The application of machine learning techniques for driving behavior analysis: A conceptual framework and a systematic literature review

ZE Abou Elassad, H Mousannif… - … Applications of Artificial …, 2020 - Elsevier
Driving Behavior (DB) is a complex concept describing how the driver operates the vehicle
in the context of the driving scene and surrounding environment. Recently, DB assessment …

Driver behavior classification: a systematic literature review

S Bouhsissin, N Sael, F Benabbou - IEEE Access, 2023 - ieeexplore.ieee.org
Driver behavior is receiving increasing attention because of the staggering number of road
accidents. Many road safety reports regard human behavior as the most important factor in …

Do you want your autonomous car to drive like you?

C Basu, Q Yang, D Hungerman, M Singhal… - Proceedings of the …, 2017 - dl.acm.org
With progress in enabling autonomous cars to drive safely on the road, it is time to start
asking how they should be driving. A common answer is that they should be adopting their …

Exploring personalised autonomous vehicles to influence user trust

X Sun, J Li, P Tang, S Zhou, X Peng, HN Li… - Cognitive Computation, 2020 - Springer
Trust is a major determinant of acceptance of an autonomous vehicle (AV), and a lack of
appropriate trust could prevent drivers and society in general from taking advantage of such …

Predicting time-varying, speed-varying dilemma zones using machine learning and continuous vehicle tracking

M Rahman, MW Kang, P Biswas - Transportation research part C: emerging …, 2021 - Elsevier
This paper proposes an innovative framework of predicting driver behavior under varying
dilemma zone conditions using artificial intelligence-based machine learning. The …

Comparison of proposed countermeasures for dilemma zone at signalized intersections based on cellular automata simulations

Y Wu, M Abdel-Aty, Y Ding, B Jia, Q Shi… - Accident Analysis & …, 2018 - Elsevier
The Type II dilemma zone describes the road segment to a signalized intersection where
drivers have difficulties to decide either stop or go at the onset of yellow signal. Such …

Vehicular data space: The data point of view

PH Rettore, G Maia, LA Villas… - … Surveys & Tutorials, 2019 - ieeexplore.ieee.org
Over the years, governments and automakers launched initiatives to improve road traffic
efficiency, safety, and people mobility. They have been working on various aspects of …

SIV-DSS: Smart in-vehicle decision support system for driving at signalized intersections with V2I communication

XF **e, ZJ Wang - Transportation Research Part C: Emerging …, 2018 - Elsevier
In this paper, we present a Smart In-Vehicle Decision Support System (SIV-DSS) to help
making better stop/go decisions in the indecision zone as a vehicle is approaching a …

An integrated intelligent intersection control system for preventing rear-end and angled crashes: system design and deployment results

YH Chen, GL Chang, SY Park, M Kim - Accident Analysis & Prevention, 2023 - Elsevier
In view of the dynamic all-red extension (DARE) system's effectiveness in preventing angled
crashes (Park et al., 2018), this study has further enhanced its function to contend with rear …

SafeSmartDrive: Real-Time Traffic Environment Detection and Driver Behavior Monitoring With Machine and Deep Learning

S Bouhsissin, N Sael, F Benabbou, A Soultana… - IEEE …, 2024 - ieeexplore.ieee.org
The advancement of intelligent transportation systems is crucial for improving road safety
and optimizing traffic flow. In this paper, we present SafeSmartDrive, an integrated …