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, 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

道路安全与事故预防是现代交通运输领域的核心关切议题。本文针对驾驶员安全系统开展了全面综述，重点聚焦该领域的最新研究进展。我们通过梳理现有文献，明确了驾驶员安全系统领域的关键研究趋势，涵盖各类解决方案范畴。本综述深入剖析了道路交通事故的成因，并评估了新兴技术与解决方案在事故预防中的应用效果。通过基于物联网（Internet of Things, IoT）与机器学习（Machine Learning, ML）对各类解决方案进行分类与评估，我们为道路事故检测与预防系统的整体格局提供了极具价值的研究视角。本综述不仅梳理了当前该领域的前沿技术现状，同时也可为驾驶员安全领域的后续研究与创新提供参考依据。

缩写说明：
IoT（物联网，Internet of Things）、CNN（卷积神经网络，Convolutional Neural Network）、SVM（支持向量机，Support Vector Machine）、HRV（心率变异性，Heart rate variability）、RRI（R-R间期，R-R Interval）、MSPC（多变量统计过程控制，Multivariate Statistical Process Control）、EAR（眼长宽比，Eye aspect ratio）、HUD（平视显示器，Head-up Display）、GPS（全球定位系统，Global Positioning System）、CAN（控制器局域网，Controller Area Network）、GPU（图形处理器，Graphics Processing Unit）、IR（红外，Infrared）、GSM（全球移动通信系统，Global System for Mobile Communication）、EEG（脑电图，Electroencephalogram）、PCA（主成分分析，Principal Component Analysis）、SVC（支持向量分类器，Support Vector Classifier）、SdsAEs（堆叠去噪稀疏自编码器，Stacked Denoising Sparse Autoencoders）、ECG（心电图，Electrocardiogram）、LED（发光二极管，Light Emitting Diode）、NFC（近场通信，Near Field Communication）、PSO（个人安全官员，Personal Security Officer）、PPG（光电容积描记法，Photoplethysmography）、EDA（皮肤电活动，Electrodermal Activity）、EMG（肌电图，Electromyography）、LCD（液晶显示器，Liquid Crystal Display）、RF SoCs（射频片上系统，Radiofrequency System on Chip）、PLR（分段线性表示，Piecewise Linear Representation）、BAC（血液酒精含量，Blood Alcohol Content）、BPNN（反向传播神经网络，Backpropagation Neural Network）、ADSD（自动化驾驶员嗜睡检测，Automated Driver Sleepiness Detection）、EOG（眼电图，Electrooculogram，原文存在笔误为Electrocoulogram）、KNN（K近邻算法，K Nearest Neighbor）、CBR（基于案例推理，Case-based Reasoning）、RF（随机森林，Random Forest）、NIR（近红外，Near-infrared）、LBP（局部二值模式，Local Binary Pattern）、PERCLOS（闭眼百分比，Percentage of Eye Closure）、SVD（奇异值分解，Singular Value Decomposition）、FFT（快速傅里叶变换，Fast Fourier Transform）、LSTM（长短期记忆网络，Long Short-term Memory）、DDD（酒驾检测，Drunk Driver Detection）、BLE（低功耗蓝牙，Bluetooth Low Energy）、SWM（方向盘运动，Steering Wheel Movements）、M-SVM（基于移动平台的支持向量机，Mobile-based Support Vector Machine）、AI（人工智能，Artificial Intelligence）、ML（机器学习，Machine Learning）、DL（深度学习，Deep Learning）、IPCA（增量主成分分析，Incremental Principal Component Analysis）、ANN（人工神经网络，Artificial Neural Network）、CAV（联网自动驾驶车辆，Connected and Automated Vehicles）