Optimizing Urban Intersection Management in Mixed Traffic Using Deep Reinforcement Learning and Genetic Algorithms

Optimizing Urban Intersection Management in Mixed Traffic Using Deep Reinforcement Learning and Genetic Algorithms

This study aims to optimize lane configurations at urban intersections within mixed traffic environments, integrating both Connected Autonomous Vehicles (CAVs) and Human-driven Vehicles (HVs). By employing genetic algorithm and deep reinforcement learning (DRL), the research seeks to dynamically adj...

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Bibliographic Details
Main Authors: Jiajun Shen, Yu Wang, Haoyu Wang, Guanyu Fu, Zhipeng Zhou, Jingxin Dong
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Connected autonomous vehicles
deep reinforcement learning
genetic algorithm
lane configuration optimization
mixed traffic environment
Online Access:https://ieeexplore.ieee.org/document/10909487/
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https://ieeexplore.ieee.org/document/10909487/

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