Interaction-Aware Driver Maneuver Inference in Highways Using Realistic Driver Models - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2017

Interaction-Aware Driver Maneuver Inference in Highways Using Realistic Driver Models


In this work, we address the problem of lane change maneuver prediction in highway scenarios using information from sensors and perception systems widely used in automated driving. Our prediction approach is twofold. First, a driver model learned from demonstrations via Inverse Reinforcement Learning is used to equip a host vehicle with the anticipatory behavior reasoning capability of common drivers. Second, inference on an interaction-aware augmented Switching State-Space Model allows the approach to account for the dynamic evidence observed. The use of a driver model that correctly balances the driving and risk-aversive preferences of a driver allows the computation of a planning-based maneuver prediction. Integrating this anticipatory prediction into the maneuver inference engine brings a degree of scene understanding into the estimate and leads to faster lane change detections compared to those obtained by relying on dynamics alone. The performance of the presented framework is evaluated using highway data collected with an instrumented vehicle. The combination of model-based maneuver prediction and filtering-based state and maneuver tracking is shown to outperform an Interacting Multiple Model filter in the detection of highway lane change maneuvers regarding accuracy, detection latency—by an average of 0.4 seconds—and false-positive rates.
Fichier principal
Vignette du fichier
ITSC17_SierraGonzalezEtAl_author_version.pdf (3 Mo) Télécharger le fichier
itsc17_inrialogo_720p.mp4 (129.79 Mo) Télécharger le fichier
Origin : Files produced by the author(s)
Origin : Files produced by the author(s)

Dates and versions

hal-01589493 , version 1 (18-09-2017)




  • HAL Id : hal-01589493 , version 1


David Sierra González, Víctor Romero-Cano, Jilles Steeve Dibangoye, Christian Laugier. Interaction-Aware Driver Maneuver Inference in Highways Using Realistic Driver Models. Proceedings of the 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC 2017), Oct 2017, Yokohama, Japan. ⟨hal-01589493⟩
693 View
470 Download


Gmail Facebook Twitter LinkedIn More