- Dosovitskiy , A. , and T. Brox 2015
- Koopman , P. and Wagner , M. Challenges in Autonomous Vehicle Testing and Validation SAE Int. J. Trans. Safety 4 1 15 24 2016 10.4271/2016-01-0128
- Urmson , C. et al. Autonomous driving in urban environments: Boss and the Urban Challenge Journal of Field Robotics 425 466 2008 10.1002/rob
- Levinson et al.
- Broggi et al. Extensive tests of autonomous driving technologies IEEE Trans. Intelligent Transportation Systems 14 3 1403 1415 Sept. 2013
- Ziegler , J. et al. 2014
- Aeberhard , M. et al. 2015
- Kalra , N. , and Paddock , S. 2016
- Butler and Finelli The infeasibility of experimental quantification of life-critical software reliability IEEE Trans. SW Engr. 19 1 3 12 Jan 1993
- Madrigal , A. 2017
- Ding , Z. http://www-personal.umich.edu/~zhaoding/accelerated-evaluation.html
- Golson , J. 2016
- Davies , A. 2017
- Box , G. 1979
- Putz , A. , Zlocki , A. , Bock , J. , and Eckstein , L. 2017
- Bustcon , J. , & Randell , B. Software Engineering Techniques: report on a conference sponsored by the NATO Science Committee April 1970
- Beizer , B. Black-Box Testing: Techniques for functional testing of software and systems Wiley 1995
- Zhou , N. 2017 https://goo.gl/jgA7Ck
- Koopman , P. 2017
- Kane , Chowdhury , Datta , and Koopman 2015
- Sargent , R. 118 131
- Law , A. and Kelton , W.D. Simulation Modeling and Analysis 3rd McGraw Hill 2000
- Freedman , R. IEEE Trans. Software Engineering 553 564 June 1991
- Dragan , A. , Lee , K. , and Srinivasa , S. 2013 301 308
- Bojarski , M. et al.
- Bojarski , M. et al.
- Wang , Y. , Lin , Z. , Shen , X. , Cohen , S. , and Cottrell , G.
- Redmon , J. , and Farhadi , A. https://arxiv.org/pdf/1612.08242.pdf
- Morris , E. https://goo.gl/Pv7SB7
- Wang , R. , Guiochet , J. & Motet , G. 2017 55 68
- Casner , S. , Hutchins , E. , & Norman , D. 2016 70 77
- Leveson IEEE Computer 1993
- Sullivan , M. , and Chillarege , R. 1991
- Kalra , N. and Groves , D. The Enemy of Good: estimating the cost of waiting for nearly perfect automated vehicles Rand Corporation 2017
- Burton , S. 2017 5 16
- Kane , Fuhrman , Koopman 2014