# Learning Eco-Driving Strategies at Signalized Intersections

arXiv: https://arxiv.org/abs/2204.12561 · HTML: https://mlsanity.com/paper/2204.12561/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Placed in Cooperative and V2X driving, one of 9 directions in End-to-end autonomous driving.
- Builds on 10 of its 36 references that are in the map.
- First posted on arXiv 26 Apr 2022.

## Placed in

- End-to-end autonomous driving / [Cooperative and V2X driving](https://mlsanity.com/driving/cooperative-and-v2x-driving/)

## Builds on (most cited first, 10 of 10)

- [Trust Region Policy Optimization](https://mlsanity.com/paper/1502.05477/) (2015)
- [Reinforcement Learning in Healthcare: A Survey](https://mlsanity.com/paper/1908.08796/) (2019)
- [Flow: A Modular Learning Framework for Mixed Autonomy Traffic](https://mlsanity.com/paper/1710.05465/) (2017)
- [Reinforcement Learning in Economics and Finance](https://mlsanity.com/paper/2003.10014/) (2020)
- [Reinforcement Learning for Mixed Autonomy Intersections](https://mlsanity.com/paper/2111.04686/) (2021)
- [Eco-Driving at Signalized Intersections: A Multiple Signal Optimization Approach](https://mlsanity.com/paper/2001.01117/) (2020)
- [A Deep Reinforcement Learning Framework for Eco-driving in Connected and Automated Hybrid Electric Vehicles](https://mlsanity.com/paper/2101.05372/) (2021)
- [Socially Compatible Control Design of Automated Vehicle in Mixed Traffic](https://mlsanity.com/paper/2112.02041/) (2021)
- [Robust Optimal Eco-driving Control with Uncertain Traffic Signal Timing](https://mlsanity.com/paper/1802.07192/) (2018)
- [Early Findings from Field Trials of Heavy-Duty Truck Connected Eco-Driving System](https://mlsanity.com/paper/1908.00541/) (2019)

## Built on by (most cited first, 9 of 9)

- [The Impact of Task Underspecification in Evaluating Deep Reinforcement Learning](https://mlsanity.com/paper/2210.08607/) (2022)
- [Model-Based Transfer Learning for Contextual Reinforcement Learning](https://mlsanity.com/paper/2408.04498/) (2024)
- [IntersectionZoo: Eco-driving for Benchmarking Multi-Agent Contextual Reinforcement Learning](https://mlsanity.com/paper/2410.15221/) (2024)
- [Generalizing Cooperative Eco-driving via Multi-residual Task Learning](https://mlsanity.com/paper/2403.04232/) (2024)
- [Model-free Learning of Corridor Clearance: A Near-term Deployment Perspective](https://mlsanity.com/paper/2312.10339/) (2023)
- [Mitigating Metropolitan Carbon Emissions with Dynamic Eco-driving at Scale](https://mlsanity.com/paper/2408.05609/) (2024)
- [Cooperative Advisory Residual Policies for Congestion Mitigation](https://mlsanity.com/paper/2407.00553/) (2024)
- [Analyzing Emissions and Energy Efficiency at Unsignalized Real-world Intersections Under Mixed Traffic Control](https://mlsanity.com/paper/2311.11866/) (2023)
- [Emergency Preemption Without Online Exploration: A Decision Transformer Approach](https://mlsanity.com/paper/2603.22315/) (2026)

## Abstract (arXiv)

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.