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Enhancing the Minimum Awareness Failure Distance in V2X Communications: A Deep Reinforcement Learning Approach.

Anthony Kyung Guzmán Leguel¹, Hoa-Hung Nguyen¹, David Gómez Gutiérrez^2,3

¹Department of Electrical Engineering, Pusan National University, Busan 46241, Republic of Korea.

Sensors (Basel, Switzerland)

|September 28, 2024

Summary

This summary is machine-generated.

This study introduces a new definition of vehicle awareness in V2X communications. A deep reinforcement learning framework (DRL-JCBRTP) improves driving safety by optimizing beaconing strategies.

Keywords:

beaconing deep reinforcement learning minimum awareness failure distance vehicle awareness vehicle-to-everything (V2X) communications

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Area of Science:

Vehicular communication networks
Intelligent transportation systems
Machine learning for autonomous driving

Background:

Cooperative awareness is crucial for vehicular networks, typically defined by vehicles' perception and sharing of kinematic data.
Existing awareness metrics often overlook the complexities of vehicle detection, tracking, and safety distance maintenance.

Purpose of the Study:

To propose a novel, multi-faceted definition of awareness in Vehicle-to-Everything (V2X) communications.
To introduce a deep reinforcement learning framework for joint control of beacon rate and transmit power (DRL-JCBRTP) to enhance V2X awareness.
To improve driving safety by minimizing awareness failure probability and maximizing awareness distance.

Main Methods:

Developed a deep reinforcement learning framework (DRL-JCBRTP) utilizing LSTM-based actor and MLP-based critic networks within the Soft Actor-Critic (SAC) algorithm.
Implemented an innovative reward function to increase the minimum awareness failure distance, leveraging local state information.
Conducted simulations using SLMLab-Gym-VEINS to evaluate the proposed framework's performance.

Main Results:

The DRL-JCBRTP scheme significantly outperformed existing beaconing schemes in simulations.
Demonstrated a reduction in awareness failure probability.
Showcased an increase in the maximum achievable awareness distance.

Conclusions:

The proposed DRL-JCBRTP framework offers a superior approach to enhancing cooperative awareness in V2X networks.
Optimizing beacon rate and transmit power through deep reinforcement learning leads to improved driving safety.
The novel definition and framework advance the field of intelligent transportation systems.