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) …
Vision-language models can identify distracted driver behavior from naturalistic videos
Recognizing the activities causing distraction in real-world driving scenarios is critical for
ensuring the safety and reliability of both drivers and pedestrians on the roadways …
ensuring the safety and reliability of both drivers and pedestrians on the roadways …
AFM3D: An Asynchronous Federated Meta-Learning Framework for Driver Distraction Detection
Driver Distraction Detection (3D) is of great significance in helping intelligent vehicles
decide whether to remind drivers or take over the driving task and avoid traffic accidents …
decide whether to remind drivers or take over the driving task and avoid traffic accidents …
Context-Aware Driver Attention Estimation Using Multi-Hierarchy Saliency Fusion With Gaze Tracking
Accurate vision-based driver attention estimation is a challenging task due to the limitations
of the visual sensor, and it is a critical and fundamental function of building a human …
of the visual sensor, and it is a critical and fundamental function of building a human …
Enhancing Cross-Dataset Performance of Distracted Driving Detection With Score Softmax Classifier and Dynamic Gaussian Smoothing Supervision
Deep neural networks enable real-time monitoring of in-vehicle drivers, facilitating the timely
prediction of distractions, fatigue, and potential hazards. This technology is now integral to …
prediction of distractions, fatigue, and potential hazards. This technology is now integral to …
Cognitive Workload Estimation in Conditionally Automated Vehicles Using Transformer Networks Based on Physiological Signals
Though driving automation promises to improve driving safety, drivers are still required to be
ready to retake control in conditionally automated vehicles, which are defined by the Society …
ready to retake control in conditionally automated vehicles, which are defined by the Society …
Highly discriminative driver distraction detection method based on Swin transformer
Z Zhang, L Yang, C Lv - Vehicles, 2024 - mdpi.com
Driver distraction detection not only helps to improve road safety and prevent traffic
accidents, but also promotes the development of intelligent transportation systems, which is …
accidents, but also promotes the development of intelligent transportation systems, which is …
Driver Distraction Behavior Detection Framework Based on the DWPose Model, Kalman Filtering, and Multi-Transformer
X Shi - IEEE Access, 2024 - ieeexplore.ieee.org
Driver distraction behavior recognition is crucial for improving driving safety. Traditional end-
to-end driver distraction detection models are susceptible to factors such as the driving …
to-end driver distraction detection models are susceptible to factors such as the driving …
Leveraging Anomaly Detection for Affective Computing Applications
S Hamieh - 2024 - theses.hal.science
Recent technological advancements have paved the way for automation in various sectors,
from education to autonomous driving, collaborative robots, and customer service. This has …
from education to autonomous driving, collaborative robots, and customer service. This has …
Mechanism Behind Hazard Recognition in Potential Rear-End Collisions: An Eeg Study of Cross-Frequency Phase Synchrony in Complex Brain Networks
K Jiang, W Yang, X Tang, B Liu, Z Chu, S Lu… - Available at SSRN … - papers.ssrn.com
Rear-end collisions, primarily resulting from subconscious braking errors because of drivers'
misrecognition of hazards, constitute a significant factor in traffic accidents. A topic of popular …
misrecognition of hazards, constitute a significant factor in traffic accidents. A topic of popular …