Bayesian Traffic Light Parameter Tracking Based on Semi-Hidden Markov Models.

The previous studies have shown that optimizing the driving velocity profiles and route selection based on the availability of the traffic lights' operation information in a traffic network can significantly reduce the individual and cumulative energy consumption of on-road vehicles for the urban driving. In this paper, we propose an accurate and precise stochastic online estimation method of the parameters of the traffic lights operating at a piecewise constant period. In this paper, we first model the traffic lights with a semi-hidden Markov model (SHMM) and then develop the period measurement model governed by a unique noise model specific to the indirect traffic light period measurements. The proposed method solves the estimation problem in two stages: in the first stage, we determine the sequence of the Markovian states maximizing the probability given the measurements and the SHMM parameters; then, in the second stage, we update the period and state duration estimates based on the Bayesian tracking given the corresponding latest measurements. The simulation and real vehicle data results prove that the proposed method can accurately estimate the switching times and the period of the piecewise fixed-period traffic lights.