Need data for driver behaviour analysis? Presenting the public UAH-DriveSet
Driving analysis is a recent topic of interest due to the growing safety concerns in vehicles.
However, the lack of publicly available driving data currently limits the progress on this field …
However, the lack of publicly available driving data currently limits the progress on this field …
Driving behavior classification based on oversampled signals of smartphone embedded sensors using an optimized stacked-LSTM neural networks
MA Khodairy, G Abosamra - IEEE Access, 2021 - ieeexplore.ieee.org
Driving behavior classification is an essential real-world requirement in different contexts. In
traffic safety, avoiding traffic accidents by taking corrective actions against aggressive …
traffic safety, avoiding traffic accidents by taking corrective actions against aggressive …
Importance weighted Gaussian process regression for transferable driver behaviour learning in the lane change scenario
Due to advantages of handling problems with nonlinearity and uncertainty, Gaussian
process regression (GPR) has been widely used in the area of driver behaviour modelling …
process regression (GPR) has been widely used in the area of driver behaviour modelling …
Dangerous driving behavior detection using smartphone sensors
F Li, H Zhang, H Che, X Qiu - 2016 IEEE 19th international …, 2016 - ieeexplore.ieee.org
This paper presents a smartphone based dangerous driving behavior detection method.
This method is meaningful in applying in driver risk behavior monitoring system, vehicle safe …
This method is meaningful in applying in driver risk behavior monitoring system, vehicle safe …
An abnormal driving behavior recognition algorithm based on the temporal convolutional network and soft thresholding
Most traffic accidents are caused by bad driving habits. Online monitoring of the abnormal
driving behaviors of drivers can help reduce traffic accidents. Recently, abnormal driving …
driving behaviors of drivers can help reduce traffic accidents. Recently, abnormal driving …
Vehicle theft detection by generative adversarial networks on driving behavior
Human driving behavior can be a unique fingerprint to identify individual drivers and can be
used for vehicle theft detection. Prior research often uses supervised learning to classify …
used for vehicle theft detection. Prior research often uses supervised learning to classify …
Auto++ detecting cars using embedded microphones in real-time
In this work, we propose a system that detects approaching cars for smartphone users. In
addition to detecting the presence of a vehicle, it can also estimate the vehicle's driving …
addition to detecting the presence of a vehicle, it can also estimate the vehicle's driving …
An advanced vehicle detection and tracking scheme for self-driving cars
In this paper, an advanced and reliable vehicle detection and tracking technique is
proposed and implemented and given the name “Real-Time Vehicle Detection and …
proposed and implemented and given the name “Real-Time Vehicle Detection and …
Transferable driver behavior learning via distribution adaption in the lane change scenario
Because of the high accuracy and low cost, learning-based methods have been widely used
to model driver behaviors in various scenarios. However, the performance of learning-based …
to model driver behaviors in various scenarios. However, the performance of learning-based …
Vehicle detection based on visual attention mechanism and adaboost cascade classifier in intelligent transportation systems
X Chen, L Liu, Y Deng, X Kong - Optical and Quantum Electronics, 2019 - Springer
Robust and efficient vehicle detection is an essential task in intelligent transportation
systems (ITS). Unfortunately, due to a great diversity of vehicle profiles and outdoor …
systems (ITS). Unfortunately, due to a great diversity of vehicle profiles and outdoor …