A review on recent driver safety systems and its emerging solutions

Road safety and accident prevention are critical concerns in modern transportation. This paper presents a comprehensive survey of driver safety systems, focusing on the latest advancements in this field. We analyze the existing literature to identify key research trends in driver safety systems, enc...

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Vydáno v:International journal of computers & applications Ročník 46; číslo 3; s. 137 - 151
Hlavní autoři: Nair, Akhil, Patil, Varad, Nair, Rohan, Shetty, Adithi, Cherian, Mimi
Médium: Journal Article
Jazyk:angličtina
Vydáno: Calgary Taylor & Francis 03.03.2024
Taylor & Francis Ltd
Témata:
Accident detection
Accident prevention
Artificial intelligence
Artificial neural networks
Automation
Back propagation networks
Cellular communication
Controller area network
Driver safety
Driving
drowsiness detection
Electrocardiography
Electroencephalography
Fast Fourier transformations
Fourier transforms
Global positioning systems
GPS
Graphics processing units
Head-up displays
Internet of Things
intoxicated drivers
IoT-based systems
Light emitting diodes
Machine learning
Neural networks
Principal components analysis
Process controls
road safety
Singular value decomposition
Support vector machines
Traffic accidents & safety
Vehicle safety
ISSN:1206-212X, 1925-7074
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Shrnutí:Road safety and accident prevention are critical concerns in modern transportation. This paper presents a comprehensive survey of driver safety systems, focusing on the latest advancements in this field. We analyze the existing literature to identify key research trends in driver safety systems, encompassing various categories of solutions. Our survey delves into the reasons behind road accidents and assesses the effectiveness of emerging technologies and solutions in accident prevention. By categorizing and evaluating these solutions based on the Internet of Things and Machine Learning, we provide valuable insights into the landscape of road accident detection and prevention systems. This survey not only highlights the current state of the art but also serves as a reference for future research and innovation in the domain of driver safety. Abbreviations IoT: Internet of things; CNN: Convolutional Neural Network; SVM: Support vector machine; HRV: Heart rate variability; RRI: R-R Interval; MSPC: Multivariate Statistical process control; EAR: Eye aspect ratio; HUD: Head-up display; GPS: Global positioning system; CAN: Controller area network; GPU: Graphics processing unit; IR: Infrared; GSM: Global system for mobile communication; EEG: Electroencephalogram; PCA: Principal component analysis; SVC: Support vector classifier; SdsAEs: Stacked denoising sparse autoencoders; ECG: Electrocardiogram; LED: Light emitting diode; NFC: Near field communication; PSO: Personal security officer; PPG: Photoplethysmography; EDA: Electrodermal activity; EMG: Electromyography; LCD: Liquid crystal display; RF SoCs: Radiofrequency system on chip; PLR: Piecewise linear representation; BAC: Blood alcohol content; BPNN: Backpropagation Neural Network; ADSD: Automated driver sleepiness detection; EOG: Electroocoulogram; KNN: K nearest neighbor; CBR: Case-based reasoning; RF: Random forest; NIR: Near-infrared; LBP: Local binary pattern; PERCLOS: Percentage of Eye Closure; SVD: Singular value decomposition; FFT: Fast Fourier transform; LSTM: Long short-term memory; DDD: Drunk driver detection; BLE: Bluetooth low energy; SWM: Steering wheel movements; M-SVM: Mobile-based Support Vector Machine; AI: Artificial intelligence; ML: Machine learning; DL: Deep learning; PCA: Principal component analysis; IPCA: Incremental principal component analysis; ANN: Artificial neural network; CAV: Connected and automated vehicles
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ISSN:1206-212X
1925-7074
DOI:10.1080/1206212X.2023.2293348