Dynamic urban traffic rerouting with fog‐cloud reinforcement learning
Dynamic rerouting has been touted as a solution for urban traffic congestion. However, its
implementation is stymied by the complexity of urban traffic. To address this, recent studies …
implementation is stymied by the complexity of urban traffic. To address this, recent studies …
[HTML][HTML] Human as AI mentor: Enhanced human-in-the-loop reinforcement learning for safe and efficient autonomous driving
Despite significant progress in autonomous vehicles (AVs), the development of driving
policies that ensure both the safety of AVs and traffic flow efficiency has not yet been fully …
policies that ensure both the safety of AVs and traffic flow efficiency has not yet been fully …
Longitudinal control of connected and automated vehicles among signalized intersections in mixed traffic flow with deep reinforcement learning approach
Trajectory optimization for connected automated vehicles (CAVs) is an effective method to
improve the overall performance of urban traffic. At the same time, the emergence of deep …
improve the overall performance of urban traffic. At the same time, the emergence of deep …
Traffic efficiency and fairness optimisation for autonomous intersection management based on reinforcement learning
Autonomous Intersection Management (AIM) for high-level Connected and Automated
Vehicles (CAVs) has evolved from rule-based to optimisation-based policies. However, at …
Vehicles (CAVs) has evolved from rule-based to optimisation-based policies. However, at …
Distributed Cooperative Driving Strategy for Connected Automated Vehicles at Unsignalized Intersections Based on Monte Carlo Method
H Li, W Dong, L Lu, Y Wang… - Journal of Advanced …, 2024 - Wiley Online Library
One of the most important goals of cooperative driving is to control connected automated
vehicles (CAVs) passing through conflict areas safely and efficiently without traffic signals …
vehicles (CAVs) passing through conflict areas safely and efficiently without traffic signals …
Cooperative lane-changing in mixed traffic: a deep reinforcement learning approach
Deep Reinforcement Learning (DRL) has made remarkable progress in autonomous vehicle
decision-making and execution control to improve traffic performance. This paper introduces …
decision-making and execution control to improve traffic performance. This paper introduces …
Transfusor: Transformer Diffusor for Controllable Human-like Generation of Vehicle Lane Changing Trajectories
With ongoing development of autonomous driving systems and increasing desire for
deployment, researchers continue to seek reliable approaches for ADS systems. The virtual …
deployment, researchers continue to seek reliable approaches for ADS systems. The virtual …
A distributed deep reinforcement learning-based longitudinal control strategy for connected automated vehicles combining attention mechanism
With the rapid development of connected automated vehicles (CAVs), the trajectory control
of CAVs has become a focus in traffic engineering. This paper proposes a distributed deep …
of CAVs has become a focus in traffic engineering. This paper proposes a distributed deep …
PFL-LSTR: A privacy-preserving framework for driver intention inference based on in-vehicle and out-vehicle information
Intelligent vehicle anticipation of the movement intentions of other drivers can reduce
collisions. Typically, when a human driver of another vehicle (referred to as the target …
collisions. Typically, when a human driver of another vehicle (referred to as the target …
Learning-Based Planning for Connected and Autonomous Vehicles: Towards Information Fusion and Trustworthy AI
J Dong - 2024 - hammer.purdue.edu
Motion planning for Autonomous Vehicles (AVs) and Connected Autonomous Vehicles
(CAVs) involves the crucial task of translating road environmental data obtained from …
(CAVs) involves the crucial task of translating road environmental data obtained from …