Green Light Optimal Speed Advisory System Designed for Electric Vehicles Considering Queuing Effect and Driver’s Speed Tracking Error

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.

[1]  Nasser L. Azad,et al.  A Distributed Reference Governor Approach to Ecological Cooperative Adaptive Cruise Control , 2018, IEEE Transactions on Intelligent Transportation Systems.

[2]  Ardalan Vahidi,et al.  Predictive Cruise Control: Utilizing Upcoming Traffic Signal Information for Improving Fuel Economy and Reducing Trip Time , 2011, IEEE Transactions on Control Systems Technology.

[3]  Petros A. Ioannou,et al.  Personalized Driver Assistance for Signalized Intersections Using V2I Communication , 2016, IEEE Transactions on Intelligent Transportation Systems.

[4]  Henry X. Liu,et al.  Optimal vehicle speed trajectory on a signalized arterial with consideration of queue , 2015 .

[5]  Xudong Zhang,et al.  Predictive Eco-Driving Application Considering Real-World Traffic Flow , 2020, IEEE Access.

[6]  Shankar C. Subramanian,et al.  Cooperative control of regenerative braking and friction braking for a hybrid electric vehicle , 2016 .

[7]  Jeffrey Gonder,et al.  Analyzing Vehicle Fuel Saving Opportunities through Intelligent Driver Feedback , 2012 .

[8]  Xinrong Li,et al.  Wireless Sensor Network System Design Using Raspberry Pi and Arduino for Environmental Monitoring Applications , 2014, FNC/MobiSPC.

[9]  Yoshitaka Marumo,et al.  Safety Evaluation of Green Light Optimal Speed Advisory (GLOSA) System in Real-World Signalized Intersection , 2020, J. Robotics Mechatronics.

[10]  Karin Brundell-Freij,et al.  Optimizing route choice for lowest fuel consumption - Potential effects of a new driver support tool , 2006 .

[11]  Yang Zheng,et al.  Distributed Model Predictive Control for Heterogeneous Vehicle Platoons Under Unidirectional Topologies , 2016, IEEE Transactions on Control Systems Technology.

[12]  Kenneth Sörensen,et al.  A Practical Approach for Robust and Flexible Vehicle Routing Using Metaheuristics and Monte Carlo Sampling , 2009, J. Math. Model. Algorithms.

[13]  Yuan Zou,et al.  A pseudospectral method for solving optimal control problem of a hybrid tracked vehicle , 2017 .

[14]  Peng Hao,et al.  Eco-Approach and Departure (EAD) Application for Actuated Signals in Real-World Traffic , 2016 .

[15]  Alberto Bemporad,et al.  Stochastic MPC With Learning for Driver-Predictive Vehicle Control and its Application to HEV Energy Management , 2014, IEEE Transactions on Control Systems Technology.

[16]  Chao Sun,et al.  Robust Optimal ECO-driving Control with Uncertain Traffic Signal Timing , 2018, 2018 Annual American Control Conference (ACC).

[17]  Kanok Boriboonsomsin,et al.  Arterial velocity planning based on traffic signal information under light traffic conditions , 2009, 2009 12th International IEEE Conference on Intelligent Transportation Systems.

[18]  Zhongke Shi,et al.  Consensus and optimal speed advisory model for mixed traffic at an isolated signalized intersection , 2019 .

[19]  Fei Luo,et al.  A Switched Control Strategy of Heterogeneous Vehicle Platoon for Multiple Objectives With State Constraints , 2019, IEEE Transactions on Intelligent Transportation Systems.

[20]  Bo Cheng,et al.  Eco-Departure of Connected Vehicles With V2X Communication at Signalized Intersections , 2015, IEEE Transactions on Vehicular Technology.

[21]  Xudong Zhang,et al.  A Hierarchical Energy Management for Hybrid Electric Tracked Vehicle Considering Velocity Planning With Pseudospectral Method , 2020, IEEE Transactions on Transportation Electrification.

[22]  Peng Hao,et al.  Preliminary evaluation of field testing on Eco-Approach and Departure (EAD) application for actuated signals , 2015, 2015 International Conference on Connected Vehicles and Expo (ICCVE).

[23]  Dirk Helbing,et al.  Enhanced intelligent driver model to access the impact of driving strategies on traffic capacity , 2009, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[24]  Hao Liu,et al.  Traffic signal control by leveraging Cooperative Adaptive Cruise Control (CACC) vehicle platooning capabilities , 2019 .

[25]  Panos Y. Papalambros,et al.  Optimal engine calibration for individual driving styles , 2008 .

[26]  Panos Y. Papalambros,et al.  Real-Time Self-Learning Optimization of Diesel Engine , 2007 .

[27]  Hao Yang,et al.  Eco-Cooperative Adaptive Cruise Control at Signalized Intersections Considering Queue Effects , 2017, IEEE Transactions on Intelligent Transportation Systems.

[28]  Mehrdad Dianati,et al.  Application of vehicular communications for improving the efficiency of traffic in urban areas , 2011, Wirel. Commun. Mob. Comput..

[29]  M J Lighthill,et al.  On kinematic waves II. A theory of traffic flow on long crowded roads , 1955, Proceedings of the Royal Society of London. Series A. Mathematical and Physical Sciences.

[30]  Yuan Zou,et al.  Reinforcement Learning of Adaptive Energy Management With Transition Probability for a Hybrid Electric Tracked Vehicle , 2015, IEEE Transactions on Industrial Electronics.

[31]  Yuan Zou,et al.  A Real-Time Markov Chain Driver Model for Tracked Vehicles and Its Validation: Its Adaptability via Stochastic Dynamic Programming , 2017, IEEE Transactions on Vehicular Technology.

[32]  Fei Luo,et al.  Ecological Cooperative Look-Ahead Control for Automated Vehicles Travelling on Freeways With Varying Slopes , 2019, IEEE Transactions on Vehicular Technology.

[33]  Bo Cheng,et al.  Legendre Pseudospectral Computation of Optimal Speed Profiles for Vehicle Eco-Driving System * , 2014 .

[34]  Harald Waschl,et al.  Flexible Spacing Adaptive Cruise Control Using Stochastic Model Predictive Control , 2018, IEEE Transactions on Control Systems Technology.

[35]  Peng Hao,et al.  Developing a framework of Eco-Approach and Departure application for actuated signal control , 2015, 2015 IEEE Intelligent Vehicles Symposium (IV).

[36]  Brian Caulfield,et al.  Measuring the success of reducing emissions using an on-board eco-driving feedback tool , 2014 .