Parallel AIOHMM-GAN: A Novel Stochastic Driver Behavior Model for Autonomous Vehicles Suffering From Oncoming High Beams
Mingcong Cao, Jingqiang Zha, Junmin Wang · 2021
Oncoming vehicle high-beams pose a potential risk to the object detection performance of cameras in autonomous driving. In this scenario, modeling stochastic human driving behavior becomes a challenging task. This paper provides an integrated framework that generates appropriate driving operations to handle the oncoming high-beams scenario based on human driver data. By decomposing human drivers' pedal and steering signals, a parallel autoregressive input-output hidden Markov model (p-AIOHMM) is developed to capture the temporal dependencies of the decomposed driving actions. Besides, parallel generative adversarial networks (p-GAN) are proposed to reconstruct the pedal positions and the steering angles from the p-AIOHMM-based actions. All the parameters can be learned from the naturalistic driver data. Experimental results have verified that the developed parallel AIOHMM-GAN solution can perform a better task of driving behavior generation when suffering from oncoming high beams.