# Multi-Task Lane-Free Driving Strategy for Connected and Automated Vehicles: A Multi-Agent Deep Reinforcement Learning Approach

arXiv: https://arxiv.org/abs/2406.14766 · HTML: https://mlsanity.com/paper/2406.14766/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Cooperative and V2X driving, one of 9 directions in End-to-end autonomous driving.
- Builds on 15 of its 54 references that are in the map.
- First posted on arXiv 20 Jun 2024.

## Placed in

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

## Builds on (most cited first, 15 of 15)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments](https://mlsanity.com/paper/1706.02275/) (2017)
- [Deep Reinforcement Learning for Autonomous Driving: A Survey](https://mlsanity.com/paper/2002.00444/) (2020)
- [Understanding Deep Neural Networks with Rectified Linear Units](https://mlsanity.com/paper/1611.01491/) (2016)
- [Deep Reinforcement Learning framework for Autonomous Driving](https://mlsanity.com/paper/1704.02532/) (2017)
- [Safe, Efficient, and Comfortable Velocity Control based on Reinforcement Learning for Autonomous Driving](https://mlsanity.com/paper/1902.00089/) (2019)
- [Automated Speed and Lane Change Decision Making using Deep Reinforcement Learning](https://mlsanity.com/paper/1803.10056/) (2018)
- [Driving in Dense Traffic with Model-Free Reinforcement Learning](https://mlsanity.com/paper/1909.06710/) (2019)
- [An Introduction to Multi-Agent Reinforcement Learning and Review of its Application to Autonomous Mobility](https://mlsanity.com/paper/2203.07676/) (2022)
- [Decision-making Strategy on Highway for Autonomous Vehicles using Deep Reinforcement Learning](https://mlsanity.com/paper/2007.08691/) (2020)
- [Giving Commands to a Self-Driving Car: How to Deal with Uncertain Situations?](https://mlsanity.com/paper/2106.04232/) (2021)
- [Lane-free Artificial-Fluid Concept for Vehicular Traffic](https://mlsanity.com/paper/1905.11642/) (2019)
- [Optimal Trajectory Planning for Connected and Automated Vehicles in Lane-free Traffic with Vehicle Nudging](https://mlsanity.com/paper/2207.09670/) (2022)
- [Optimal Lane-Free Crossing of CAVs through Intersections](https://mlsanity.com/paper/2204.04156/) (2022)

## Abstract (arXiv)

Deep reinforcement learning has shown promise in various engineering applications, including vehicular traffic control. The non-stationary nature of traffic, especially in the lane-free environment with more degrees of freedom in vehicle behaviors, poses challenges for decision-making since a wrong action might lead to a catastrophic failure. In this paper, we propose a novel driving strategy for Connected and Automated Vehicles (CAVs) based on a competitive Multi-Agent Deep Deterministic Policy Gradient approach. The developed multi-agent deep reinforcement learning algorithm creates a dynamic and non-stationary scenario, mirroring real-world traffic complexities and making trained agents more robust. The algorithm's reward function is strategically and uniquely formulated to cover multiple vehicle control tasks, including maintaining desired speeds, overtaking, collision avoidance, and merging and diverging maneuvers. Moreover, additional considerations for both lateral and longitudinal passenger comfort and safety criteria are taken into account. We employed inter-vehicle forces, known as nudging and repulsive forces, to manage the maneuvers of CAVs in a lane-free traffic environment. The proposed driving algorithm is trained and evaluated on lane-free roads using the Simulation of Urban Mobility platform. Experimental results demonstrate the algorithm's efficacy in handling different objectives, highlighting its potential to enhance safety and efficiency in autonomous driving within lane-free traffic environments.