# Roadside Units Assisted Localized Automated Vehicle Maneuvering: An Offline Reinforcement Learning Approach

arXiv: https://arxiv.org/abs/2405.03935 · HTML: https://mlsanity.com/paper/2405.03935/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Imitation and offline RL for driving, one of 13 directions in Offline RL and imitation learning from data.
- Builds on 11 of its 32 references that are in the map.
- First posted on arXiv 7 May 2024.

## Placed in

- Offline RL and imitation learning from data / [Imitation and offline RL for driving](https://mlsanity.com/offlinerl/imitation-and-offline-rl-for-driving/)

## Builds on (most cited first, 11 of 11)

- [Addressing Function Approximation Error in Actor-Critic Methods](https://mlsanity.com/paper/1802.09477/) (2018)
- [Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems](https://mlsanity.com/paper/2005.01643/) (2020)
- [A Minimalist Approach to Offline Reinforcement Learning](https://mlsanity.com/paper/2106.06860/) (2021)
- [Behavioral Cloning from Observation](https://mlsanity.com/paper/1805.01954/) (2018)
- [Interruption-Aware Cooperative Perception for V2X Communication-Aided Autonomous Driving](https://mlsanity.com/paper/2304.11821/) (2023)
- [Smart Mobility Digital Twin Based Automated Vehicle Navigation System: A Proof of Concept](https://mlsanity.com/paper/2402.12682/) (2024)
- [Digital Twin-Assisted Cooperative Driving at Non-Signalized Intersections](https://mlsanity.com/paper/2105.01357/) (2021)
- [Digital Twins for Autonomous Driving: A Comprehensive Implementation and Demonstration](https://mlsanity.com/paper/2401.08653/) (2023)
- [Distributed Cooperative Driving in Multi-Intersection Road Networks](https://mlsanity.com/paper/2104.10313/) (2021)
- [Towards mmWave V2X in 5G and Beyond to Support Automated Driving](https://mlsanity.com/paper/2011.09590/) (2020)
- [Proof of Travel for Trust-Based Data Validation in V2I Communication](https://mlsanity.com/paper/2104.05070/) (2021)

## Built on by (most cited first, 2 of 2)

- [Multi-Agent Reinforcement Learning-based Cooperative Autonomous Driving in Smart Intersections](https://mlsanity.com/paper/2505.04231/) (2025)
- [Digital Twin-based Cooperative Autonomous Driving in Smart Intersections: A Multi-Agent Reinforcement Learning Approach](https://mlsanity.com/paper/2509.15099/) (2025)

## Abstract (arXiv)

Traffic intersections present significant challenges for the safe and efficient maneuvering of connected and automated vehicles (CAVs). This research proposes an innovative roadside unit (RSU)-assisted cooperative maneuvering system aimed at enhancing road safety and traveling efficiency at intersections for CAVs. We utilize RSUs for real-time traffic data acquisition and train an offline reinforcement learning (RL) algorithm based on human driving data. Evaluation results obtained from hardware-in-loop autonomous driving simulations show that our approach employing the twin delayed deep deterministic policy gradient and behavior cloning (TD3+BC), achieves performance comparable to state-of-the-art autonomous driving systems in terms of safety measures while significantly enhancing travel efficiency by up to 17.38% in intersection areas. This paper makes a pivotal contribution to the field of intelligent transportation systems, presenting a breakthrough solution for improving urban traffic flow and safety at intersections.