A Driver's Physiology Sensor-Based Driving Risk Prediction Method for Lane-Changing Process Using Hidden Markov Model

Recent Advances in Vehicle Driver Health Monitoring Systems

The need for creative solutions in the real-time monitoring of health is rapidly increasing, especially in light of health incidents in relation to drivers of motor vehicles. A sensor-based health monitoring system provides an integrated mechanism for diagnosing and managing in real time, enabling the detection, prediction, and recommendation of treatment and the prevention of disease onset. The real-time monitoring of driver's health represents a significant advancement in the assurance of driver safety and well-being. From fitness trackers to advanced biosensors, these devices have not only made healthcare more accessible but have also transformed how people interact with their health data. The purpose of this scoping review is to systematically collect and evaluate information from publications on driver health monitoring systems to provide a comprehensive overview of the current state of research on wearable or remote sensor technologies for driver health monitoring. It aims to identify knowledge gaps that need to be addressed and suggest future research directions that will help to fill these gaps. This approach involves the topic of vehicle safety and healthcare and will contribute to the advancement of this field. By focusing on the real-time monitoring of health parameters in an automotive context, this review highlights the potential of different types of technologies to bridge the gap between health monitoring and driver safety.

An analysis of physiological responses as indicators of driver takeover readiness in conditionally automated driving

By the year 2045, it is projected that Autonomous Vehicles (AVs) will make up half of the new vehicle market. Successful adoption of AVs can reduce drivers' stress and fatigue, curb traffic congestion, and improve safety, mobility, and economic efficiency. Due to the limited intelligence in relevant technologies, human-in-the-loop modalities are still necessary to ensure the safety of AVs at current or near future stages, because the vehicles may not be able to handle all emergencies. Therefore, it is important to know the takeover readiness of the drivers to ensure the takeover quality and avoid any potential accidents. To achieve this, a comprehensive understanding of the drivers' physiological states is crucial. However, there is a lack of systematic analysis of the correlation between different human physiological responses and takeover behaviors which could serve as important references for future studies to determine the types of data to use. This paper provides a comprehensive analysis of the effects of takeover behaviors on the common physiological indicators. A program for conditional automation was developed based on a game engine and applied to a driving simulator. The experiment incorporated three types of secondary tasks, three takeover events, and two traffic densities. Brain signals, Skin Conductance Level (SCL), and Heart Rate (HR) of the participants were collected while they were performing the driving simulations. The Frontal Asymmetry Index (FAI) (as an indicator of engagement) and Mental Workload (MWL) were calculated from the brain signals to indicate the mental states of the participants. The results revealed that the FAI of the drivers would slightly decrease after the takeover alerts were issued when they were doing secondary tasks prior to the takeover activities, and the higher difficulty of the secondary tasks could lead to lower overall FAI during the takeover periods. In contrast, The MWL and SCL increased during the takeover periods. The HR also increased rapidly at the beginning of the takeover period but dropped back to a normal level quickly. It was found that a fake takeover alert would lead to lower overall HR, slower increase, and lower peak of SCL during the takeover periods. Moreover, the higher traffic density scenarios were associated with higher MWL, and a more difficult secondary task would lead to higher MWL and HR during the takeover activities. A preliminary discussion of the correlation between the physiological data, takeover scenario, and vehicle data (that relevant to takeover readiness) was then conducted, revealing that although takeover event, SCL, and HR had slightly higher correlations with the maximum acceleration and reaction time, none of them dominated the takeover readiness. In addition, the analysis of the data across different participants was conducted, which emphasized the importance of considering standardization or normalization of the data when they were further used as input features for estimating takeover readiness. Overall, the results presented in this paper offer profound insights into the patterns of physiological data changes during takeover periods. These findings can be used as benchmarks for utilizing these variables as indicators of takeover preparedness and performance in future research endeavors.

Assessment of particulate matter inhalation during the trip process with the considerations of exercise load

A Particulate Matter (PM) inhalation model considering exercise load is established to evaluate the impact of PM on residents' travel health. The study chooses PM detectors to collect PM concentrations at the various transportation space, including walking, bicycle, bus, taxi, and subway. A multiple linear regression model revised by road greening is utilized to study the influence factors that have a potential impact on the PM concentration. The air inhalation model with the consideration of exercise load can be acquired by connecting the heart rate (HR) and individual characteristics. The PM_2.5 and PM₁₀ inhalation for a complete trip of traveler can be estimated using the proposed model based on air inhalation per time unit, travel time, and PM concentration. The analysis results using the experimental data in Xi'an indicate that PM concentrations in taxi carriage, bus carriage, and subway carriage are significantly different from those obtained from environmental monitoring stations. However, the difference is not significant in the locations of sidewalk, non-motorized lane, taxi station, bus station, subway concourse, and subway platform. PM concentration and humidity in background environment have a positive influence on the increase of PM concentration in transportation environment, while temperature and wind speed are negative. The mean values of air inhalation per time unit for male and female using each mode are in the range of 9.6-26.8 L/min and 9.8-27.8 L/min, respectively. Exposure time in non-motorized transportation has a large effect on PM inhalation of travelers, walking connections and waiting in motorized transportation are the main contributing states to PM inhalation of travelers. The results of the study can be used to predict travelers' PM inhalation in completed trips, and provide recommendations for travelers to choose a healthier mode.

A comparative driving safety study of mountainous expressway individual tunnel and tunnel group based on eye gaze behavior

The traffic environment of a tunnel group is more complex than that of a single tunnel, which increases the driving risk. The eye gaze behavior of drivers can be used to evaluate driving safety and comfort. To analyze the fixation characteristics of drivers in a single tunnel and tunnel group of mountainous expressways, an actual vehicle test is conducted. The test area has a total length of 160 km and 38 tunnels, including 8 tunnel groups and 16 single tunnels. In the test, the difference in the gaze time of five drivers between single tunnels and tunnel groups is compared. The k-means method is used to cluster driver’s gaze points dynamically. Based on the Markov theory, the attributes related to gaze transfer are obtained. The results show that when tunnels are of short or medium length, there is no significant difference in the gaze time and gaze point transfer between the tunnel group and a single tunnel. In contrast, when tunnels have long or extra-long length, the repeated fixation probability and the two-step transition probability of looking back of a driver in a tunnel group are higher than those in a single tunnel. The design and management method of a single tunnel cannot be directly used, especially for extra-long tunnels located at the back of a tunnel group with a long upstream tunnel length and a short interval distance from the upstream tunnel. Therefore, it is necessary to focus on the design and management methods of tunnel groups.

A hybrid neural network for driving behavior risk prediction based on distracted driving behavior data

Distracted driving behavior is one of the main factors of road accidents. Accurately predicting the risk of driving behavior is of great significance to the active safety of road transportation. The large amount of information collected by the sensors installed on the vehicle can be identified by the algorithm to obtain the distracted driving behavior data, which can be used to predict the driving behavior risk of the vehicle and the area. In this paper, a new neural network named Driving Behavior Risk Prediction Neural Network (DBRPNN) is developed for prediction based on the distracted driving behavior data. The network consists of three modules: the Feature Processing Module, the Memory Module, and the Prediction Module. In this process, attribute data (time in a day, daily driving time, and daily driving mileage) that can reflect external factors and driver statuses, are added to the network to increase the accuracy of the model. We predicted the driving behavior risk of different objects (Vehicle and Area). For the applicability improvement of the model, we further classify the distracted driving behavior categories, and DBRPNN can provide more accurate risk prediction. The results show that compared with traditional models (Classification and Regression Tree, Support Vector Machines, Recurrent Neural Network, and Long Short-Term Memory), DBRPNN has better prediction performance. The method proposed in this paper has been fully verified and may be transplanted into active safety early warning system for more accurate and flexible application.

A Novel Intelligent Approach to Lane-Change Behavior Prediction for Intelligent and Connected Vehicles

The prediction of lane-change behavior is a challenging issue in intelligent and connected vehicles (ICVs), which can help vehicles predict in advance and change lanes safely. In this paper, a novel intelligent approach, which considering both the driving style-based lane-change environment and the driving trajectory-related parameters of the ICV and surrounding vehicles, is proposed to predict the lane-change behaviors for ICVs. By analyzing the characteristics of the lane-change behavior of the vehicle, a modified dataset for the prediction of lane-change behavior was established based on the Next-Generation Simulation (NGSIM) dataset, which is made up of real vehicle trajectories collected by camera. In the proposed approach, the hidden Markov model (HMM)-based model is designed to judge whether the current environment is suitable for lane change according to the driving environment parameters around the vehicle; then according to the driving state of the vehicle, a learning-based prediction-then-judgment model is proposed and designed to realize the prediction of the ICV's lane-change behavior. Experiments are implemented by using the modified dataset. From the experimental results, the lane-change probability value on the target lane in the truth of the lane-change behavior calculated by the designed HMM-based model is basically above 0.5, indicating that the model can make a more accurate judgment on the surrounding lane-change environment. The proposed learning-based prediction-then-judgment model has an accuracy of 99.32% for the prediction of lane-change behavior, and the accuracy of the lane-change detection algorithm in the model is 99.56%. The experimental results show that the proposed approach has a good performance in the prediction of lane-change behavior, which could effectively assist ICVs to change lanes safely.

Three Layered Architecture for Driver Behavior Analysis and Personalized Assistance with Alert Message Dissemination in 5G Envisioned Fog-IoCV

The Internet of connected vehicles (IoCV) has made people more comfortable and safer while driving vehicles. This technology has made it possible to reduce road casualties; however, increased traffic and uncertainties in environments seem to be limitations to improving the safety of environments. In this paper, driver behavior is analyzed to provide personalized assistance and to alert surrounding vehicles in case of emergencies. The processes involved in this research are as follows. (i) Initially, the vehicles in an environment are clustered to reduce the complexity in analyzing a large number of vehicles. Multi-criterion-based hierarchical correlation clustering (MCB-HCC) is performed to dynamically cluster vehicles. Vehicular motion is detected by edge-assisted road side units (E-RSUs) by using an attention-based residual neural network (AttResNet). (ii) Driver behavior is analyzed based on the physiological parameters of drivers, vehicle on-board parameters, and environmental parameters, and driver behavior is classified into different classes by implementing a refined asynchronous advantage actor critic (RA3C) algorithm for assistance generation. (iii) If the driver’s current state is found to be an emergency state, an alert message is disseminated to the surrounding vehicles in that area and to the neighboring areas based on traffic flow by using jelly fish search optimization (JSO). If a neighboring area does not have a fog node, a virtual fog node is deployed by executing a constraint-based quantum entropy function to disseminate alert messages at ultra-low latency. (iv) Personalized assistance is provided to the driver based on behavior analysis to assist the driver by using a multi-attribute utility model, thereby preventing road accidents. The proposed driver behavior analysis and personalized assistance model are experimented on with the Network Simulator 3.26 tool, and performance was evaluated in terms of prediction error, number of alerts, number of risk maneuvers, accuracy, latency, energy consumption, false alarm rate, safety score, and alert-message dissemination efficiency.

Health risk assessment of PM2.5 on walking trips

PM_2.5 has an impact on residents' physical health during travelling, especially walking completely exposed to the environment. In order to obtain the specific impact of PM_2.5 on walking, 368 healthy volunteers were selected and they were grouped according to gender and age. In the experiment, the heart rate change rate (HR%) is taken as test variable. According to receiver operating characteristic (ROC) curve, the travel is divided into two states: safety and risk. Based on this, a binary logit model considering Body Mass Index (BMI) is established to determine the contribution of PM_2.5 concentration and body characteristics to travel risk. The experiment was conducted on Chang'an Middle Road in Xi'an City. The analysis results show that the threshold of HR% for safety and risk ranges from 31.1 to 40.1%, and that of PM_2.5 concentration ranges from 81 to 168 μg/m³. The probability of risk rises 5.8% and 11.4%, respectively, for every unit increase in PM_2.5 concentration and HR%. Under same conditions, the probability of risk for male is 76.8% of that for female. The probability of risk for youth is 67.5% of that for middle-aged people, and the probability of risk for people with BMI in healthy range is 72.1% of that for non-healthy range. The research evaluates risk characteristics of walking in particular polluted weather, which can improve residents' health level and provide suggestions for travel decision while walking.

Eye-Tracking Evaluation of Exit Advance Guide Signs in Highway Tunnels in Familiar and Unfamiliar Drivers

As a component of the traffic control plan, traffic signs on highways offer drivers necessary information. Unfortunately, many signs are unfamiliar to or misunderstood by drivers, especially when lacking a setting method; this includes exit advance guide signs in tunnels. These are generally set in roadbed sections, but space limitations in tunnels dictate that they must be set differently. To evaluate the effect of the setting method, an experiment was designed and conducted, during which the eye movements of 44 drivers with different familiarity levels were tracked. Twenty-two of the drivers had not previously participated in any experiment involving exit advance guide signs in highway tunnels, while 22 of them had. Time period data were analyzed, including data from before the sign appeared, when it appeared, and when it disappeared. Based on area division and Markov theory, attributes related to gaze transition were obtained, including one- and two-step gaze transition probabilities and area gaze probabilities. The results showed that gaze transition was confirmed to be significantly different between the three periods and between the drivers. Features extracted from eye movement characteristics, gaze transition paths, and gaze areas demonstrated that visual attention is more dispersed in familiar drivers during the lane-change intention period. Therefore, signs should be placed on the left wall of the highway tunnel.

Application of Wireless Accelerometer Mounted on Wheel Rim for Parked Car Monitoring

Damages of different kinds that can be inflicted to a parked car. Among them, loosening of the car wheel bolts is difficult to detect during normal use of the car and is at the same time very dangerous to the health and life of the driver. Moreover, in patents and publications, only little information is presented about electronic sensors available for activation from inside of the car to inform the driver about the mentioned dangerous situation. Thus, the main aim of this work is the proposition and examination of a sensing device using of a wireless accelerometer head to detect loosening of wheel fixing bolts before ride has been started. The proposed sensing device consists of a wireless accelerometer head, an assembly interface and a receiver unit. The assembly interface between the head and the inner part of the rim enables the correct operation of the system. The data processing algorithm developed for the receiver unit enables the proper detection of the unscrewing of bolts. Moreover, the tested algorithm is resistant to the interference signals generated in the accelerometer head by cars and men passing in close distance.

Assessing Driving Risk Using Internet of Vehicles Data: An Analysis Based on Generalized Linear Models

With the major advances made in internet of vehicles (IoV) technology in recent years, usage-based insurance (UBI) products have emerged to meet market needs. Such products, however, critically depend on driving risk identification and driver classification. Here, ordinary least square and binary logistic regressions are used to calculate a driving risk score on short-term IoV data without accidents and claims. Specifically, the regression results reveal a positive relationship between driving speed, braking times, revolutions per minute and the position of the accelerator pedal. Different classes of risk drivers can thus be identified. This study stresses both the importance and feasibility of using sensor data for driving risk analysis and discusses the implications for traffic safety and motor insurance.

Research on the Influence of Vehicle Speed on Safety Warning Algorithm: A Lane Change Warning System Case Study

Speed has an important impact on driving safety, however, this factor is not included in existing safety warning algorithms. This study uses lane change systems to study the influence of vehicle speed on safety warning algorithms, aiming to determine lane change warning rules for different speeds (DS-LCW). Thirty-five drivers are recruited to carry out an extreme trial and naturalistic driving experiment. The vehicle speed, relative speed, relative distance, and minimum safety deceleration (MSD) related to lane change characteristics are then analyzed and calculated as warning rule characterization parameters. Lane change warning rules for a rear vehicle in the target lane under four-speed levels of 60 ≤ v < 70 km/h, 70 ≤ v < 80 km/h, 80 ≤ v < 90 km/h, and v ≥ 90 km/h are established. The accuracy of lane change warning rules not considering speed level (NDS-LCW) and ISO 17387 are found to be 87.5% and 79.8%, respectively. Comparatively, the accuracy rate of DS-LCW under four-speed levels is 94.6%, 93.8%, 90.0%, and 92.6%, respectively, which is significantly superior. The algorithm proposed in this paper provides warning in the lane change process with a smaller relative distance, and the accuracy rate of DS-LCW is significantly superior to NDS-LCW and ISO 17387.

Wireless Sensors System for Stress Detection by Means of ECG and EDA Acquisition

This paper describes the design of a two channels electrodermal activity (EDA) sensor and two channels electrocardiogram (ECG) sensor. The EDA sensors acquire data on the hands and transmit them to the ECG sensor with wireless WiFi communication for increased wearability. The sensors system acquires two EDA channels to improve the removal of motion artifacts that take place if EDA is measured on individuals who need to move their hands in their activities. The ECG channels are acquired on the chest and the ECG sensor is responsible for aligning the two ECG traces with the received packets from EDA sensors; the ECG sensor sends via WiFi the aligned packets to a laptop for real time plot and data storage. The metrological characterization showed high-level performances in terms of linearity and jitter; the delays introduced by the wireless transmission from EDA to ECG sensor have been proved to be negligible for the present application.

Estimating Driving Fatigue at a Plateau Area with Frequent and Rapid Altitude Change

Due to the influence of altitude change on a driver’s heart rate, it is difficult to estimate driving fatigue using heart rate variability (HRV) at a road segment with frequent and rapid altitude change. Accordingly, a novel method of driving fatigue estimation for driving at plateau area with frequent altitude changes is proposed to provide active safety monitoring in real time. A naturalistic driving experiment at Qinghai-Tibet highway was conducted to collect drivers’ electrocardiogram data and eye movement data. The results of the eye movement-based method were selected to enhance the HRV-based driving fatigue degree estimation method. A correction factor was proposed to correct the HRV-based method at the plateau area so that the estimation can be made via common portable devices. The correction factors for both upslope and downslope segments were estimated using the field experiment data. The results on the estimation of revised driving fatigue degree can describe the driver’s fatigue status accurately for all the road segments at the plateau area with altitudes from 3540 to 4767 m. The results can provide theoretical references for the design of the devices of active safety prevention.