Attention for vision-based assistive and automated driving: A review of algorithms and datasets

I Kotseruba, JK Tsotsos - IEEE transactions on intelligent …, 2022 - ieeexplore.ieee.org
Driving safety has been a concern since the first cars appeared on the streets. Driver
inattention has been singled out as a major cause of accidents early on. This is hardly …

Attention-based interrelation modeling for explainable automated driving

Z Zhang, R Tian, R Sherony… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Automated driving desires better performance on tasks like motion planning and interacting
with pedestrians in mixed-traffic environments. Deep learning algorithms can achieve high …

Drivers and barriers to implementation of connected, automated, shared, and electric vehicles: An agenda for future research

A Mahdavian, A Shojaei, S Mccormick… - IEEE …, 2021 - ieeexplore.ieee.org
Several converging trends appear to reshape the way citizens and goods move about.
These trends are social, including urbanization and population growth, and technological …

ID-YOLO: Real-time salient object detection based on the driver's fixation region

L Qin, Y Shi, Y He, J Zhang, X Zhang… - IEEE Transactions …, 2022 - ieeexplore.ieee.org
Object detection is an important task for self-driving vehicles or advanced driver assistant
systems (ADASs). Additionally, visual selective attention is a crucial neural mechanism in a …

Look both ways: Self-supervising driver gaze estimation and road scene saliency

I Kasahara, S Stent, HS Park - European Conference on Computer Vision, 2022 - Springer
We present a new on-road driving dataset, called “Look Both Ways”, which contains
synchronized video of both driver faces and the forward road scene, along with ground truth …

Orclsim: A system architecture for studying bicyclist and pedestrian physiological behavior through immersive virtual environments

X Guo, A Angulo, E Robartes, TD Chen… - Journal of advanced …, 2022 - Wiley Online Library
Injuries and fatalities for vulnerable road users, especially bicyclists and pedestrians, are on
the rise. To better inform design for vulnerable road users, we need to evaluate how bicyclist …

Multimodal driver state modeling through unsupervised learning

A Tavakoli, A Heydarian - Accident Analysis & Prevention, 2022 - Elsevier
Naturalistic driving data (NDD) can help understand drivers' reactions to each driving
scenario and provide personalized context to driving behavior. However, NDD requires a …

Cognitive accident prediction in driving scenes: A multimodality benchmark

J Fang, LL Li, K Yang, Z Zheng, J Xue… - arXiv preprint arXiv …, 2022 - arxiv.org
Traffic accident prediction in driving videos aims to provide an early warning of the accident
occurrence, and supports the decision making of safe driving systems. Previous works …

Cross-Modality Graph-Based Language and Sensor Data Co-Learning of Human-Mobility Interaction

M Tabatabaie, S He, KG Shin - Proceedings of the ACM on Interactive …, 2023 - dl.acm.org
Learning the human--mobility interaction (HMI) on interactive scenes (eg, how a vehicle
turns at an intersection in response to traffic lights and other oncoming vehicles) can …

Multisource adaption for driver attention prediction in arbitrary driving scenes

S Gan, X Pei, Y Ge, Q Wang, S Shang… - IEEE transactions on …, 2022 - ieeexplore.ieee.org
Driver attention cues contribute to the following intended maneuver prediction and provide a
risk indicator for the advanced driver-assistance systems in complex driving scenarios. The …