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Latest revision as of 17:47, 28 January 2021

Abstract

dvanced driver assistance systems are designed to make driving easier that is, to alleviate the driver's workload, and to increase traffic safety. However, traffic safety is affected by negative behavioral adaptation, meaning that drivers tend to increase speed and pay less attention to driving when supported by an advanced assistance system. We relate behavioral adaptation to reinforcement learning at a subconscious level, and propose that driver assistance is dynamically varied within predetermined safety limits. The aim of employing a dynamic assistance policy is to prevent the driver from noticing a constant improvement in vehicle handling. We conclude by describing ongoing work for empirically evaluating an improved lane departure warning system that uses a dynamic assistance policy.

LITH-IKP-CR--05/1267--SE

Original document

The different versions of the original document can be found in:

• http://liu.diva-portal.org/smash/get/diva2:252726/FULLTEXT01

• http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-31903

• http://www.diva-portal.org/smash/record.jsf?pid=diva2:252726,

https://academic.microsoft.com/#/detail/2135230490

• http://xplorestaging.ieee.org/ielx5/10053/32246/01505171.pdf?arnumber=1505171,

http://dx.doi.org/10.1109/ivs.2005.1505171