Behavior classification algorithms at intersections and validation using naturalistic data

Abstract

The ability to classify driver behavior lays the foundation for more advanced driver assistance systems. Improving safety at intersections has also been identified as high priority due to the large number of intersection related fatalities. This paper focuses on developing algorithms for estimating driver behavior at road intersections. It introduces two classes of algorithms that can classify drivers as compliant or violating. They are based on 1) Support Vector Machines (SVM) and 2) Hidden Markov Models (HMM), two very popular machine learning approaches that have been used extensively for classification in multiple disciplines. The algorithms are successfully validated using naturalistic intersection data collected in Christiansburg, VA, through the US Department of Transportation Cooperative Intersection Collision Avoidance System for Violations (CICAS-V) initiative.

Original document

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

• http://dx.doi.org/10.1109/ivs.2011.5940569 under the license https://creativecommons.org/licenses/by-nc-sa

• https://dspace.mit.edu/bitstream/1721.1/65892/1/How_Behavior%20Classification_DF.pdf

• http://xplorestaging.ieee.org/ielx5/5930385/5940387/05940569.pdf?arnumber=5940569,

http://dx.doi.org/10.1109/ivs.2011.5940569 under the license cc-by-nc-sa

• https://dblp.uni-trier.de/db/conf/ivs/ivs2011.html#AoudeDSH11,

http://yadda.icm.edu.pl/yadda/element/bwmeta1.element.ieee-000005940569,

http://dx.doi.org/10.1109/IVS.2011.5940569,

https://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5940569,

https://academic.microsoft.com/#/detail/2131762276