A review of vision-based traffic semantic understanding in ITSs
J Chen, Q Wang, HH Cheng, W Peng… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
A semantic understanding of road traffic can help people understand road traffic flow
situations and emergencies more accurately and provide a more accurate basis for anomaly …
situations and emergencies more accurately and provide a more accurate basis for anomaly …
A review of driver fatigue detection and its advances on the use of RGB-D camera and deep learning
Driver fatigue is an essential reason for traffic accidents, which poses a severe threat to
people's lives and property. In this review, we summarize the latest research findings and …
people's lives and property. In this review, we summarize the latest research findings and …
Driver anomaly quantification for intelligent vehicles: A contrastive learning approach with representation clustering
Driver anomaly quantification is a fundamental capability to support human-centric driving
systems of intelligent vehicles. Existing studies usually treat it as a classification task and …
systems of intelligent vehicles. Existing studies usually treat it as a classification task and …
Quantitative identification of driver distraction: A weakly supervised contrastive learning approach
Accurate recognition of driver distraction is significant for the design of human-machine
cooperation driving systems. Existing studies mainly focus on classifying varied distracted …
cooperation driving systems. Existing studies mainly focus on classifying varied distracted …
Behavioral intention prediction in driving scenes: A survey
In driving scenes, road agents often engage in frequent interaction and strive to understand
their surroundings. Ego-agent (each road agent itself) predicts what behavior will be …
their surroundings. Ego-agent (each road agent itself) predicts what behavior will be …
A Robust driver emotion recognition method based on high-purity feature separation
Since emotions generally affect driver's behavior, judgment, and reaction time, accurately
identifying driver's emotions is of great significance to improve the safety and comfort of …
identifying driver's emotions is of great significance to improve the safety and comfort of …
100-driver: a large-scale, diverse dataset for distracted driver classification
J Wang, W Li, F Li, J Zhang, Z Wu… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Distracted driver classification (DDC) plays an important role in ensuring driving safety.
Although many datasets are introduced to support the study of DDC, most of them are small …
Although many datasets are introduced to support the study of DDC, most of them are small …
Leveraging driver vehicle and environment interaction: Machine learning using driver monitoring cameras to detect drunk driving
Excessive alcohol consumption causes disability and death. Digital interventions are
promising means to promote behavioral change and thus prevent alcohol-related harm …
promising means to promote behavioral change and thus prevent alcohol-related harm …
[HTML][HTML] DDSS: Driver decision support system based on the driver behaviour prediction to avoid accidents in intelligent transport system
Accidents caused by drivers who exhibit unusual behavior are putting road safety at ever-
greater risk. When one or more vehicle nodes behave in this way, it can put other nodes in …
greater risk. When one or more vehicle nodes behave in this way, it can put other nodes in …
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 …