A survey on driver behavior analysis from in-vehicle cameras
Distracted or drowsy driving is unsafe driving behavior responsible for thousands of crashes
every year. Studying driver behavior has challenges associated with observing drivers in …
every year. Studying driver behavior has challenges associated with observing drivers in …
Detecting and recognizing driver distraction through various data modality using machine learning: A review, recent advances, simplified framework and open …
Driver distraction is one of the main causes of fatal traffic accidents. Therefore, the ability to
detect driver inattention is essential in building a safe yet intelligent transportation system …
detect driver inattention is essential in building a safe yet intelligent transportation system …
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 …
A novel learning model of driver fatigue features representation for steering wheel angle
In the field of advanced driver assistance systems (ADAS), effective learning of driver fatigue
characteristics representation is a major challenge due to uncertainties of both real roads …
characteristics representation is a major challenge due to uncertainties of both real roads …
A survey on vision-based driver distraction analysis
Motor vehicle crashes are great threats to our life, which may result in numerous fatalities, as
well as tremendous economic and societal costs. Driver inattention, either distraction or …
well as tremendous economic and societal costs. Driver inattention, either distraction or …
Human–machine interaction in intelligent and connected vehicles: A review of status quo, issues, and opportunities
Human–Machine Interaction (HMI) in Intelligent and Connected Vehicles (ICVs) has drawn
great attention in recent years due to its potentially significant positive impacts on the …
great attention in recent years due to its potentially significant positive impacts on the …
Deep learning-based hard spatial attention for driver in-vehicle action monitoring
Distracted driving is one of the main causes of deaths and injuries in the world. Monitoring
driver behaviors through Driver Action Recognition (DAR) contributes significantly to …
driver behaviors through Driver Action Recognition (DAR) contributes significantly to …
CNN-based driving maneuver classification using multi-sliding window fusion
Driving behavior classification has received increasing attention in recent years, where
driving maneuver classification plays an important role. The first step of building a driving …
driving maneuver classification plays an important role. The first step of building a driving …
TransDARC: Transformer-based driver activity recognition with latent space feature calibration
Traditional video-based human activity recognition has experienced remarkable progress
linked to the rise of deep learning, but this effect was slower as it comes to the downstream …
linked to the rise of deep learning, but this effect was slower as it comes to the downstream …
Towards Implicit Interaction in Highly Automated Vehicles-A Systematic Literature Review
The inclusion of in-vehicle sensors and increased intention and state recognition
capabilities enable implicit in-vehicle interaction. Starting from a systematic literature review …
capabilities enable implicit in-vehicle interaction. Starting from a systematic literature review …