Results 1 to 10 of about 206,344 (279)

GNN-RMNet: Leveraging graph neural networks and GPS analytics for driver behavior and route optimization in logistics. [PDF]

PLoS ONE
Logistics networks are becoming increasingly complex and rely more heavily on real-time vehicle data, necessitating intelligent systems to monitor driver behavior and identify route anomalies.
Eman Ali Aldhahri, Abdulwahab Ali Almazroi, Monagi Hassan Alkinani, Mohammed Alqarni, Elham Abdullah Alghamdi, Nasir Ayub   +5 more
doaj   +2 more sources

Research on engine power-loss fault diagnosis method based on time-series data mining [PDF]

Scientific Reports
Traditional diagnostic approaches for engine power-loss faults in commercial vehicles are limited by their heavy reliance on on-site road testing and high consumption of human and material resources.
Li Feng, Le Liu, Hongsheng Xu, Sumeng Gao, Hai Tang, Jianing Cui, Bin Zhang, Yufeng Rao   +7 more
doaj   +2 more sources

Calibration of the Intelligent Driver Model (IDM) at the Microscopic Level

Future Transportation
This paper presents a calibration technique for the Intelligent Driver Model (IDM), a car-following model that considers the physical interpretation of each parameter. Using an instrumented vehicle, trajectory data were gathered for a group of Portuguese
Luís Vasconcelos, Jorge M. Bandeira
doaj   +2 more sources

Cockpit-Llama: Driver Intent Prediction in Intelligent Cockpit via Large Language Model

Sensors
The cockpit is evolving from passive, reactive interaction toward proactive, cognitive interaction, making precise predictions of driver intent a key factor in enhancing proactive interaction experiences.
Yi Chen, Chengzhe Li, Qirui Yuan, Jinyu Li, Yuze Fan, Xiaojun Ge, Yun Li, Fei Gao, Rui Zhao   +8 more
doaj   +3 more sources

NeuroSafeDrive: An Intelligent System Using fNIRS for Driver Distraction Recognition [PDF]

Sensors
Driver distraction remains a critical factor in road accidents, necessitating intelligent systems for real-time detection. This study introduces a novel fNIRS-based method to to classify varying levels of driver distraction across diverse simulated ...
Ghazal Bargshady, Hakki Gokalp Ustun, Yasaman Baradaran, Houshyar Asadi, Ravinesh C Deo, Jeroen Van Boxtel, Raul Fernandez Rojas   +6 more
doaj   +2 more sources

A Microscopic Traffic Model Considering Driver Reaction and Sensitivity

Applied Sciences, 2023
A new microscopic traffic model is proposed that characterizes driver response according to reaction and sensitivity. Driver response in the intelligent driver (ID) model is based on a fixed acceleration exponent and so does not follow traffic physics ...
Faryal Ali, Zawar Hussain Khan, Thomas Aaron Gulliver, Khurram Shehzad Khattak, Ahmed B. Altamimi   +4 more
doaj   +1 more source

AutoCoach: An Intelligent Driver Behavior Feedback Agent with Personality-Based Driver Models [PDF]

Electronics, 2021
Nowadays, AI has many applications in everyday human activities such as exercise, eating, sleeping, and automobile driving. Tech companies can apply AI to identify individual behaviors (e.g., walking, eating, driving), analyze them, and offer personalized feedback to help individuals make improvements accordingly.
Zahraa Marafie, Kwei-Jay Lin, Daben Wang, Haoyu Lyu, Yanan Liu, Yu Meng, Jiaao Ma   +6 more
openaire   +1 more source

A New Driver Model Based on Driver Response

Applied Sciences, 2022
In this paper, a new microscopic traffic model based on forward and rearward driver response is proposed. Driver response is characterized using the distance and time headways.
Faryal Ali, Zawar Hussain Khan, Fayaz Ahmad Khan, Khurram Shehzad Khattak, Thomas Aaron Gulliver   +4 more
doaj   +1 more source

Adaptive driver following model that integrates perception process and driving behavior

Scientific Reports, 2022
In order to meet the personalized needs of Chinese intelligent vehicles and improve the satisfaction and acceptance of human–computer interaction and collaboration in domestic intelligent vehicles.
Changhao Piao, Kexin Li, Qifan Yu, Junren Shi   +3 more
doaj   +1 more source

Extreme Gradient Boosting (XGBoost) Model for Vehicle Trajectory Prediction in Connected and Autonomous Vehicle Environment

Promet (Zagreb), 2021
Connected and autonomous vehicles (CAVs) have the ability to receive information on their leading vehicles through multiple sensors and vehicle-to-vehicle (V2V) technology and then predict their future behaviour thus to improve roadway safety and ...
Pengfei Liu, Wei Fan
doaj   +1 more source