Abstract
The GLOSA (Green Light Optimal Speed Advisory) system provides speed advice to drivers so that drivers can pass through congested intersections at right instant with shorter time and lower energy consumption. Traditional GLOSA system only considers the SPaT (Signal Phase and Timing) of traffic light. However, two another important factors, namely queuing effect and actual tracking error of drivers, are seldomly considered, which degrades the actual performance of the GLOSA system. Intelligent connected vehicles based on V2I (Vehicle to Infrastructure) have great application potential in solving this problem. In this study, firstly, a vehicle queue length estimation method based on V2I technology is proposed to predict the effective green light time. Secondly, a hierarchical GLOSA system is developed, where the upper layer provides the global recommended optimal speed aiming at minimizing energy consumption, while the bottom layer provides the modified recommended speed considering the driver's tracking error. Finally, the tracking error of the driver when executing the recommended speed is derived based on the real-world experiment. Corresponding simulation and field test platforms are also established. Results show that compared with the traditional GLOSA system, the improved GLOSA system considering the vehicle queuing effect and driving error can effectively improve the energy-saving performance of the vehicle.
Highlights
The increasing traffic activities greatly improve the mobility of people and goods, and produce more greenhouse gas emissions and consume a lot of energy [1]
According to the probability Ps of each possible path, the cost function of Stochastic Model Predictive Control (SMPC) is defined as the expected square error of the reference speed vref and the predicted speed vpred in the specified time steps: Nmc t l
Based on the simulation data and Eq(1-4), the energy consumption without considering driving error and considering driving error increase by 7.38% and 6.51%, respectively. Compared with the former, the latter realizes 11.8% improvement in energy-saving performance. This is because the tracking error is considered in the lower layer, so the driver’s real speed trajectory when following the modified recommended speed trajectory is closer to the planned optimal speed
Summary
The increasing traffic activities greatly improve the mobility of people and goods, and produce more greenhouse gas emissions and consume a lot of energy [1]. The numerical simulation results in reference [23] show that the fuel-saving performance of connected vehicles can reach 10% if the optimal speed is followed accurately, while in the real test of [24], the economic performance improvement of EAD system is only 2%. A GLOSA system for electric vehicles, which considers the queuing effect and human driving error, is designed and evaluated through simulation and road test. The main contributions of this paper are concluded as follows: firstly, based on the monitored traffic flow, the queue length of vehicles at intersections is estimated, and the effective green light time model is constructed. The upper level calculates the optimal speed trajectory through global planning to minimize energy consumption, while the bottom layer considers the actual tracking error of drivers and uses Stochastic Model Predictive Control (SMPC) to conduct a local adaptive speed planning. GLOSA can be used to take over the system to execute adaptive cruise control mode, which depends on the driver's mode selection
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