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• We quantify the cognitive load under varying non-driving-related tasks (NDRTs) in automated driving by physiological state of eye movement. • Dempster-Shafer (D-S) Evidence Theory is used to associate the workload of different typical scenarios, NDRTs and the safety of takeover. • Takeover safety is predicted by Convolutional Neural Networks (CNN) based on cognitive load. • The effect of window size of the input data for takeover safety prediction is especially considered. Automated vehicles alleviate the need for driver attention and control. However, a takeover request (TOR) to the driver remains essential for emergencies and safety–critical scenarios beyond automation's capability. Thus, accessing drivers' safety performance in various TOR scenarios is crucial for conditionally automated driving (SAE L3). However, TOR safety performance is seldom examined concerning drivers' cognitive load, despite its presumed relevance to TOR scenarios and human–machine interaction. Moreover, the adequacy of the time window preceding a TOR, critical for TOR safety prediction, remains inadequately explored. This study aims to assess safety performance across diverse TOR scenarios in Level 3 conditional automation, incorporating drivers' cognitive load, and predict TOR safety by considering the time window's impact. A driving simulator experiment gathered eye movement and driving behavior data from 37 recruited participants. Participants were instructed to take control of the vehicle from automated driving within a time budget (TB) of 3 s or 7 s in obstacle avoidance (OA) or lane keeping (LK) scenarios while engaging in non-driving-related tasks (NDRTs). Participants' subjective cognitive load in the different TOR scenarios was scaled using NASA-TLX. Furthermore, safe TOR performance was predicted utilizing Convolutional Neural Networks (CNNs) across different time window sizes preceding a TOR. The results indicate that: 1) cognitive loads in takeover scenarios ranked from highest to lowest as TB = 3s_OA, TB = 3s_LK, TB = 7s_OA, TB = 7s_LK; and the cognitive loads of the NDRTs ranked from highest to lowest as mistake finding, texting, chatting, monitoring; 2) the takeover safety performance in the four scenarios from lowest to highest was TB = 3s_OA, TB = 3s_LK, TB = 7s_OA, TB = 7s_LK; likewise, the takeover safety performance during the four NDRTs ranked from lowest to highest as mistake finding, monitoring, texting, chatting; 3) the time window size before the TORs significantly affected the prediction performance of the model. A 30-second window was recommended as optimal for predicting takeover safety using the CNN model, achieving an average F1 score of 0.8120 and 81.98 % accuracy. This study's findings enhance our comprehension of driving behavior characteristics during TOR and offer valuable insights for detecting driver states in conditionally automated driving contexts. [ABSTRACT FROM AUTHOR]

• QN-ACTR could model effect of limited sight distance on car-following performance. • Cognition modeling provides support for adverse fog traffic safety assessments. • Frequent eye movement led to increasing of cognitive time when driving in fog. • Driver's perception, decision-making and control behavior were more complex in fog. • Reduction of cognitive capacity led to an impairment of the overall driver behavior. This study explored human error risk factors in car-following behaviors, cognitive resource bottlenecks and performance impairment mechanisms in the context of cognitive capacity while driving on foggy roads. Firstly, a hierarchical driving behavior assessment was used to observe risky driving behavior in a foggy area. A driver's dynamic speed adjustment analog experiment was carried out in foggy conditions. Unique parameters were substituted into the traditional QN-ACTR driver behavior model. The visual far-time of the traditional model and the latitude-longitude control sensitivity parameters were estimated. Foggy conditions were modeled based on the QN-ACTR cognitive architecture to observe the effect of limited sight distance through fog on car-following performance. Jsim in the Eclipse environment and TORCS were applied in co-simulation and verified. The results show that (1) the proposed cognitive modeling approach effectively simulates a foggy driving environment and allows researchers to study affected and related driver behavior; and (2) the car-following performance in low visibility is significantly worse than in high visibility. These findings provide theoretical support and a scientific basis for the study of speed, safe distance, standard of limited speed and design methods for engineering safety mechanisms to counter the negative effects of driving on foggy roads. This study can inform actions to increase road safety during fog. [ABSTRACT FROM AUTHOR]

With the rapid development of Intelligent Transport Systems (ITS), emerging technologies such as Advanced Vehicle Driving Assist Systems, and Connected Vehicles, Intelligent Vehicles introduce new opportunities and challenges into our traffic systems and raise questions regarding the interaction of drivers' cognitive and driving behaviors with these technologies. Meanwhile, development of these technologies makes innovative vehicle data collection, Vehicle to Vehicle communication, and Vehicle to Infrastructure communication possible. [4] introduced a driver behavior influence factor associated with drivers' driving styles comparing to a theoretical curve speed model that only considers the vehicle-road interaction. Driverless vehicles promise to remove the human operator in favor of safer driving, but drivers still need to participate driving tasks in the lower tiers of vehicle automation. [Extracted from the article]

Existing fatigued driving analysis methods mainly focus on lateral driving performance by using the measurements related to the steering wheel or lane position. There is a lack of research on longitudinal car following behavior. In this study, 40 professional drivers are invited to participate in field expressway driving experiment, lasting at least for 6 h. During the test, their performance is measured in terms of their self-reported fatigued driving level according to the Karolinska Sleepiness Scale (KSS), the PERcentage of eye CLOSures (PERCLOS) and the Time Headway (THW). Then the effects of the fatigued driving level on car following behavior are evaluated. The results indicate that the fatigue level (for both KSS and PERCLOS) has significantly impact on THW parameters, including the mean, standard deviation and minimum THW. An increase in KSS and PERCLOS leads to a lower mean and minimum THW. Meanwhile, the standard deviation of THW increases with the increase of KSS and PERCLOS. In conclusion, this study found that a higher fatigue level leads to the driver keeping a smaller THW when following another vehicle and choosing shorter THW to make lane change. More deviation of car following performance was also found with the increase of fatigue level. Therefore, the findings of this study can be used to explain fatigue as one of the major reasons for rear-end collisions. Also, the research findings demonstrate the impact of fatigue on driving behavior in terms of car following performance, which can be used as a measurement for monitoring fatigued drivers. [ABSTRACT FROM AUTHOR]

Achieving road safety depends on driver attitudes and behaviours in handling the vehicle on roads. The availability of good road, improvement of vehicle designs and drivers experience lead to reduction in crashes but not prevention of crashes. The study aims to predict the drivers’ intentions towards speeding and overtaking violations when under the influence of motivational factors using belief measure of TPB and DBQ variables. To achieve this, questionnaires were randomly administered to a sample of Ghanaian drivers ( N = 354) who held valid driving licenses. This study applied regression techniques. The result shows that the components of TPB and DBQ variables were able to predict drivers’ intentions towards speeding and overtaking violations. The study further shows that components of TPB made larger contributions to the prediction of divers’ intentions to speeding and overtaking than the DBQ. Further analysis revealed that, in the prediction of drivers’ intentions, speeding attitude was the most frequent violations compared to overtaking. The drivers tend to involved in overtaking violations when they perceived the driving motivations would enhance the performance of the behaviour. Additionally, control belief has been the strongest predictor of drivers’ intentions under the influence of motivations to speeding and overtaking violations. It appeared that the drivers who intended to involve in speeding and overtaking violations had strong beliefs in the factors and are more likely to violate based on their beliefs. The practical implications of the findings for the development of interventions to promote road safety and positive changes are also discussed. [ABSTRACT FROM AUTHOR]

• Driver behaviour influence factor was introduced to the curve safe speed model. • Correlation was examined between DBQ subscales and driver behaviour influence factor. • The violations subscale in DBQ was utilized to classify drivers' driving style. • Simulation study indicated the practicability of the proposed curve speed model. • The proposed model is a successful application of DBQ. Inappropriate curve speed influenced by the interactions of driver behaviours, vehicle dynamics and road environments is the dominant cause of vehicle lateral instability induced crashes, like sideslips and rollovers. The present study introduced a driver behaviour influence factor associated with drivers' driving styles comparing to a theoretical curve speed model that only considers the vehicle-road interaction. This factor is defined as the ratio of drivers' actual selected speed to the theoretical curve speed. Aiming at deriving the factor for different driving styles, it was utilized the 28-item Chinese version of Driver Behaviour Questionnaire (DBQ). A correlation analysis between DBQ subscales and the factor indicated that a driver with higher violations scores is prone to drive faster in curve negotiation. Based on this finding, 24 experienced professional drivers were classified into two types, i.e. the moderate and the aggressive, corresponding to their scores on DBQ violations scale. Through a simulation, it showed that the improved curve speed model could not only prevent the risks of rollover and sideslip, but also provided different appropriate curve safe speeds in accordance with drivers' driving styles. [ABSTRACT FROM AUTHOR]

• The relationships between traffic climate and driver behaviors were examined cross-culturally. • External affective demands and aberrant driver behaviors were positively related in Turkey and China. • External affective demands and positive driver behaviors were negatively related in Turkey and China. • Violations were negatively related to functionality in Turkey and negatively related to internal requirements in China. • The patterns of relationships might show both similarities and differences between countries. Road traffic accidents/fatalities and driver behaviors show regional differences. It is assumed that the perceived traffic climate in a given context is closely related to driver behaviors. In the current study, this assumption was tested cross-culturally for the first time. The aim was to compare the perceived traffic climate and driver behaviors between Turkey and China. Also, the relationships between traffic climate and driver behaviors in Turkey and China were investigated. In the study, there were 292 drivers aged between 21 and 64 years from China and 294 drivers aged between 19 and 61 from Turkey. The results revealed that Turkish drivers perceived their traffic climate higher in internal requirements and lower in external affective demands and functionality than Chinese drivers. Among driver behaviors, Turkish drivers reported higher numbers of violation and lower numbers of error than Chinese drivers. Perceiving traffic climate as externally demanding was positively related to aberrant driver behaviors (i.e., violations and errors) and negatively related to positive driver behaviors in both Turkey and China. Functionality was negatively related to violations in Turkey, and internal requirements were negatively related to violations in China. In both Turkey and China, external affective demands and functionality were closely related to driver behaviors and might be critical dimensions in road traffic safety. The interventions to improve road traffic safety should be planned based on the differences among cultures. Based on the differences, it might be plausible to suggest that more functional traffic is desired to increase road safety in Turkey, whereas higher internal requirements are important to increase road safety in China. [ABSTRACT FROM AUTHOR]

• 1319 questionnaires were collected from cargo TTWs groups all China. • Most attributes of Cargo TTWs groups: young men, less experience, high workload. • The three-layer risk framework was verified and extent with SEM. • Mediating mechanism, "risk factors - violations - errors", have been discovered. • Anxiety has a negligible risk effect and Workload has a direct weak risk effect. Cargo two- or three-wheeled vehicles (TTWs), as a new form of micro-mobility, have become a popular mode of urban cargo transportation in China. Cargo TTW riders' psychological factors and risky behaviors lead to a number of accidents. A questionnaire is designed by comprehensively considering these factors and behaviors of cargo TTW riders that includes eleven risk factors to quantitatively analyze the risky behaviors based on structural equation modeling (SEM). One thousand three hundred nineteen participants reported using cargo TTWs on a questionnaire distributed across the country. The characteristics of riding behavior data are analyzed to verify the three-layer risk theoretical framework of "Psychological factors (Personality traits/specific factors) - Psychological acceptability of risks (confidence/perception/attitude) - Risky behaviors". The results show that anger has a strong direct effect on riding violations, while normlessness and altruism have a direct effect on riding errors. Workload has a weak but direct effect on risky behaviors, and riding feedback has a weak and mixed effect. In addition, high-risk groups are identified by analysis of variance (ANOVA) with rider population attributes. These quantitative analyses can help guide safety countermeasures to mitigate accidents involving cargo TTWs. [ABSTRACT FROM AUTHOR]

• The effectiveness of ADAS on braking behavior is significant. • Drivers tend to increase braking times and reducing relative speed in the ADAS. • In any road conditions, when exposed in ADAS, the time headway has increased. • The impact of road categories and experience on driving behaviors is significant. • Drivers' acceptance on FCW function is higher than that of LDW. • Drivers' acceptance on expressway and freeway is higher than that on urban road. Various advanced driver assistance systems (ADAS) have been developed to improve drivers' behavior and perceptual ability; however, whether these ADAS have any measurable effect on driving performance needs to be verified by field operational tests. The purpose of this study was to evaluate the effectiveness of ADAS on Chinese drivers as well as any possible influences of roadway type, gender and experience on driving performance, which can be measured by several variables, including longitudinal, lateral and braking behavior. The ADAS used in this study was a Mobileye M630 with forward collision warning (FCW) and lane departure warning (LDW) functions. Thirty-two participants were recruited to drive a vehicle equipped with Mobileye M630. Participants drove the same test route twice. The route consisted of a 12 km urban road, 34 km urban expressway and 45 km freeway as well as a 14 km adaption road. Vehicle dynamics, environmental information and driving operational data was recorded by CAN (Controller Area Network) bus and video cameras. The results show that ADAS significantly affects braking behavior. Braking time increased and relative speed decreased when drivers were exposed to ADAS. The ADAS also significantly affects several longitudinal behaviors, including the longitudinal deceleration and time headway (THW). The occurrence of critically low THW decreased in the experiment. However, there was no significant effect on lateral behavior. Furthermore, driver acceptance of the FCW function was much higher than the LDW function, and acceptance on the expressway and freeway was much higher than on the urban road. The results also reveal the significant influence of road type and experience on driving behaviors. These findings support policy development and technology improvements for future development of ADAS. [ABSTRACT FROM AUTHOR]

• Effect of surrounding vehicles on target driver's behavior was described by force. • Vehicle cluster situation data was collected and analyzed. • Double variation process of cluster situation and driver's propensity was modeled. • The proposed model is verified by simulation, real and virtual vehicle experiment. The vehicle cluster situation is a kind of dynamic arrangement of a target vehicle and the surrounding vehicles during driving. Revealing the transfer mechanism of vehicle clustering in complex environments is of great significant for studying automated driving systems and driver assist systems. Taking three-lane scenario as an example, vehicle cluster situations changing with driver's evolving propensity were studied. To this end, the data of vehicle cluster situations were collected and analyzed through driving experiments in different environments. In addition, the dual random variations of the vehicle cluster situation and driver's propensity were modeled to explore the transfer mechanism. The verification results show that the predicted outcomes of vehicle cluster situations using the evolution rule of driver's propensity are consistent with the real-time recognition. Therefore, the transfer mechanism of vehicle cluster situations was found to be effective and reasonable. It is important for the research of intelligent driving command system of Internet of Things (IOT). [ABSTRACT FROM AUTHOR]

Although trucks only account for approximately 4% of all the vehicles based on the Federal Railway Administration (FRA) database, about 25% involved truck accidents happen at highway-rail grade crossings. This study applied an ordered probit model to explore the determinants of injury severity of truck drivers at highway-rail grade crossing in the United States. Given the importance of trucking to the economics of a country and the safety concerns posed by the trucks (as a result of their large size and weight making them difficult to control, maneuver, and stop), a comprehensive research on truck accidents is critical. Based on data analysis results, the strong effects of driver-, environmental-, weather- characteristics on the injury severities in truck accidents happened at highway-rail grade crossings are found. The findings reveal that better speed control for trucks will significantly reduce driver injury severity in accidents occurring at highway-rail grade crossings. In addition, several truck driver behavior characteristics (such as driving under influence of fatigue during peak hour) were found to be statistically significant predictors of high-level injury severity. Thus, education and enforcement targeted to truck drivers could facilitate safety improvements. Moreover, environmental factor (such as area type and roadway pavement) is found to be statistically significant. Truck drivers are more likely to have severe injury in open space area with low traffic volume compared with other areas. The bad weather and visibility condition is found to increase the probability of truck drivers’ high level injury severity. [ABSTRACT FROM AUTHOR]