Learning-based approach for online lane change intention prediction

P. Kumar 1 Mathias Perrollaz 1, * Stéphanie Lefèvre 1, * Christian Laugier 1
* Auteur correspondant
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.
Type de document :
Communication dans un congrès
IEEE Intelligent Vehicles Symposium, Jun 2013, Gold Coast, Australia. 2013
Liste complète des métadonnées

Littérature citée [16 références]  Voir  Masquer  Télécharger

https://hal.inria.fr/hal-00821309
Contributeur : Stéphanie Lefèvre <>
Soumis le : jeudi 9 mai 2013 - 00:05:33
Dernière modification le : jeudi 11 octobre 2018 - 08:48:02
Document(s) archivé(s) le : samedi 10 août 2013 - 04:11:40

Fichier

Kumar_IV_13.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

  • HAL Id : hal-00821309, version 1

Collections

Citation

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. 2013. 〈hal-00821309〉

Partager

Métriques

Consultations de la notice

449

Téléchargements de fichiers

1815