A comprehensive review of driver behavior analysis utilizing smartphones
Human factors are the primary catalyst for traffic accidents. Among different factors, fatigue,
distraction, drunkenness, and/or recklessness are the most common types of abnormal …
distraction, drunkenness, and/or recklessness are the most common types of abnormal …
[HTML][HTML] Smartphone sensing for understanding driving behavior: Current practice and challenges
Understanding driving behavior–even in the rapid emergence of automation-remains in the
spotlight, for decomposing complex driving dynamics, enabling the development of user …
spotlight, for decomposing complex driving dynamics, enabling the development of user …
Machine learning techniques to identify unsafe driving behavior by means of in-vehicle sensor data
E Lattanzi, V Freschi - Expert Systems with Applications, 2021 - Elsevier
Traffic crashes are one of the biggest causes of accidental death in the way where, every
year, more than 1.35 million of people die. In most of them, the main cause is related to the …
year, more than 1.35 million of people die. In most of them, the main cause is related to the …
Driver's black box: A system for driver risk assessment using machine learning and fuzzy logic
Risky driving behaviors can cause accidents, which may result in major material and moral
damages. Due to the increase in road accidents, it has become an important issue to identify …
damages. Due to the increase in road accidents, it has become an important issue to identify …
Deep reinforcement learning for personalized driving recommendations to mitigate aggressiveness and riskiness: Modeling and impact assessment
EG Mantouka, EI Vlahogianni - Transportation research part C: emerging …, 2022 - Elsevier
Most driving recommendation and assistance systems, such as Advanced Driving
Assistance Systems (ADAS), are usually designed based on the behavior of an average …
Assistance Systems (ADAS), are usually designed based on the behavior of an average …
Multimodal corpus design for audio-visual speech recognition in vehicle cabin
This paper introduces a new methodology aimed at comfort for the driver in-the-wild
multimodal corpus creation for audio-visual speech recognition in driver monitoring systems …
multimodal corpus creation for audio-visual speech recognition in driver monitoring systems …
Maneuver-based driving behavior classification based on random forest
J Xie, M Zhu - IEEE Sensors Letters, 2019 - ieeexplore.ieee.org
Driving behavior classification is highly correlated with vehicle accidents and injury.
Automatically recognizing different driving behaviors is important for improving road safety …
Automatically recognizing different driving behaviors is important for improving road safety …
Symbolic aggregate approximation based data fusion model for dangerous driving behavior detection
Detecting dangerous driving behavior is of great significance for reducing the occurrence of
traffic accidents, and very challenging as it is affected by multiple factors. However, the …
traffic accidents, and very challenging as it is affected by multiple factors. However, the …
Macroscopic big data analysis and prediction of driving behavior with an adaptive fuzzy recurrent neural network on the internet of vehicles
DC Li, MYC Lin, LD Chou - IEEE Access, 2022 - ieeexplore.ieee.org
Dangerous driving behaviors are diverse and complex. Determining how to analyze the
driving behavior of public drivers objectively and accurately has always been a research …
driving behavior of public drivers objectively and accurately has always been a research …
Combining machine learning and dynamic time wrapping for vehicle driving event detection using smartphones
The detection of driving events could be useful for reducing accidents, fleet management
and insurance premiums etc. Currently, top of the range vehicles and large fleets employ …
and insurance premiums etc. Currently, top of the range vehicles and large fleets employ …