Driver Error Research Articles

Wreck on the Highway: Intersectionality of Driver Culpability, Tetrahydrocannabinols, Other Intoxicants and Fatalities in Washington State

Given the legalization of recreational cannabis in 2012 in Washington State and recent mixed results regarding the effects of cannabis on driver safety, the paper examines the link between delta-9-tetrahydrocannabinols (THC) and driver behavior, including speeding and driver errors which may have contributed to a particular fatal crash. The current study utilized data from the Washington State Fatality Analysis Reporting System Analytical File (WA FARS) from 2008 to 2016. A series of logistic regressions were employed to compare THC-positive and -negative drivers, as well as drivers who tested positive for other intoxicants. The results of the study were mixed; it was found that delta-9-THC positively predicted speeding, but not other driver errors. Interestingly, carboxy-THC, a non-psychoactive chemical which can be detected for a longer period of time, was a significant predictor of both speeding and driver errors. This research further demonstrates that cannabis is predictive of risky behavior by drivers in fatal crashes, though it is not nearly as strong a predictor as alcohol. Additional research is needed to understand better why carboxy-THC is a stronger and more robust predictor of poor driving behavior than delta-9-THC.

Potential Effects of Automated Vehicles on Road Transportation: A Literature Review

Abstract Automated vehicles (AVs) are one of the emerging technologies that can perform the driving task themselves. The market penetration of AVs is expected to get growth in the close future. Therefore, it is crucial to have an overall clue on how they play the role in the road transportation sector. Automation might be assumed to have a beneficial impact on many aspects related to road transportation. The current paper attempts to investigate this rough assumption by reviewing the literature on the potential effects of automated vehicles on road transportation. A comprehensive look at the overall potential effects of automated vehicles will show the entire picture, and not just a cropped portion of that, to the researchers, decision makers, and practitioners and helps them to identify the negative and positive effects as well as challenges and uncertainties towards this new technology. In this paper, literature findings on the potential effects of automated vehicles on traffic flow, pedestrians mobility, travel demand and travel pattern, safety and security, and energy consumption and emissions are reviewed and discussed. According to the literature, it is concluded that AVs, as their market penetration increases, promisingly improve the capacity of a road network, eliminates human driver errors, and provide better mobility for groups of people who are currently facing travel-restriction conditions. However, the long-term effects of AVs especially on energy consumption, emission, pedestrian interaction, safety and security has uncertainty due to the complexity of predicting the future mobility pattern.

Benefits and Costs of Ride-Sharing in Shared Automated Vehicles across Austin, Texas: Opportunities for Congestion Pricing

A self-driving, fully automated, or “autonomous” vehicle (AV) revolution is imminent, with the potential to eliminate driver costs and driver error, while ushering in an era of shared mobility. Dynamic ride-sharing (DRS), which refers to sharing rides with strangers en route, is growing, with top transportation network companies providing such services. This work uses an agent-based simulation tool called MATSim to simulate travel patterns in Austin, Texas in the presence of personal AVs, and shared AVs (SAVs), with DRS and advanced road-pricing policies in place. Fleet size, pricing, and fare level impacts are analyzed in depth to provide insight into how SAVs may best be introduced to a city or region. Results indicate that the cost-effectiveness of traveling with strangers overcomes inconvenience and privacy issues at moderate-to-low fare levels, with high fares being more detrimental than the base case. A moderately sized Austin, Texas fleet (one SAV for every 25 people) serves nearly 30% of all trips made during the day. The average vehicle occupancy of this fleet was around 1.48 [after including the 12.7% of SAV vehicle-miles traveled (VMT) empty/without passengers], with a 4.5% increase in VMT. This same fleet performs better when road-pricing is enforced in the peak periods (4 h a day), moderating VMT by 2%, increasing SAV demand and in turn fleet-manager revenue. SAVs are able to earn around $100 per SAV per day even after paying tolls, but only at low-fare levels.

Factors associated with driving under the influence of alcohol

Objective: This study examined the risk factors of driving under the influence of alcohol (DUI) among drivers of specific vehicle categories (DSC). On the basis of this research, the variables related to DUI and involvement in traffic crashes were defined. The analysis was conducted for car drivers, bicyclists, motorcyclists, bus drivers, and truck drivers.Method: The research sample included drivers involved in traffic crashes on the territory of Serbia in 2016 (60,666). Two types of analyses were conducted in this study. Logistic regression established the correlation between DUI and DSC and the The Technique for Order of Preference by Similarity to Ideal Solution (Multi-criteria decision making) method was applied to consider the scoring and explore the potential for the prevalence of DUI on the basis of 2 data sets (DUI and non DUI).Results: The study results showed that driver error and male drivers were the 2 most significant risk factors for DUI, with the highest scores and potential for prevalence. The nonuse of restraint systems, driver experience, and driver age are the factors with a significant prediction of involvement in an accident and an insignificant prediction of DUI.Conclusions: Following the development of the logistic prediction models for DUI drivers, testing of the model was conducted for 3 control driver groups: Car, motorcycle, and bicycle. The prediction model with a probability greater than 50% showed that 77% of car drivers were under the influence of alcohol. Similarly, the prediction percentage for motorcyclists and bicyclists amounted to 71 and 67%, respectively. The recommendation of the study is that drivers whose DUI probability is above 50% should be potentially suspected of DUI. The results of this study can help to understand the problem of DUI among specific driver categories and detect DUI drivers, with the aim of creating successful traffic safety policy.

Sleeping, sleeping environments, and human errors in South Korean male train drivers.

ObjectivesReducing human errors caused by daytime sleepiness among train drivers is important to prevent train accidents. Our purpose of the study was to investigate the association among sleep, workplace sleeping environments, and human errors.MethodsWe recruited 144 South Korean train drivers belongs to the Korean Railroad Corporation. This cross‐sectional data was analyzed to investigate the association of insomnia (insomnia severity index), sleep quality (Pittsburgh sleep quality index), obstructive sleep apnea (Berlin questionnaire), and daytime sleepiness (Epworth scale) with human error and near‐miss experiences. We examined whether human error and near‐miss events were associated with various sleeping environments at work and at home after adjusting for the sleep indices.ResultsThe experience of human errors was associated with insomnia and daytime sleepiness, and near‐miss events were associated with insomnia among South Korean drivers. Sleeping environments including cold temperature and odor were related to both human errors and near‐miss events among South Korean train drivers, after adjusted for age, working years, shiftwork, obesity, smoking, binge drinking, regular exercise, caffeine consumption, sleep quality, severity of insomnia, obstructive sleep apnea, and daytime sleepiness.ConclusionsThe train drivers’ workplace sleeping environment is significantly associated with human error events and near‐miss events after adjusting for sleep quality, insomnia, obstructive sleep apnea, and daytime sleepiness. To prevent train accidents caused by human errors, more attention is necessary for improving workplace sleeping environments.

Braking System Automation on Cars using a Distance Sensor

The factor of driver error in driving (human error) is one of the causes of the high number of traffic accidents in the present. To anticipate this, the discipline and concentration of the driver when driving is needed. However, when drivers have discipline and sufficient concentration of accidents can still occur. Therefore, the quality of the vehicle security system also greatly affects safety when driving. With the development of science and technology today it is very possible for humans to make security systems in vehicles. Accidents are often caused by the driver being unable to react quickly when there is a sudden blocking of objects. Especially when the driver is driving at high speed. This can be overcome by automating the braking system, so that the vehicle's speed will slow down even though the driver does not step on the brake lever. So that the possibility of an accident can be avoided. And automation is expected to be implemented in vehicles widely. In this study, designed a brake automation system using fuzzy logic by making a prototype. This prototype serves as a visual aid to evaluate the workings of the membership functions that will be used. From this evaluation it is known the value of each membership function. These values ​​are used as the main parameters in determining the Rules that will affect the output value of the brake force. With these Rules, automation of the brake system can work optimally. And it is known that the distance of the prototype car can run between other obstacle with a width of 50cm and the braking of the prototype car stops completely when the prototype is ± 5-10 cm with the obstacle in front of it.
 Keywords: Automation of Brake Systems, Fuzzy Logic, Distance, Speed.

Joint Optimization of Signal Phasing and Timing and Vehicle Speed Guidance in a Connected and Autonomous Vehicle Environment

A joint traffic signal optimization algorithm is proposed which utilizes connected vehicle (CV) information to identify optimum signal timing and phasing plans while also providing speed guidance to individual vehicles to minimize total number of stopping maneuvers. The contribution of this paper is provision of speed guidance to both autonomous (AVs) and human-driven speed guidance-enabled vehicles (SGVs), recognizing that the latter may not fully comply with the speed guidance and would require some delay (i.e., reaction time) to implement it. The control algorithm is triggered at regular discrete time intervals during which CV information is used to identify the presence of non-CVs and incorporate them into signal timing decision-making. Optimal speeds are determined for any AVs or SGVs so that they can travel through the intersection at the expected departure time without stopping, considering both acceleration/deceleration and human reaction times. Simulation tests are performed under different CV, AV, and SGV penetration rates, while explicitly modeling the potential human errors and varying acceptance levels. The results suggest that average delay and number of stops decrease with higher CV penetration rate. Furthermore, the number of stops decreases as the ratio of both AVs and SGVs increases. While AVs are about 10% more efficient than SGVs, human-driven vehicles still provide a benefit even when they do not fully comply with speed guidance information. Sensitivity tests suggest that operation is not significantly affected by the range of human driver errors in speed compliance or range of reaction times.

The effect of aggressive driver behaviour, violation and error on vehicle crashes involvement in Jordan

The current study aims at examining the effect of aggressive driver behaviour, violation and error on motor vehicle crashes and investigating the factor structure of the Driver Behaviour Questionnaire (DBQ) factors on the crash involvement in Jordan. This is a cross-sectional study design conducted during September, 2016 to May 2017 in Amman, the capital of Jordan. The study included a representative sample of aged 22-65 years old 1450 drivers of which 1084 drivers agreed to participate (74.7%). The Manchester DBQ instrument was used to measure the aggressive, aberrant driving behaviours leading to crashes. Univariate and Multivariate logistic regression statistical analysis were performed. Out of a total 1084 responding drivers, 778 (71.7%) were males and 306 (28.3%) were females. The study revealed a statistically significant difference between males and females drivers in terms of age (p < 0.001), education (p < 0.001), occupation (p = 046), marital status (p < 0.001), income (p < 0.001), driving experience (p < 0.001) and seat belts use (p < 0.001). Most of drivers admitted to ever had crashes (p < 0.001), careless driving (p < 0.001), having excessive speed (p < 0.001), smoking while driving (p < 0.001), crossing red traffic light (p < 0.001), using mobile phone (p < 0.001) and using SMS while driving (p = 0.041). The DBQ mean scores of males were significantly higher in violations, error and frequently reported lapses whereas the mean driving skills score was similar between male and female divers. The factor included nine items of violations, six items of errors and five items of lapses in the Jordanian driver sample. Further, multivariate logistic regression analysis revealed violations, mobile phone use, excessive speed, driving skills, errors, lapses and type and size of vehicle significantly increased the risk of crash involvement. The results of the study demonstrated the increased risk of road traffic crashes among young Jordanian driverswhich had history of injuries at the time of crash (37.8%). Excessive speeding and mobile phone were the strongest predictors for the crash occurrence.

Personality Traits and Attitudes Toward Traffic Safety Predict Risky Behavior Across Young, Adult, and Older Drivers.

In the last few decades, several studies have investigated the role of personality traits and attitudes toward traffic safety in predicting driving behaviors in diverse types of drivers across several countries. However, to the best of our knowledge, no studies so far have investigated the possible moderating role played by age in relation to predictors of accident risk. Answering this open question would provide information about the generalizability of the model across different subpopulations and would make possible the tailoring of the interventions to specific target groups. The study involved 1,286 drivers from three different age groups (young: n = 435; adult: n = 412; old: n = 439) which completed a questionnaire measuring drivers’ personality traits (i.e., anxiety, hostility, excitement seeking, altruism, normlessness), positive attitudes toward traffic safety, risky driving behaviors (i.e., errors, lapses, and traffic violations), accident involvement and number of traffic fines issued in the last 12 months. Multi-group Variance Based Structural Equation Modeling (VB-SEM) across the three age groups showed that the hypothesized model had a good fit with the data in all the three age groups. However, some pattern of relationships between the variables varied across the three groups, for example, if considering the direct effects of personality traits on risky driving behaviors, anxiety, altruism, and normlessness predicted violations only in young and adult drivers, whereas excitement seeking was associated with lapses only in young drivers; anxiety was a positive predictor of drivers’ errors, both in adult and older drivers, whereas excitement seeking predicted errors in adult and young drivers. On the other hand, attitudes significantly and negatively predicted violations and errors in all the three age groups, whereas they significantly and negatively predicted lapses only in young and older drivers. The results of the present study provided empirical basis to develop evidence-based road safety interventions differently tailored to the specific life’s stage of the drivers.

From traffic conflict simulation to traffic crash simulation: Introducing traffic safety indicators based on the explicit simulation of potential driver errors

This paper presents a methodology that could support researchers and engineers in the important and original issue of the simulation of crashes and the evaluation of consequences on existing microsimulation packages. The proposed approach, by introducing potential driver errors, specifically allows consideration of the potential conflicts with road side barriers and obstacles and between vehicles that are moving on trajectories (close in space) that do not intersect. The severity of resulting crashes can, then, be evaluated in terms of accurate crash dynamic variables. Moreover, the methodology presented in the paper allows researchers to develop new conflict indicators that overcome many limitations of traditional traffic safety conflict indicators which, unfortunately, do not consider: the types of vehicle that are involved in a conflict, roadside obstacles or barriers, vehicles which are travelling on non-conflicting trajectories and the severity of a potential resulting crash. The paper applies a specific family of new simple and reproducible conflict indicators and provides the information necessary to develop new algorithms and simulation methodologies which, introducing human error in traffic simulation, may revolutionize the way traffic microsimulation can be used to establish a traffic safety for different scenarios. Feasibility tests on simple networks are presented and the results show the feasibility of making safety evaluations for different traffic scenarios by introducing driver errors.

Active rectifying control of vehicle with tire blowout based on adaptive fuzzy proportional–integral–derivative control

To avoid casualties and economic loss caused by vehicle yawing motion during the tire blowout, first, by changing several key parameters of the characteristics, this article uses CarSim software and MATLAB/Simulink to establish a vehicle model of tire blowout based on the UniTire model. This model is implemented to simulate tire blowout caused by the change of the vehicle motion state. Second, considering the driver error and radical-operated steering wheel after tire blowout leads to runaway car problems. This article takes the target trajectory and actual trajectory of error and error rate as the system input; an adaptive fuzzy proportional–integral–derivative controller is designed to determine the vehicle steering wheel angle during the tire blowout and replace the driver to control the direction of the vehicle. The results indicate that the designed controller is capable of ensuring the vehicle constancy and keeping the vehicle on the original track.

An analysis of the relationship between driver characteristics and driving safety using structural equation models

Traffic crashes pose a serious threat to global health and have negative impacts on social and economic development. Driving safety could be significantly improved if drivers obtained effective training based on practical suggestions from studies of the relationship between driver characteristics and driving safety. In this study, driver characteristics are analyzed with a focus on illegal driving behaviors, and a new method is developed to reduce driver error. However, driver characteristics are complex, and the associated interrelationships among variables are not easily identified. A structural equation model (SEM) is adopted in this paper to capture the complex relationships among related variables. This model can simultaneously address the complex relationships among endogenous and exogenous variables. We investigate five illegal driving behaviors, including illegal passing, illegal lane changes, speeding, running red lights and distracted driving, based on a driving simulator. Distracted driving is chosen as an example to construct the SEM. We group 13 variables into four latent variables in the model. In this paper, 44 participants (average age ± S.D. = 33 ± 12.8 years, range: 19–55 years; average driving experience ± S.D. = 10 ± 8.8 years, range: 2–30 years) were recruited for the experiment, driving behavior data were collected via a driving simulator and driving attitude data were collected using a questionnaire. Our findings reveal that the main factor that influences illegal driving actions is driving attitude and the main factor that influences distracted driver performance is basic driver characteristics. Moreover, the driver training level is the most significant factor that negatively affects the basic driver characteristics (factor load = −0.91), and anger with slow driving is the main factor that influences the driver attitude (factor load = 0.90). According to the SEM results, the relationship between driver characteristics and driving safety is analyzed. The model results suggest that driver training lessons must be implemented and traffic issues must be urgently solved. In addition, our results suggest that extra training sessions should be required of young females and drivers with bad attitudes and serious road rage to improve driving safety. Although the results are based on distracted driving, the method presented in this paper is suitable for other analyses of driving behavior.

Multivariate copula temporal modeling of intersection crash consequence metrics: A joint estimation of injury severity, crash type, vehicle damage and driver error

This study employs a copula-based multivariate temporal ordered probit model to simultaneously estimate the four common intersection crash consequence metrics – driver error, crash type, vehicle damage and injury severity – by accounting for potential correlations due to common observed and unobserved factors, while also accommodating the temporal instability of model estimates over time. To this end, a comprehensive literature review of relevant studies was conducted; four different copula model specifications including Frank, Clayton, Joe and Gumbel were estimated to identify the dominant factors contributing to each crash consequence indicator; the temporal effects on model estimates were investigated; the elasticity effects of the independent variables with regard to all four crash consequence indicators were measured to express the magnitude of the effects of an independent variable on the probability change for each level of four indicators; and specific countermeasures were recommended for each of the contributing factors to improve the intersection safety.The model goodness-of-fit illustrates that the Joe copula model with the parameterized copula parameters outperforms the other models, which verifies that the injury severity, crash type, vehicle damage and driver error are significantly correlated due to common observed and unobserved factors and, accounting for their correlations, can lead to more accurate model estimation results. The parameterization of the copula function indicates that their correlation varies among different crashes, including crashes that occurred at stop-controlled intersections, four-leg intersections and crashes which involved drivers younger than 25. The model coefficient estimates indicate that the driver’s age, driving under the influence of drugs and alcohol, intersection geometry and control types, and adverse weather and light conditions are the most critical factors contributing to severe crash consequences. The coefficient estimates of four-leg intersections, yield and stop-controlled intersections and adverse weather conditions varied over time, which indicates that the model estimation of crash data may not be stable over time and should be accommodated in crash prediction analysis. In the end, relevant countermeasures corresponding to law enforcement and intersection infrastructure design are recommended to all of the contributing factors identified by the model. It is anticipated that this study can shed light on selecting valid statistical models for crash data analysis, identifying intersection safety issues, and helping develop effective countermeasures to improve intersection safety.

Increasing the efficiency of vehicle ad-hoc network to enhance the safety status of highways by artificial neural network and fuzzy inference system

The future of traffic control and management may manifest itself in the form of digital and automatic methods, which could help to decrease driver errors. One of the most important technologies in this area is the Vehicle Ad-Hoc Network (VANET), in which each vehicle is a node that can receive and transmit information from and to other vehicles. Such systems can be used for safety or nonsafety purposes. This study investigates the safety applications of this system and aims to develop a method to detect and record dangerous situations for each vehicle based on microscopic traffic data. To detect danger, VANET must transmit data between vehicles and traffic safety indicators must be applied. To increase the efficiency of VANET, artificial neural network (ANN) and fuzzy inference system (FIS) were used. This study focuses on rear-end collisions in car-following situations. For this purpose, microscopic traffic data were collected along a 400-meter long segment of the Modares highway in Tehran, Iran. Based on the analysis, applying FIS to develop a new safety index was found to help detect rear-end collisions. Results also indicate that the average mean of errors based on the motion predictions generated by ANN is negligible and that an adequate history of motion for a vehicle diminishes such errors. Therefore, applying artificial intelligence can improve the workability of VANET in detecting a dangerous car-following situation that might lead to a rear-end collision.

Behavioural impacts of Advanced Driver Assistance Systems–an overview

The purpose of Advanced Driver Assistance Systems (ADAS) is that driver error will be reduced or even eliminated, and efficiency in traffic and transport is enhanced. The benefits of ADAS implementations are potentially considerable because of a significant decrease in human suffering, economical cost and pollution. However, there are also potential problems to be expected, since the task of driving a ordinary motor vehicle is changing in nature, in the direction of supervising a (partly) automated moving vehicle.

Improving the Safety of Urban Freight Deliveries by Organization of the Transportation Process Considering Driver’s State

Nowadays, the problem of ensuring traffic safety has become especially acute due to the growth of the number of enterprises engaged in goods transportation. The driver is the main and direct participant in the implementation of the transport process. The organization of the drivers work significantly influences the level of productivity, efficiency and safety of traffic. The reason for driver errors during long-term driving is fatigue that reduces performance and can form the direct cause of road traffic accidents or an unfavorable condition that complicates the driver’s actions in emergencies. This study assesses the driver’s state evolution when freight is being transported along a set route in a city. The driver’s state is assessed using an indicator of the regulatory systems activity based on the analysis of the driver’s cardiograms during the performance of particular elements of the transport process. The “Driver–Vehicle–Route–Environment” system is described using parameters such as: the driver’s age and driving experience, indicator value of the regulatory systems activity before the start of movement on the route, vehicle attributes, route length and driving time. The impact of each parameter on the driver’s state was evaluated that allowed the parameters of freight transportation technology to be determined, which are rational from the standpoint of reducing driver’s fatigue. The results obtained made it possible to determine a model of the driver’s state evolution during urban freight transportation. The research results can be used by forwarders and transport companies for route planning and scheduling with regard to human factors.

Estimation of Driver's Danger Level when Accessing the Center Console for Safe Driving.

This paper proposes a system for estimating the level of danger when a driver accesses the center console of a vehicle while driving. The proposed system uses a driver monitoring platform to measure the distance between the driver’s hand and the center console during driving, as well as the time taken for the driver to access the center console. Three infrared sensors on the center console are used to detect the movement of the driver’s hand. These sensors are installed in three locations: the air conditioner or heater (temperature control) button, wind direction control button, and wind intensity control button. A driver’s danger level is estimated to be based on a linear regression analysis of the distance and time of movement between the driver’s hand and the center console, as measured in the proposed scenarios. In the experimental results of the proposed scenarios, the root mean square error of driver H using distance and time of movement between the driver’s hand and the center console is 0.0043, which indicates the best estimation of a driver’s danger level.

Key considerations for automated enforcement of non-compliance with road rules at railway level crossings: The Laverton case in Victoria, Australia

In Australia, most railway level crossings in high-traffic road environments are actively protected. Despite this, crashes at these crossings account for two thirds of the costs of crashes at all level crossings. Such crashes typically arise from non-compliance with road rules. Current protections and interventions have mainly focused on reducing driver errors through increased level crossing awareness. The rail industry is now exploring the use of automated enforcement at level crossings to reduce non-compliance. However, very little research has evaluated the potential benefits of such an approach, or provided a sound scientific evidence-base for its utility within Australia. The aim of this research is to create a scientific evidence-base reflecting the size of the problem at such crossings, and to understand whether the current automated enforcement policies used on the road can be viably adapted for level crossings. A level crossing in a suburb called Laverton close to Melbourne was selected as a study site based on reported congestion issues and high rates of non-compliance to road rules. Monitoring equipment (cameras and radar) was installed for two months to obtain a baseline dataset of non-compliance with a two-day observation study conducted at the site prior to the installation in order to evaluate the reliability of these data. Over 700 separate road user non-compliance events were recorded with the equipment – such as entering the level crossing while flashing lights were activated, and stopping on the level crossing, highlighting that non-compliance at active level crossings can be commonplace. The equipment was found to be effective at detecting some non-compliance events but not very effective at detecting others, such as occurrences of vehciles stopping on the crossing. Further studies need to be conducted to determine whether such technology could modify driver behaviour at such level crossing, and reduce rates of non-compliance.

Impact of Train Drivers’ Cognitive Responses on Rail Accidents

Despite the innovations in automatic train control (ATC) systems to reduce the risk of driver error, many rail accidents still occur due to defects in these systems, emphasizing the essential role of the driver in preventing rail accidents and proper control of the train. This paper studies the influence of drivers’ cognitive performance, including attention and visual perception, on the occurrence of rail accidents. The research is conducted using so-called Ex-Post facto method on a random sample of 56 train drivers with a minimum of three years of experience. The research instruments included drivers’ cognition test system including WAFV (perception and attention function) sustained attention test, COG (cognitrone) selective attention test, LVT (visual pursuit) visual perception test, demographic questionnaire, and drivers’ safety history. Results of this research showed that there is no significant relationship between age and education level of train drivers, and rate of occurrence of rail accidents. A comparison on train drivers’ cognitive characteristics, between drivers with accident record(s) and those without, showed that the drivers who had experienced rail accident(s) had lower levels of sustained attention. However, no significant difference was found between the two groups in selective attention and visual perception. Investigating the association of drivers’ ages with their levels of sustained attention, the drivers with high levels of sustained attention were found to be significantly older than other drivers. According to practical implications of these findings, cognitive rehabilitation courses are recommended for train drivers to attenuate the risk of rail accidents.

Adaptive cruise control system with traffic jam tracking function based on multi-sensors and the driving behavior of skilled drivers

The traditional adaptive cruise control system generally requires 25–40 km/h velocity to function. Moreover, the adaptive cruise control system cannot decelerate to the stop state, cannot adjust for stationary objects, and has limited scope of application. This study achieved the traffic jam tracking function of vehicles through the simultaneous use of millimeter wave and laser sensors and the analysis of the driving behavior of skilled drivers. The spacing and acceleration control of a vehicle is optimized based on the premise of ensuring safety and comfort by providing smooth, comfortable, safe, and radical control modes for driver selection, thereby increasing the probability that adaptive cruise control adopted by drivers. In addition, the collision avoidance function is added for safety reasons. Finally, actual vehicle experiments show that the distance and acceleration errors are in the expected range of errors of drivers. Moreover, the validity and practicability of the proposed adaptive cruise control algorithm are verified.