Latest revision as of 06:08, 23 March 2021

Abstract

Purpose Advanced Driver Assistance Systems (ADAS) have been among the key innovations in the automotive market for over a decade, since they promote traffic safety. This tendency is strengthened even more lately, with the introduction of the autonomous vehicles. A plethora of ADAS exist in the market today, using common warning thresholds for all drivers. However, since we are not all driving the same way, by offering common systems for all the drivers, neither the acceptance nor the effectiveness levels of ADAS are optimal. This manuscript attempts to optimize the Collision Avoidance System (CAS) warning, through intelligent personalized algorithms. Methods Starting with the identification of the dynamic parameters for driving behaviour modeling on the longitudinal road axis, the personalization parameters for ADAS are derived that form the basis for the algorithms developed. Also, based on literature studies, the safety boundaries for warning provision by the CAS are set and implemented in the algorithms. Results Specific personalized algorithms for the longitudinal road axis behaviour are developed, based on Time to Collision and Time Headway. The proposed algorithms based on Time Headway were assessed on-road with 10 drivers and were positively evaluated by the majority of the participants, with a varying degree of reliability and usability. Conclusions Based on the results obtained, it can be concluded that with the proposed algorithms, the initial hypothesis of the paper is verified, i.e. personalised warnings would get a greater acceptance by the drivers, of course without braking the safety limits. Further improvements of the algorithm could be achieved, possibly through a better determination of the car following event, since its definition includes a few assumptions.

Document type: Article

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

• https://etrr.springeropen.com/track/pdf/10.1186/s12544-018-0324-6 under the license https://creativecommons.org/licenses/by/4.0

• http://link.springer.com/article/10.1186/s12544-018-0324-6,

https://doaj.org/toc/1867-0717,

https://doaj.org/toc/1866-8887 under the license cc-by

• http://link.springer.com/content/pdf/10.1186/s12544-018-0324-6.pdf,

http://link.springer.com/article/10.1186/s12544-018-0324-6/fulltext.html,

http://dx.doi.org/10.1186/s12544-018-0324-6

• https://etrr.springeropen.com/track/pdf/10.1186/s12544-018-0324-6,

https://link.springer.com/content/pdf/10.1186/s12544-018-0324-6.pdf,

https://link.springer.com/article/10.1186/s12544-018-0324-6,

https://etrr.springeropen.com/articles/10.1186/s12544-018-0324-6,

https://academic.microsoft.com/#/detail/2905103902 under the license https://creativecommons.org/licenses/by/4.0