An Optimal Velocity-Planning Scheme for Vehicle Energy Efficiency Through Probabilistic Prediction of Traffic-Signal Timing

The main contribution of this paper is the formulation of a predictive optimal velocity-planning algorithm that uses probabilistic traffic-signal phase and timing (SPAT) information to increase a vehicle's energy efficiency. We introduce a signal-phase prediction model that uses historically averaged timing data and real-time phase data to determine the probability of green for upcoming traffic lights. In an optimal control framework, we then calculate the best velocity trajectory that maximizes the chance of going through green lights. The case study results from a multisignal simulation indicating that energy efficiency can be increased with probabilistic timing data and real-time phase data. Monte Carlo simulations are used to confirm that the case study results are valid, on average. Finally, simulated vehicles are driven through a series of traffic signals, using recorded data from a real-world set of traffic-adaptive signals, to determine the applicability of these predictive models to various types of traffic signals.