Learning Eco-Driving Strategies at Signalized Intersections

• URL: http://arxiv.org/abs/2204.12561v1
• Date: Tue, 26 Apr 2022 19:45:11 GMT
• Title: Learning Eco-Driving Strategies at Signalized Intersections
• Authors: Vindula Jayawardana and Cathy Wu
• Abstract summary: We propose a reinforcement learning approach to learn effective eco-driving control strategies. We analyze the potential impact of a learned strategy on fuel consumption, CO2 emission, and travel time. Results indicate that even 25% CAV penetration can bring at least 50% of the total fuel and emission reduction benefits.
• Score: 1.7682859739940435
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Signalized intersections in arterial roads result in persistent vehicle idling and excess accelerations, contributing to fuel consumption and CO2 emissions. There has thus been a line of work studying eco-driving control strategies to reduce fuel consumption and emission levels at intersections. However, methods to devise effective control strategies across a variety of traffic settings remain elusive. In this paper, we propose a reinforcement learning (RL) approach to learn effective eco-driving control strategies. We analyze the potential impact of a learned strategy on fuel consumption, CO2 emission, and travel time and compare with naturalistic driving and model-based baselines. We further demonstrate the generalizability of the learned policies under mixed traffic scenarios. Simulation results indicate that scenarios with 100% penetration of connected autonomous vehicles (CAV) may yield as high as 18% reduction in fuel consumption and 25% reduction in CO2 emission levels while even improving travel speed by 20%. Furthermore, results indicate that even 25% CAV penetration can bring at least 50% of the total fuel and emission reduction benefits.

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