HAL will be down for maintenance from Friday, June 10 at 4pm through Monday, June 13 at 9am. More information
Skip to Main content Skip to Navigation
Conference papers

Learning-based approach for online lane change intention prediction

P. Kumar 1 Mathias Perrollaz 1, * Stéphanie Lefèvre 1, * Christian Laugier 1
* Corresponding author
1 E-MOTION - Geometry and Probability for Motion and Action
Inria Grenoble - Rhône-Alpes, LIG - Laboratoire d'Informatique de Grenoble
Abstract : Predicting driver behavior is a key component for Advanced Driver Assistance Systems (ADAS). In this paper, a novel approach based on Support Vector Machine and Bayesian filtering is proposed for online lane change intention prediction. The approach uses the multiclass probabilistic outputs of the Support Vector Machine as an input to the Bayesian filter, and the output of the Bayesian filter is used for the final prediction of lane changes. A lane tracker integrated in a passenger vehicle is used for real-world data collection for the purpose of training and testing. Data from different drivers on different highways were used to evaluate the robustness of the approach. The results demonstrate that the proposed approach 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.
Complete list of metadata

Cited literature [16 references]  Display  Hide  Download

Contributor : Stéphanie Lefèvre Connect in order to contact the contributor
Submitted on : Thursday, May 9, 2013 - 12:05:33 AM
Last modification on : Wednesday, February 2, 2022 - 3:56:35 PM
Long-term archiving on: : Saturday, August 10, 2013 - 4:11:40 AM


Files produced by the author(s)


  • HAL Id : hal-00821309, version 1



P. Kumar, Mathias Perrollaz, Stéphanie Lefèvre, Christian Laugier. Learning-based approach for online lane change intention prediction. IEEE Intelligent Vehicles Symposium, Jun 2013, Gold Coast, Australia. ⟨hal-00821309⟩



Record views


Files downloads