Citation

Ou C, Karray F. IEEE Trans. Vehicular Tech. 2020; 69(2): 1328-1340.

Copyright

(Copyright © 2020, IEEE (Institute of Electrical and Electronics Engineers))

DOI

10.1109/TVT.2019.2958622

PMID

unavailable

Abstract

Many of today's vehicles come equipped with Advanced Driver Assistance Systems (ADAS). Proactive ADAS have the ability to predict short term driving situations. This provides drivers more time to take adequate actions to avoid or mitigate driving risks. In this work, we address the question of predicting drivers' imminent maneuvers before they perform an actual steering operation. The proposed system uses deep recurrent neural networks to fuse the information regarding driver observation actions and the driving environment. With new data labeling methods and effective sequential modeling approaches, the system is able to predict with high accuracy driving maneuvers shortly before the actual steering operations. A set of experiments show that the proposed approach anticipates lane change maneuvers 1.50 seconds before cars start to yaw with an accuracy improved to 90.52% and anticipates turn maneuvers at intersections with green lights 2.53 seconds before cars start to yaw with an accuracy improved to 78.59%. We also show in this work how the system can be adapted for driving proficiency assessment.

Language: en

Keywords

Acceleration; Automobiles; deep learning; Driver; driving proficiency; driving safety; Hidden Markov models; maneuver prediction; Predictive models; Recurrent neural networks; Trajectory