Behavior Classification Algorithms at Intersections
Author(s)Aoude, Georges; Desaraju, Vishnu Rajeswar; Stephens, Lauren H.; How, Jonathan P.
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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.
DepartmentMassachusetts Institute of Technology. Department of Aeronautics and Astronautics; Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Proceedings of the 2011 IEEE Intelligent Vehicles Symposium (IV)
Institute of Electrical and Electronics Engineers
Aoude, Georges S. et al. “Behavior classification algorithms at intersections and validation using naturalistic data.” IEEE, 2011. 601-606.
Author's final manuscript
INSPEC Accession Number: 12095304