Towards Adapting Cars to their Drivers
AbstractTraditionally, vehicles have been considered as machines that are controlled by humans for the purpose of transportation. A more modern view is to envision drivers and passengers as actively interacting with a complex automated system. Such interactive activity leads us to consider intelligent and advanced ways of interaction leading to cars that can adapt to their drivers.In this paper, we focus on the Adaptive Cruise Control (ACC) technology that allows a vehicle to automatically adjust its speed to maintain a preset distance from the vehicle in front of it based on the driver’s preferences. Although individual drivers have different driving styles and preferences, current systems do not distinguish among users. We introduce a method to combine machine learning algorithms with demographic information and expert advice into existing automated assistive systems. This method can reduce the interactions between drivers and automated systems by adjusting parameters relevant to the operation of these systems based on their specific drivers and context of drive. We also learn when users tend to engage and disengage the automated system. This method sheds light on the kinds of dynamics that users develop while interacting with automation and can teach us how to improve these systems for the benefit of their users. While generic packages such as Weka were successful in learning drivers’ behavior, we found that improved learning models could be developed by adding information on drivers’ demographics and a previously developed model about different driver types. We present the general methodology of our learning procedure and suggest applications of our approach to other domains as well.
How to Cite
Rosenfeld, A., Bareket, Z., Goldman, C. V., Kraus, S., LeBlanc, D. J., & Tsimhoni, O. (2012). Towards Adapting Cars to their Drivers. AI Magazine, 33(4), 46. https://doi.org/10.1609/aimag.v33i4.2433
Authors who publish with this journal agree to the following terms:
1. Author(s) agree to transfer their copyrights in their article/paper to the Association for the Advancement of Artificial Intelligence (AAAI), in order to deal with future requests for reprints, translations, anthologies, reproductions, excerpts, and other publications. This grant will include, without limitation, the entire copyright in the article/paper in all countries of the world, including all renewals, extensions, and reversions thereof, whether such rights current exist or hereafter come into effect, and also the exclusive right to create electronic versions of the article/paper, to the extent that such right is not subsumed under copyright.
2. The author(s) warrants that they are the sole author and owner of the copyright in the above article/paper, except for those portions shown to be in quotations; that the article/paper is original throughout; and that the undersigned right to make the grants set forth above is complete and unencumbered.
3. The author(s) agree that if anyone brings any claim or action alleging facts that, if true, constitute a breach of any of the foregoing warranties, the author(s) will hold harmless and indemnify AAAI, their grantees, their licensees, and their distributors against any liability, whether under judgment, decree, or compromise, and any legal fees and expenses arising out of that claim or actions, and the undersigned will cooperate fully in any defense AAAI may make to such claim or action. Moreover, the undersigned agrees to cooperate in any claim or other action seeking to protect or enforce any right the undersigned has granted to AAAI in the article/paper. If any such claim or action fails because of facts that constitute a breach of any of the foregoing warranties, the undersigned agrees to reimburse whomever brings such claim or action for expenses and attorneys’ fees incurred therein.
4. Author(s) retain all proprietary rights other than copyright (such as patent rights).
5. Author(s) may make personal reuse of all or portions of the above article/paper in other works of their own authorship.
6. Author(s) may reproduce, or have reproduced, their article/paper for the author’s personal use, or for company use provided that AAAI copyright and the source are indicated, and that the copies are not used in a way that implies AAAI endorsement of a product or service of an employer, and that the copies per se are not offered for sale. The foregoing right shall not permit the posting of the article/paper in electronic or digital form on any computer network, except by the author or the author’s employer, and then only on the author’s or the employer’s own web page or ftp site. Such web page or ftp site, in addition to the aforementioned requirements of this Paragraph, must provide an electronic reference or link back to the AAAI electronic server, and shall not post other AAAI copyrighted materials not of the author’s or the employer’s creation (including tables of contents with links to other papers) without AAAI’s written permission.
7. Author(s) may make limited distribution of all or portions of their article/paper prior to publication.
8. In the case of work performed under U.S. Government contract, AAAI grants the U.S. Government royalty-free permission to reproduce all or portions of the above article/paper, and to authorize others to do so, for U.S. Government purposes.
9. In the event the above article/paper is not accepted and published by AAAI, or is withdrawn by the author(s) before acceptance by AAAI, this agreement becomes null and void.