01744819

Component

https://doi.org/10.1177/0361198120931848

Eco-approach and departure is a complex control problem wherein a driver’s actions are guided over a period of time or distance so as to optimize fuel consumption. Reinforcement learning (RL) is a machine learning paradigm that mimics human learning behavior, in which 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 require prior knowledge of the environment to be learned and processed. This paper develops a deep reinforcement learning (DRL) agent for solving the eco-approach and departure problem in the vicinity of signalized intersections for minimization of fuel consumption. The DRL algorithm utilizes a deep neural network for the RL. Novel strategies such as varying actions, prioritized experience replay, target network, and double learning were implemented to overcome the expected instabilities during the training process. The results revealed the significance of the DRL algorithm in reducing fuel consumption. Interestingly, the DRL algorithm was able to successfully learn the environment and guide vehicles through the intersection without red light running violation. On average, the DRL provided fuel savings of about 13.02% with no red light running violations.

© National Academy of Sciences: Transportation Research Board 2020.

English

pp 119-131

2020-8

Transportation Research Record: Journal of the Transportation Research Board

Web

References (25)

Ecodriving; Fuel consumption; Highway safety; Intelligent agents; Machine learning; Neural networks; Signalized intersections

Data and Information Technology; Energy; Environment; Highways; Operations and Traffic Management; Safety and Human Factors

TRIS, TRB, ATRI

Jul 5 2020 3:03PM