Trajectory-based embedding for random coefficients of a theory-based car-following model

Abstract

Deep neural networks have been adopted to recognize human car-following behaviors under the assumption that data would be all that was needed. These attempts, however, are inefficient because the knowledge accumulated by previous theory-based car-following studies is not utilized. In order to combine both approaches, we investigated the potential for using coefficients in a theory-based car-following model to introduce stochasticity to car-following behavior. To achieve this, we developed a probabilistic graphical model (PGM) that generates an ego vehicle's car-following response and the trajectories of the ego and surrounding vehicles. The proposed modeling framework integrates a theory-based car-following model with two variational autoencoders (VAEs) to embed the trajectories of the ego vehicle and surrounding vehicles into the hidden driving regimes and the corresponding random coefficients of the car-following model. The reaction time embedding was also incorporated into the modeling framework. The PGM was estimated using the variational inference (VI) within a Bayesian framework. As a result, the proposed car-following model outperformed other benchmark models in reproducing real driver responses.