Virtual validation of autonomous vehicle safety through simulation-based testing

  • Conference paper
  • First Online:
20. Internationales Stuttgarter Symposium

Part of the book series: Proceedings ((PROCEE))

  • 2118 Accesses

Zusammenfassung

Full validation of the safety of an autonomous vehicle requires excessive amounts of testing and driving hours, which is impossible to achieve without virtual simulations. Therefore, companies seek efficient and smart testing strategies to virtually validate the safety of autonomous vehicle functions aiming to achieve lower costs and less time to market. Simulation-based testing for autonomous and connected vehicles is essential for the future generation of transportation systems.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
GBP 19.95
Price includes VAT (United Kingdom)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
GBP 95.50
Price includes VAT (United Kingdom)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
GBP 119.99
Price includes VAT (United Kingdom)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

Literatur

  1. SAMSUNG Business, “The Connected Vehicle Comes of Age”, Featuring research from Gartner, 2015.

    Google Scholar 

  2. Bernard J. Arseneau, Santanu Roy, Joshua Salazar, Joey Yang, “Autonomous and Connected Vehicles Preparing for the Future of Surface Transportation”, HDR, 2015.

    Google Scholar 

  3. John Bradburn, David Williams, Rob Piechocki, Kat Hermans, “Connected & Autonomous Vehicles Introducing the Future of Mobility”, Atkins and Intelligent Mobility, 2015.

    Google Scholar 

  4. Juez, G., Amparan, E., Lattarulo, R., Rastelli, J.P., Ruiz, A., and Espinoza, H. (2017). Safety assessment of automated vehicle functions by simulation-based fault injection. In 2017 IEEE International Conference on Vehicular Electronics and Safety (ICVES), 214-219, doi:10:1109/ICVES:2017:7991928.

    Google Scholar 

  5. Silveira, A.M., Arajo, R.E., and de Castro, R. (2012). Fieev: A co-simulation framework for fault injection in electrical vehicles. In 2012 IEEE International Conference on Vehicular Electronics and Safety (ICVES 2012), 357-362. doi: 10:1109/ICVES:2012:6294254.

    Google Scholar 

  6. Krajzewicz, D., Erdmann, J., Behrisch, M., and Bieker, L. (2012). Recent development and applications of SUMO - Simulation of Urban MObility. International Journal On Advances in Systems and Measurements, 5(3&4), 128-138.

    Google Scholar 

  7. Backfrieder, C., Mecklenbruker, C.F., and Ostermayer, G. (2013). TraffSim : a traffic simulator for investigating benefits ensuing from intelligent traffic management. In 2013 European Modelling Symposium, 451-456. doi: 10:1109/EMS:2013:76.

    Google Scholar 

  8. Treiber, M. and Kesting, A. (2010). An open-source microscopic traffic simulator. IEEE Intelligent Transportation Systems Magazine, 2(3), 6-13. doi: 10:1109/MITS:2010:939208.

    Google Scholar 

  9. Saraoglu, Mustafa, Andrey Morozov, and Klaus Janschek. “MOBATSim: MOdel-Based Autonomous Traffic Simulation Framework for Fault-Error-Failure Chain Analysis.” IFAC-PapersOnLine 52.8 (2019): 239-244.

    Google Scholar 

  10. Minnerup, P. M. (2017). An Efficient Method for Testing Autonomous Driving Software against Nondeterministic Influences (Doctoral dissertation, Technische Universität München).

    Google Scholar 

  11. Saraoğlu, Mustafa, et al. “ErrorSim: A tool for error propagation analysis of simulink models.” International Conference on Computer Safety, Reliability, and Security. Springer, Cham, 2017.

    Google Scholar 

  12. ISO 26262-2:2018(en) Road vehicles — Functional safety. International Standardization Organization.

    Google Scholar 

  13. D. N. Lee. A theory of visual control of braking based on information about time to collision. Perception, 1976.

    Google Scholar 

  14. Kristofer D. Kusano and Hampton C. Gabler. Safety benefits of forward collision warning, brake assist, and autonomous braking systems in rearend collisions. IEEE Transactions on Intelligent Transportation Systems, 13(4):1546–1555, 2012.

    Google Scholar 

  15. Praprut Songchitruksa and Andrew P. Tarko. The extreme value theory approach to safety estimation. Accident Analysis and Prevention, 38(4):811– 822, 2006.

    Google Scholar 

  16. Michiel M. Minderhoud and Piet H.L. Bovy. Extended time-to-collision measures for road traffic safety assessment. Accident Analysis and Prevention, 33(1):89–97, 2001.

    Google Scholar 

  17. Qiang Meng and **xian Weng. Evaluation of rear-end crash risk at work zone using work zone traffic data. Accident Analysis and Prevention, 43(4):1291–1300, 2011.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Saraoğlu, M., Shi, Q., Morozov, A., Janschek, K. (2020). Virtual validation of autonomous vehicle safety through simulation-based testing. In: Bargende, M., Reuss, HC., Wagner, A. (eds) 20. Internationales Stuttgarter Symposium . Proceedings. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-29943-9_33

Download citation

Publish with us

Policies and ethics

Navigation