A systematic survey of control techniques and applications in connected and automated vehicles
Vehicle control is one of the most critical challenges in autonomous vehicles (AVs) and
connected and automated vehicles (CAVs), and it is paramount in vehicle safety, passenger …
connected and automated vehicles (CAVs), and it is paramount in vehicle safety, passenger …
Graph neural network and reinforcement learning for multi‐agent cooperative control of connected autonomous vehicles
A connected autonomous vehicle (CAV) network can be defined as a set of connected
vehicles including CAVs that operate on a specific spatial scope that may be a road network …
vehicles including CAVs that operate on a specific spatial scope that may be a road network …
A taxonomy for autonomous vehicles considering ambient road infrastructure
To standardize definitions and guide the design, regulation, and policy related to automated
transportation, the Society of Automotive Engineers (SAE) has established a taxonomy …
transportation, the Society of Automotive Engineers (SAE) has established a taxonomy …
State-of-the-art review on recent advancements on lateral control of autonomous vehicles
The most well-known research on driverless vehicles at the moment is connected
autonomous vehicles (CAVs), which reflects the future path for the self driving field. The …
autonomous vehicles (CAVs), which reflects the future path for the self driving field. The …
Why did the AI make that decision? Towards an explainable artificial intelligence (XAI) for autonomous driving systems
User trust has been identified as a critical issue that is pivotal to the success of autonomous
vehicle (AV) operations where artificial intelligence (AI) is widely adopted. For such …
vehicle (AV) operations where artificial intelligence (AI) is widely adopted. For such …
[PDF][PDF] An enhanced eco-driving strategy based on reinforcement learning for connected electric vehicles: Cooperative velocity and lane-changing control
H Ding, W Li, N Xu, J Zhang - Journal of Intelligent and …, 2022 - ieeexplore.ieee.org
Purpose-This study aims to propose an enhanced eco-driving strategy based on
reinforcement learning (RL) to alleviate the mileage anxiety of electric vehicles (EVs) in the …
reinforcement learning (RL) to alleviate the mileage anxiety of electric vehicles (EVs) in the …
Modeling driver's evasive behavior during safety–critical lane changes: Two-dimensional time-to-collision and deep reinforcement learning
Lane changes are complex driving behaviors and frequently involve safety–critical
situations. This study aims to develop a lane-change-related evasive behavior model, which …
situations. This study aims to develop a lane-change-related evasive behavior model, which …
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 …
[PDF][PDF] Development and testing of an image transformer for explainable autonomous driving systems
Purpose-Perception has been identified as the main cause underlying most autonomous
vehicle related accidents. As the key technology in perception, deep learning (DL) based …
vehicle related accidents. As the key technology in perception, deep learning (DL) based …
Leveraging vehicle connectivity and autonomy for highway bottleneck congestion mitigation using reinforcement learning
Automation and connectivity based platforms have great potential for managing highway
traffic congestion including bottlenecks. Speed harmonisation (SH), one of such platforms, is …
traffic congestion including bottlenecks. Speed harmonisation (SH), one of such platforms, is …