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A GLOSA system with stochastic signal switching times

Koutsas Petros

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Summary

The main goal of this work is to generate optimal trajectories for vehicles crossing a signalized junction, with traffic signals function in real-time (adaptive) mode. Adaptive traffic signals decide their switching according to the prevailing traffic conditions, and that is why the information of the switch is unknown beforehand (a-priori). The Green Light Optimal Speed Advisory (GLOSA) systems use information of the current state and timing of a traffic signal in order to guide the driver (or an automated vehicle) all the way to the traffic light by calculating the optimal trajectory and velocity profile for an initial state (position and speed) and a final state, which ensures that the vehicle will cross the traffic signal at green and with minimum fuel consumption and emissions. In previous works the problem of producing fuel-optimal vehicle trajectories for vehicles approaching an intersection, with traffic lights operating in both cases of fixed and stochastic switching times was considered. However, there was an assumption that the traffic signal is initially red and turn to green, which means that only half traffic light cycle was considered.In this work, the aforementioned problem is extended considering a full traffic light cycle, meaning that in the case of fixed switching times the traffic light switches from red to green and vice versa, while in case of unknown switching times, the traffic signal’s cycle consists of four phases: a certain green phase, in which the vehicle can freely pass; an uncertain green phase, in which there is a probability that the traffic light will extend its duration or it will turn to red; a certain red phase that the vehicle cannot pass and; an uncertain red phase, in which there is a probability that the red will be extended or it will turn to green. For the first case, the problem is formulated as an optimal control problem and is solved analytically via PMP (Pontryagin's Maximum Principle). In the second case, the traffic light switching times may vary based on the prevailing traffic conditions. In such cases, there are typically time-windows of admissible switching times for each stochastic phase; hence probability distributions of the switching times can be derived within these time-windows, e.g., based on statistics from past signal switching activity. Thus, the problem can be cast in the format of a stochastic optimal control problem, which may be solved numerically using Stochastic Dynamic Programming (SDP) techniques.

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