Behavior Classification Algorithms at Intersections
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How_Behavior Classification_DF.pdf
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782.05 KB
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Author(s)
Aoude, Georges
Desaraju, Vishnu Rajeswar
Stephens, Lauren H.
How, Jonathan P.
Date Issued
June 2011
Journal
Proceedings of the 2011 IEEE Intelligent Vehicles Symposium (IV)
Publisher
Institute of Electrical and Electronics Engineers
Citation
Aoude, Georges S. et al. “Behavior classification algorithms at intersections and validation using naturalistic data.” IEEE, 2011. 601-606.
Version
Author's final manuscript
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.
MIT Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike 3.0
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/IVS.2011.5940569
