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Found 12 papers, 3 papers with code
Enhancing High-Speed Cruising Performance of Autonomous Vehicles through Integrated Deep Reinforcement Learning Framework
High-speed cruising scenarios with mixed traffic greatly challenge the road safety of autonomous vehicles (AVs).
Bi-Level Control of Weaving Sections in Mixed Traffic Environments with Connected and Automated Vehicles
Connected and automated vehicles (CAVs) can be beneficial for improving the operation of highway bottlenecks such as weaving sections.
Safe Reinforcement Learning-Based Eco-Driving Control for Mixed Traffic Flows With Disturbances
This paper presents a safe learning-based eco-driving framework tailored for mixed traffic flows, which aims to optimize energy efficiency while guaranteeing safety during real-system operations.
A Parameter Privacy-Preserving Strategy for Mixed-Autonomy Platoon Control
First, we integrate a parameter privacy filter into LCC to protect sensitive car-following parameters.
Privacy-Preserving Data Fusion for Traffic State Estimation: A Vertical Federated Learning Approach
This paper proposes a privacy-preserving data fusion method for traffic state estimation (TSE).
Enhancing System-Level Safety in Mixed-Autonomy Platoon via Safe Reinforcement Learning
Second, we incorporate system-level safety constraints into our proposed method to account for the safety of both CAVs and the following HDVs to address the potential collisions due to irrational human driving behavior.
Graph Reinforcement Learning for Network Control via Bi-Level Optimization
Optimization problems over dynamic networks have been extensively studied and widely used in the past decades to formulate numerous real-world problems.
Privacy-Preserving Adaptive Traffic Signal Control in a Connected Vehicle Environment
We further develop a linear optimization model for adaptive signal control based on the traffic variables obtained via the data aggregation mechanism.
Differentially Private Stochastic Convex Optimization for Network Routing Applications
In this paper, we present a differentially private algorithm for network routing problems.
Time-to-Green predictions for fully-actuated signal control systems with supervised learning
Recently, efforts have been made to standardize signal phase and timing (SPaT) messages.
Graph Meta-Reinforcement Learning for Transferable Autonomous Mobility-on-Demand
Autonomous Mobility-on-Demand (AMoD) systems represent an attractive alternative to existing transportation paradigms, currently challenged by urbanization and increasing travel needs.
Graph Neural Network Reinforcement Learning for Autonomous Mobility-on-Demand Systems
Autonomous mobility-on-demand (AMoD) systems represent a rapidly developing mode of transportation wherein travel requests are dynamically handled by a coordinated fleet of robotic, self-driving vehicles.
