A review on recent driver safety systems and its emerging solutions

Abstract

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

Keywords:

• Driver safety
• IoT-based systems
• road safety
• drowsiness detection
• intoxicated drivers

Acknowledgments

We would like to express our sincere gratitude to all the authors for their valuable contributions to this research paper. Their collective efforts and expertize significantly enriched the content and quality of this work.

Disclosure statement

No potential conflict of interest was reported by the author(s).