VLP: Vision Language Planning for Autonomous Driving
Autonomous driving is a complex and challenging task that aims at safe motion planning
through scene understanding and reasoning. While vision-only autonomous driving …
through scene understanding and reasoning. While vision-only autonomous driving …
Driver distraction detection using semi-supervised lightweight vision transformer
The continuously increasing number of traffic accidents necessitates addressing distracted
driving, which is responsible for numerous fatalities. Enhancing driver behavior recognition …
driving, which is responsible for numerous fatalities. Enhancing driver behavior recognition …
Efficient mixture-of-expert for video-based driver state and physiological multi-task estimation in conditional autonomous driving
Road safety remains a critical challenge worldwide, with approximately 1.35 million fatalities
annually attributed to traffic accidents, often due to human errors. As we advance towards …
annually attributed to traffic accidents, often due to human errors. As we advance towards …
Evaluation and comparison of visual language models for transportation engineering problems
S Prajapati, T Singh, C Hegde… - arXiv preprint arXiv …, 2024 - arxiv.org
Recent developments in vision language models (VLM) have shown great potential for
diverse applications related to image understanding. In this study, we have explored state-of …
diverse applications related to image understanding. In this study, we have explored state-of …
Zone-YOLO: Vision-Language Object Detection Using Zone Prompt
J Yang, N Jia, X Liu, R Fan, Y Sun… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Object detection in complex traffic scenarios is crucial for Intelligent Transportation Systems
(ITS). At present, most real-time traffic object detection methods primarily rely on YOLO-style …
(ITS). At present, most real-time traffic object detection methods primarily rely on YOLO-style …
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 …
Deep Unsupervised Transfer Adversarial Network for Abnormal Driving Behavior Recognition Based on Smartphone Sensors
X Chen, R Qu, F Zhao - IEEE Sensors Journal, 2024 - ieeexplore.ieee.org
Abnormal driving has been widely recognized as one of the key factors highly related to
traffic accidents. Smartphones mounted on vehicles can be leveraged to record a variety of …
traffic accidents. Smartphones mounted on vehicles can be leveraged to record a variety of …
MELD3: Integrating Multi-Task Ensemble Learning for Driver Distraction Detection
G Azizoglu, AN Toprak - IEEE Access, 2024 - ieeexplore.ieee.org
Detecting and alerting distracted drivers is crucial to prevent traffic accidents. Although
numerous studies have been proposed that use deep learning methods to detect driver …
numerous studies have been proposed that use deep learning methods to detect driver …
When language and vision meet road safety: leveraging multimodal large language models for video-based traffic accident analysis
The increasing availability of traffic videos functioning on a 24/7/365 time scale has the great
potential of increasing the spatio-temporal coverage of traffic accidents, which will help …
potential of increasing the spatio-temporal coverage of traffic accidents, which will help …
PlanAgent: A Multi-modal Large Language Agent for Closed-loop Vehicle Motion Planning
Vehicle motion planning is an essential component of autonomous driving technology.
Current rule-based vehicle motion planning methods perform satisfactorily in common …
Current rule-based vehicle motion planning methods perform satisfactorily in common …