A State-Space Solution to the Estimation of Interacting Vehicle Trajectories with Deep Neural Networks and Variational Bayes Filtering

This paper addresses the estimation of trajectories of interacting vehicles at a microscopic scale, as a prerequisite to their prediction for risk assessment. A state-space solution is investigated, where both the Markov hidden state (continuous-valued, which captures the joint histories of vehicles) and the measurements (low-dimensional and noisy) admit a vehicle-wise structure. The vehicles' transition models are assumed independent of each other, time- and vehicle-invariant, and coequal to an “egocentric” prior dynamics pdf. To cope with the vehicles' interactions, this pdf is conditioned on the full state vector as the past time index, which imposes a centralized estimation/prediction of the fleet motion. The two fundamental pillars of the approach are developed: learning of a Gaussian mixture egocentric transition model by means of Deep Neural Networks; synthesis of a stochastic variational Bayes filtering algorithm which features a decentralized vehicle-wise structure but takes into account interactions. Tests on highway scenarios are presented.