Behavior classification algorithms at intersections and validation using naturalistic data
Georges S. Aoude,Vishnu R. Desaraju,Lauren H. Stephens,Jonathan P. How +3 more
- 05 Jun 2011
- pp 601-606
TL;DR: In this paper, two machine learning approaches, Support Vector Machines (SVM) and Hidden Markov Models (HMM), are used to classify drivers as compliant or violating at road intersections.
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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.
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Citations
Learning-based approach for online lane change intention prediction
Puneet Kumar,Mathias Perrollaz,Stephanie Lefevre,Christian Laugier +3 more
- 23 Jun 2013
TL;DR: A novel approach based on Support Vector Machine and Bayesian filtering is proposed for online lane change intention prediction that is able to predict driver intention to change lanes on average 1.3 seconds in advance, with a maximum prediction horizon of 3.29 seconds.
Driver Behavior Classification at Intersections and Validation on Large Naturalistic Data Set
TL;DR: Two classes of algorithms that can classify drivers as compliant or violating at road intersections are introduced, based on support vector machines and hidden Markov models, which are two very popular machine learning approaches that have been used successfully for classification in multiple disciplines.
257
Driver intent inference at urban intersections using the intelligent driver model
Martin Liebner,Michael Baumann,Felix Klanner,Christoph Stiller +3 more
- 03 Jun 2012
TL;DR: A simple, real-time capable approach using an explicit model to represent both car-following and turning behaviour is proposed and preliminary results based on a Bayes net classification are presented.
234
Looking at Intersections: A Survey of Intersection Monitoring, Behavior and Safety Analysis of Recent Studies
TL;DR: A new behavior and safety classification is presented based on key features used for intersection design, planning, and safety, and a new technique which is strong candidates for automation with visual sensing technology is emphasized.
184
Lane changing intention recognition based on speech recognition models
TL;DR: A novel algorithm combining the hidden Markov model (HMM) and Bayesian filtering techniques to recognize a driver’s lane changing intention and can achieve a recognition accuracy of 93.5% and 90.3% which is a significant improvement compared with the HMM-only algorithm.
173
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