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Validation of Collision Frequency Estimation Using Extreme Value Theory

Daniel Åsljung (Institutionen för elektroteknik, Mekatronik) ; Jonas Nilsson ; Jonas Fredriksson (Institutionen för elektroteknik, Mekatronik)
Proceedings of the IEEE Intelligent Transportation Systems Conference, 2017 p. 1857-1862. (2017)
[Konferensbidrag, refereegranskat]

There is a lot of focus right now on how to build an autonomous vehicle, which can handle all the situations that a human driver is experiencing. Less is done on how to ensure that these vehicles are safe enough to be released to the public. Using traditional statistical methods would require one to drive extensive distances without incidents to prove the safety to a sufficient degree. Recent research has shown the possibility of using near-collisions in order to estimate the frequency of actual collisions using Extreme Value Theory. In order to trust these estimations, the precision of these estimates needs to be validated. The results from a 250 000 km field test shows that the Extreme Value estimations are reasonable in relation to a crash statistics estimate for rear-end collisions. This further suggests that extreme value is a method that can be used to predict collision frequencies from data containing no collisions.

Nyckelord: Automotive, Autonomous vehicles, Safety, Statistical inference, Verification & Validation



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Denna post skapades 2017-11-16. Senast ändrad 2017-11-29.
CPL Pubid: 253210