Human-factors-in-driving-loop: Driver identification and verification via a deep learning approach using psychological behavioral data
Driver identification has been popular in the field of driving behavior analysis, which has a
broad range of applications in anti-thief, driving style recognition, insurance strategy, and …
broad range of applications in anti-thief, driving style recognition, insurance strategy, and …
Machine learning applications in surface transportation systems: A literature review
Surface transportation has evolved through technology advancements using parallel
knowledge areas such as machine learning (ML). However, the transportation industry has …
knowledge areas such as machine learning (ML). However, the transportation industry has …
Driving behavior analysis guidelines for intelligent transportation systems
MN Azadani, A Boukerche - IEEE transactions on intelligent …, 2021 - ieeexplore.ieee.org
The advent of in-vehicle networking systems as well as state-of-the-art sensors and
communication technologies have facilitated the collection of large volume and almost real …
communication technologies have facilitated the collection of large volume and almost real …
Deep unsupervised multi-modal fusion network for detecting driver distraction
Y Zhang, Y Chen, C Gao - Neurocomputing, 2021 - Elsevier
The risk of incurring a road traffic crash has increased year by year. Studies show that lack of
attention during driving is one of the major causes of traffic accidents. In this work, in order to …
attention during driving is one of the major causes of traffic accidents. In this work, in order to …
Genfollower: Enhancing car-following prediction with large language models
X Chen, M Peng, PH Tiu, Y Wu, J Chen… - IEEE Transactions …, 2024 - ieeexplore.ieee.org
Accurate modeling of car-following behaviors is crucial for autonomous driving systems.
While recent advancements in large language models (LLMs) have shown promise in …
While recent advancements in large language models (LLMs) have shown promise in …
Securing smart vehicles from relay attacks using machine learning
Due to the rapid developments in intelligent transportation systems, modern vehicles have
turned into intelligent transportation means which are able to exchange data through various …
turned into intelligent transportation means which are able to exchange data through various …
Driver identification and verification from smartphone accelerometers using deep neural networks
SH Sánchez, RF Pozo… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
This paper addresses driver identification and verification using Deep Learning (DL) on tri-
axial accelerometer signals from drivers' smartphones. The proposed driver identification …
axial accelerometer signals from drivers' smartphones. The proposed driver identification …
Investigations on driver unique identification from smartphone's GPS data alone
Driver identification is an emerging area of interest in vehicle telematics, automobile control,
and insurance. Recent body of works indicates that it may be possible to uniquely identify a …
and insurance. Recent body of works indicates that it may be possible to uniquely identify a …
Driver identification using vehicular sensing data: A deep learning approach
MN Azadani, A Boukerche - 2021 IEEE Wireless …, 2021 - ieeexplore.ieee.org
Driver identification plays a pivotal role in the design of advanced driver assistant systems.
The continued development of in-vehicle networking systems, CAN-bus technology, and the …
The continued development of in-vehicle networking systems, CAN-bus technology, and the …
A hybrid deep learning approach for driver anomalous lane changing identification
P Fan, J Guo, Y Wang, JS Wijnands - Accident Analysis & Prevention, 2022 - Elsevier
Reliable knowledge of driving states is of great importance to ensure road safety. Anomaly
detection in driving behavior means recognizing anomalous driving states as a direct result …
detection in driving behavior means recognizing anomalous driving states as a direct result …