# Hybrid Reinforcement Learning-Based Eco-Driving Strategy for Connected and Automated Vehicles at Signalized Intersections

arXiv: https://arxiv.org/abs/2201.07833 · HTML: https://mlsanity.com/paper/2201.07833/

## Key facts

- Cited by 16 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 8 of its 42 references that are in the map.
- First posted on arXiv 19 Jan 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, 8 of 8)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Prioritized Experience Replay](https://mlsanity.com/paper/1511.05952/) (2015)
- [Dueling Network Architectures for Deep Reinforcement Learning](https://mlsanity.com/paper/1511.06581/) (2015)
- [Safe, Multi-Agent, Reinforcement Learning for Autonomous Driving](https://mlsanity.com/paper/1610.03295/) (2016)
- [Unity: A General Platform for Intelligent Agents](https://mlsanity.com/paper/1809.02627/) (2018)
- [Deep Reinforcement Learning framework for Autonomous Driving](https://mlsanity.com/paper/1704.02532/) (2017)
- [Deep Reinforcement Learning Based High-level Driving Behavior Decision-making Model in Heterogeneous Traffic](https://mlsanity.com/paper/1902.05772/) (2019)
- [Optimal Control and Coordination of Connected and Automated Vehicles at Urban Traffic Intersections](https://mlsanity.com/paper/1509.08689/) (2015)

## Built on by (most cited first, 16 of 16)

- [A Survey and Framework of Cooperative Perception: From Heterogeneous Singleton to Hierarchical Cooperation](https://mlsanity.com/paper/2208.10590/) (2022)
- [Infrastructure-Based Object Detection and Tracking for Cooperative Driving Automation: A Survey](https://mlsanity.com/paper/2201.11871/) (2022)
- [VINet: Lightweight, Scalable, and Heterogeneous Cooperative Perception for 3D Object Detection](https://mlsanity.com/paper/2212.07060/) (2022)
- [Real-time system optimal traffic routing under uncertainties -- Can physics models boost reinforcement learning?](https://mlsanity.com/paper/2407.07364/) (2024)
- [Cyber Mobility Mirror for Enabling Cooperative Driving Automation in Mixed Traffic: A Co-Simulation Platform](https://mlsanity.com/paper/2201.09463/) (2022)
- [Cyber Mobility Mirror: A Deep Learning-based Real-World Object Perception Platform Using Roadside LiDAR](https://mlsanity.com/paper/2202.13505/) (2022)
- [Cooperverse: A Mobile-Edge-Cloud Framework for Universal Cooperative Perception with Mixed Connectivity and Automation](https://mlsanity.com/paper/2302.03128/) (2023)
- [Mitigating Metropolitan Carbon Emissions with Dynamic Eco-driving at Scale](https://mlsanity.com/paper/2408.05609/) (2024)
- [A Survey of Reinforcement Learning-Based Motion Planning for Autonomous Driving: Lessons Learned from a Driving Task Perspective](https://mlsanity.com/paper/2503.23650/) (2025)
- [Adaptive Frequency Green Light Optimal Speed Advisory based on Hybrid Actor-Critic Reinforcement Learning](https://mlsanity.com/paper/2306.04660/) (2023)
- [A Scoping Review of Energy-Efficient Driving Behaviors and Applied State-of-the-Art AI Methods](https://mlsanity.com/paper/2403.02053/) (2024)
- [Evaluating Scenario-based Decision-making for Interactive Autonomous Driving Using Rational Criteria: A Survey](https://mlsanity.com/paper/2501.01886/) (2025)
- [Deep reinforcement learning-based longitudinal control strategy for automated vehicles at signalised intersections](https://mlsanity.com/paper/2505.08896/) (2025)
- [Eco-driving for Electric Connected Vehicles at Signalized Intersections: A Parameterized Reinforcement Learning approach](https://mlsanity.com/paper/2206.12065/) (2022)
- [Analyzing Emissions and Energy Efficiency at Unsignalized Real-world Intersections Under Mixed Traffic Control](https://mlsanity.com/paper/2311.11866/) (2023)
- [DVS-RG: Differential Variable Speed Limits Control using Deep Reinforcement Learning with Graph State Representation](https://mlsanity.com/paper/2405.09163/) (2024)

## Abstract (arXiv)

Taking advantage of both vehicle-to-everything (V2X) communication and automated driving technology, connected and automated vehicles are quickly becoming one of the transformative solutions to many transportation problems. However, in a mixed traffic environment at signalized intersections, it is still a challenging task to improve overall throughput and energy efficiency considering the complexity and uncertainty in the traffic system. In this study, we proposed a hybrid reinforcement learning (HRL) framework which combines the rule-based strategy and the deep reinforcement learning (deep RL) to support connected eco-driving at signalized intersections in mixed traffic. Vision-perceptive methods are integrated with vehicle-to-infrastructure (V2I) communications to achieve higher mobility and energy efficiency in mixed connected traffic. The HRL framework has three components: a rule-based driving manager that operates the collaboration between the rule-based policies and the RL policy; a multi-stream neural network that extracts the hidden features of vision and V2I information; and a deep RL-based policy network that generate both longitudinal and lateral eco-driving actions. In order to evaluate our approach, we developed a Unity-based simulator and designed a mixed-traffic intersection scenario. Moreover, several baselines were implemented to compare with our new design, and numerical experiments were conducted to test the performance of the HRL model. The experiments show that our HRL method can reduce energy consumption by 12.70% and save 11.75% travel time when compared with a state-of-the-art model-based Eco-Driving approach.