Machine Learning Tools for Optimization of Fuel Consumption at Signalized Intersections in Connected/Automated Vehicles Environment

Researchers continue to seek numerous techniques for making the transportation sector more sustainable in terms of fuel consumption and greenhouse gas emissions. Among the most effective techniques is Eco-driving at signalized intersections. Eco-driving is a complex control problem where drivers approaching the intersections are guided, over a period of time, to optimize fuel consumption. Eco-driving control systems reduce fuel consumption by optimizing vehicle trajectories near signalized intersections based on information of the SpaT (Signal Phase and Timing). Developing Eco-driving applications for semi-actuated signals, unlike pre-timed, is more challenging due to variations in cycle length resulting from fluctuations in traffic demand. Reinforcement learning (RL) is a machine learning paradigm that mimics the human learning behavior where an agent attempts to solve a given control problem by interacting with the environment and developing an optimal policy. Unlike the methods implemented in previous studies for solving the Eco-driving problem, RL does not necessitate prior knowledge of the environment being learned and processed. Therefore, the aim of this study is twofold: (1) Develop a novel brute force Eco-driving algorithm (ECO-SEMI-Q) for CAV (Connected/Autonomous Vehicles) passing through semi-actuated signalized intersections; and (2) Develop a novel Deep Reinforcement Learning (DRL) Eco-driving algorithm for CAV passing through fixed-time signalized intersections. The developed algorithms are tested at both microscopic and macroscopic levels. For the microscopic level, results indicate that the fuel consumption for vehicles controlled by the ECO-SEMI-Q and DRL models is 29.2% and 23% less than that for the case with no control, respectively. For the macroscopic level, a sensitivity analysis for the impact of MPR (Market Penetration Rate) shows that the savings in fuel consumption increase with higher MPR. Furthermore, when MPR is greater than 50%, the ECO-SEMI-Q algorithm provides appreciable savings in travel times. The sensitivity analysis indicates savings in the network fuel consumption when the MPR of the DRL algorithm is higher than 35%. At MPR less than 35%, the DRL algorithm has an adverse impact on fuel consumption due to aggressive lane change and passing maneuvers. These reductions in fuel consumption demonstrate the ability of the algorithms to provide more environmentally sustainable signalized intersections

TRA-910: CONNECTED VEHICLE V2I COMMUNICATION APPLICATION TO ENHANCE DRIVER AWARENESS AT SIGNALIZED INTERSECTIONS

This study introduces a Vehicle-To-Infrastructure (V2I) architecture to enhance driver awareness at signalized intersections. The main objectives are to (i) provide a proof-of-concept field experiment on the use of V2I communication architecture at a signalized intersection and (ii) evaluate the impact of V2I communication on improving driver performance while crossing the intersection. The proposed V2I communication application will relay an advisory auditory message to the driver regarding the status of the traffic signal. It is expected that driver behaviour is going to change as a result of the in-vehicle audible message. Consequently, the proposed application will collect additional driver performance indicators which include information on average speed, maximum speed, and the acceleration\deceleration profiles. To understand the impact of the advisory message on changing driver behaviour, a comparison was performed between the indicators with and without the in-vehicle message. Driver behavior was investigated under two scenarios, namely; as the driver heads towards a green signal and as the driver heads towards a red signal. For both scenarios, the results show that the average speed of the driver have changed significantly after turning “on” the in-vehicle messages. In addition, the maximum speed distribution shifted towards a lower value indicating decreases in maximum speeds. Moreover, the difference between the acceleration\deceleration profiles near the intersection when driving with and without the message, while heading towards a red signal, was found to be significant. These preliminary results show that the proposed V2I communication application can have promising impacts on improving driver awareness at signalized intersections

Eco-driving for Electric Connected Vehicles at Signalized Intersections: A Parameterized Reinforcement Learning approach

This paper proposes an eco-driving framework for electric connected vehicles (CVs) based on reinforcement learning (RL) to improve vehicle energy efficiency at signalized intersections. The vehicle agent is specified by integrating the model-based car-following policy, lane-changing policy, and the RL policy, to ensure safe operation of a CV. Subsequently, a Markov Decision Process (MDP) is formulated, which enables the vehicle to perform longitudinal control and lateral decisions, jointly optimizing the car-following and lane-changing behaviors of the CVs in the vicinity of intersections. Then, the hybrid action space is parameterized as a hierarchical structure and thereby trains the agents with two-dimensional motion patterns in a dynamic traffic environment. Finally, our proposed methods are evaluated in SUMO software from both a single-vehicle-based perspective and a flow-based perspective. The results show that our strategy can significantly reduce energy consumption by learning proper action schemes without any interruption of other human-driven vehicles (HDVs)