Abnormal driving detection with normalized driving behavior data: A deep learning approach
J Hu, X Zhang, S Maybank - IEEE transactions on vehicular …, 2020 - ieeexplore.ieee.org
Abnormal driving may cause serious danger to both the driver and the public. Existing
detectors of abnormal driving behavior are mainly based on shallow models, which require …
detectors of abnormal driving behavior are mainly based on shallow models, which require …
Unsupervised scalable multimodal driving anomaly detection
Driving anomaly detection aims to identify objects, events or actions that can increase the
risk of accidents, reducing road safety. While supervised approaches can effectively identify …
risk of accidents, reducing road safety. While supervised approaches can effectively identify …
Abnormal driving detection based on normalized driving behavior
J Hu, L Xu, X He, W Meng - IEEE Transactions on Vehicular …, 2017 - ieeexplore.ieee.org
Abnormal driving behavior may cause serious danger to both the driver and the public. In
this study, we propose to detect abnormal driving by analyzing normalized driving behavior …
this study, we propose to detect abnormal driving by analyzing normalized driving behavior …
[HTML][HTML] Unusual driver behavior detection in videos using deep learning models
Anomalous driving behavior detection is becoming more popular since it is vital in ensuring
the safety of drivers and passengers in vehicles. Road accidents happen for various …
the safety of drivers and passengers in vehicles. Road accidents happen for various …
[HTML][HTML] A deep encoder-decoder network for anomaly detection in driving trajectory behavior under spatio-temporal context
Traditional trajectory anomaly detection aims to find abnormal trajectory points or sequences
using data mining techniques. As a comparison, we focus on the evaluation of the …
using data mining techniques. As a comparison, we focus on the evaluation of the …
Efficient driver anomaly detection via conditional temporal proposal and classification network
Detecting driver inattentive behaviors is crucial for driving safety in a driver monitoring
system (DMS). Recent works treat driver distraction detection as a multiclass action …
system (DMS). Recent works treat driver distraction detection as a multiclass action …
Detecting anomalous driving behavior using neural networks
The ability to robustly detect abnormal driving behavior has the potential to limit traffic
accidents and save many lives. Abnormal driving behavior that threatens road safety …
accidents and save many lives. Abnormal driving behavior that threatens road safety …
SafeDrive: Online driving anomaly detection from large-scale vehicle data
Identifying driving anomalies is of great significance for improving driving safety. The
development of the Internet-of-Vehicle (IoV) technology has made it feasible to acquire big …
development of the Internet-of-Vehicle (IoV) technology has made it feasible to acquire big …
A comparative study of aggressive driving behavior recognition algorithms based on vehicle motion data
Y Ma, Z Zhang, S Chen, Y Yu, K Tang - IEEE Access, 2018 - ieeexplore.ieee.org
Aggressive driving, amongst inappropriate driving behaviors, is largely responsible for
leading to traffic accidents, which threatens both the safety and property of human beings …
leading to traffic accidents, which threatens both the safety and property of human beings …
[HTML][HTML] HSDDD: A hybrid scheme for the detection of distracted driving through fusion of deep learning and handcrafted features
Traditional methods for behavior detection of distracted drivers are not capable of capturing
driver behavior features related to complex temporal features. With the goal to improve …
driver behavior features related to complex temporal features. With the goal to improve …