Federated learning for connected and automated vehicles: A survey of existing approaches and challenges
Machine learning (ML) is widely used for key tasks in Connected and Automated Vehicles
(CAV), including perception, planning, and control. However, its reliance on vehicular data …
(CAV), including perception, planning, and control. However, its reliance on vehicular data …
Fuzzy adaptive finite-time event-triggered control of time-varying formation for nonholonomic multirobot systems
Y Li, S Dong, K Li - IEEE Transactions on Intelligent Vehicles, 2023 - ieeexplore.ieee.org
This article studies the problem of fuzzy adaptive finite-time event-triggered time-varying
formation tracking control for nonholonomic multirobot systems with multiple constraints. The …
formation tracking control for nonholonomic multirobot systems with multiple constraints. The …
Cooperative path following control of USV-UAVs considering low design complexity and command transmission requirements
This article investigates a robust cooperative path following control algorithm for an
unmanned surface vessel and unmanned aerial vehicles (USV-UAVs) that releases the …
unmanned surface vessel and unmanned aerial vehicles (USV-UAVs) that releases the …
Event-based predefined-time second-order practical consensus with application to connected automated vehicles
J Liu, J Shi, Y Wu, X Wang, J Sun… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
In this study, an event-based control strategy is proposed for second-order disturbed multi-
agent systems (MASs) to achieve predefined-time practical consensus. In comparison to the …
agent systems (MASs) to achieve predefined-time practical consensus. In comparison to the …
Recent advances in reinforcement learning-based autonomous driving behavior planning: A survey
Autonomous driving (AD) holds the potential to revolutionize transportation efficiency, but its
success hinges on robust behavior planning (BP) mechanisms. Reinforcement learning (RL) …
success hinges on robust behavior planning (BP) mechanisms. Reinforcement learning (RL) …
The road ahead: DAO-secured V2X infrastructures for safe and smart vehicular management
Vehicle-to-Everything (V2X) technology relies on wireless communication and coordination,
aiming to improve road safety and traffic efficiency by orchestrating the interaction among …
aiming to improve road safety and traffic efficiency by orchestrating the interaction among …
Event-triggered model predictive control with deep reinforcement learning for autonomous driving
Event-triggered model predictive control (eMPC) is a popular optimal control method with an
aim to alleviate the computation and/or communication burden of MPC. However, it …
aim to alleviate the computation and/or communication burden of MPC. However, it …
Rl-driven mppi: Accelerating online control laws calculation with offline policy
Y Qu, H Chu, S Gao, J Guan, H Yan… - IEEE Transactions …, 2023 - ieeexplore.ieee.org
Model Predictive Path Integral (MPPI) is a recognized sampling-based approach for finite
horizon optimal control problems. However, the efficacy and computational efficiency of …
horizon optimal control problems. However, the efficacy and computational efficiency of …
Analysis of driving behavior in unprotected left turns for autonomous vehicles using ensemble deep clustering
The advent of autonomous driving technology offers transformative potential in mitigating
traffic congestion and enhancing road safety. A particularly challenging aspect of traffic …
traffic congestion and enhancing road safety. A particularly challenging aspect of traffic …
Autonomous driving policy learning from demonstration using regression loss function
Y Xiao, Y An, T Li, N Wu, W He, P Li - Knowledge-Based Systems, 2024 - Elsevier
How to efficiently train a high-performance autonomous driving agent remains a realistic and
challenging issue. Although in the literature, many techniques, especially deep …
challenging issue. Although in the literature, many techniques, especially deep …