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 …
Pavement defect detection with deep learning: A comprehensive survey
Pavement defect detection is of profound significance regarding road safety, so it has been a
trending research topic. In the past years, deep learning based methods have turned into a …
trending research topic. In the past years, deep learning based methods have turned into a …
[HTML][HTML] Gpt-4 enhanced multimodal grounding for autonomous driving: Leveraging cross-modal attention with large language models
In the field of autonomous vehicles (AVs), accurately discerning commander intent and
executing linguistic commands within a visual context presents a significant challenge. This …
executing linguistic commands within a visual context presents a significant challenge. This …
Toward personalized decision making for autonomous vehicles: a constrained multi-objective reinforcement learning technique
Reinforcement learning promises to provide a state-of-the-art solution to the decision
making problem of autonomous driving. Nonetheless, numerous real-world decision making …
making problem of autonomous driving. Nonetheless, numerous real-world decision making …
Bat: Behavior-aware human-like trajectory prediction for autonomous driving
The ability to accurately predict the trajectory of surrounding vehicles is a critical hurdle to
overcome on the journey to fully autonomous vehicles. To address this challenge, we …
overcome on the journey to fully autonomous vehicles. To address this challenge, we …
USV formation navigation decision-making through hybrid deep reinforcement learning using self-attention mechanism
Z Cui, W Guan, X Zhang - Expert Systems with Applications, 2024 - Elsevier
To address the challenging of balancing Unmanned Surface Vessel (USV) autonomous
collision avoidance and formation maintenance in uncertain environments, a formation …
collision avoidance and formation maintenance in uncertain environments, a formation …
Simulation of vehicle interaction behavior in merging scenarios: A deep maximum entropy-inverse reinforcement learning method combined with game theory
W Li, F Qiu, L Li, Y Zhang… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Simulation testing based on virtual scenarios can improve the efficiency of safety testing for
high-level autonomous vehicles (AVs). In most traffic scenarios, such as merging scenarios …
high-level autonomous vehicles (AVs). In most traffic scenarios, such as merging scenarios …
Deep demand prediction: An enhanced conformer model with cold-start adaptation for origin–destination ride-hailing demand prediction
In intelligent transportation systems, one key challenge for managing ride-hailing services is
the balancing of traffic supply and demand while meeting passenger needs within vehicle …
the balancing of traffic supply and demand while meeting passenger needs within vehicle …
SOSMaskFuse: An infrared and visible image fusion architecture based on salient object segmentation mask
High-quality fusion images with infrared and visible information contribute to intelligent and
safe driving. In the infrared and visible fusion images, the useless noise information in …
safe driving. In the infrared and visible fusion images, the useless noise information in …
Toward trustworthy decision-making for autonomous vehicles: A robust reinforcement learning approach with safety guarantees
While autonomous vehicles are vital components of intelligent transportation systems,
ensuring the trustworthiness of decision-making remains a substantial challenge in realizing …
ensuring the trustworthiness of decision-making remains a substantial challenge in realizing …