Complex traffic situations and high driving workload are the leading contributing factors to traffic crashes. There is a strong correlation between driving performance and driving workload, such as visual workload from traffic signs on highway off-ramps. This study aimed to evaluate traffic safety by analyzing drivers’ behavior and performance under the cognitive workload in complex environment areas. First, the driving workload of drivers was tested based on traffic signs with different quantities of information. Forty-four drivers were recruited to conduct a traffic sign cognition experiment under static controlled environment conditions. Different complex traffic signs were used for applying the cognitive workload. The static experiment results reveal that workload is highly related to the amount of information on traffic signs and reaction time increases with the information grade, while driving experience and gender effect are not significant. This shows that the cognitive workload of subsequent driving experiments can be controlled by the amount of information on traffic signs. Second, driving characteristics and driving performance were analyzed under different secondary task driving workload levels using a driving simulator. Drivers were required to drive at the required speed on a designed highway off-ramp scene. The cognitive workload was controlled by reading traffic signs with different information, which were divided into four levels. Drivers had to make choices by pushing buttons after reading traffic signs. Meanwhile, the driving performance information was recorded. Questionnaires on objective workload were collected right after each driving task. The results show that speed maintenance and lane deviations are significantly different under different levels of cognitive workload, and the effects of driving experience and gender groups are significant. The research results can be used to analyze traffic safety in highway environments, while considering more drivers’ cognitive and driving performance.

Road alignment, traffic flow, and sign information interact and create complex traffic situations. To evaluate the effects of freeway horizontal radius, slope grade, traffic flow, and sign information on subjective driving workload and performance, a simulated driving experiment based on an orthogonal test design was conducted. The National Aeronautics and Space Administration Task Load Index was used to measure self-reported workload. Data regarding speed and lane deviation were collected through the driving simulator. A multivariate analysis of variance results indicated that the radius of the horizontal curve significantly influenced workload score, average speed, and lane deviation. It was observed that in a high workload environment, participants exerted more effort on the driving task; however, driving performance still decreased. Although there was no significant correlation between slope grade and subjective driving workload or performance, the primary effect of slope grade on workload and average speed was statistically significant. The amount of sign information significantly influenced the driver’s perceived workload; however, it did not significantly impact driving performance. In addition, the low correlation coefficient between subjective workload and performance was obtained. These research findings can provide insights for the design of freeway alignments and traffic signs to maintain optimal workload and minimize safety risks.

The quantitative evaluation of driving risk is a crucial prerequisite for intelligent vehicle accident warning, and it is necessary to predict it comprehensively and accurately. Therefore, a simulated driving experiment was conducted with 16 experimental scenarios designed through an orthogonal design, and 44 subjects were recruited to explore the driving risks in different situations. A two-layer fuzzy integrated evaluation model was constructed, which considered the workload as an important element for balancing driving risk and driving behavior. Workload and road environment indicators were taken as the underlying input variables. The results show that the comprehensive evaluation model is well-suited to identify the risks of each scenario. The effectiveness of the proposed method is further confirmed by comparing the results with those of the technique for order preference by similarity to an ideal solution (TOPSIS) model. The proposed method could be used for real-time vehicle safety warning and provide a reference for accident prevention.

Vehicle-pedestrian accidents are one of the main types of road traffic accidents in China because of their mixed traffic features. By analyzing the characteristics of vehicle-pedestrian accidents, the head injury criterion (HIC) was selected as a quantitative index of pedestrian head injury risk, and vehicle-pedestrian collision simulation tests were carried out using PC-Crash. From the collected test data, the multivariate relationship models between the HIC, vehicle speed, and collision angle were fitted for different vehicle types. A risk assessment method for vehicle-pedestrian accidents based on the HIC was proposed by the Fisher optimal segmentation algorithm. Finally, a new index for evaluating the accuracy of accident risk classification, the degree of error classification, was proposed to verify the validity of the accident risk assessment method. The results show that vehicle speed, collision angle, and vehicle type play a key role in pedestrian injury. Flat-headed vehicles are more likely to cause head injuries to pedestrians than high-headed and low-headed vehicles. Rear-end collisions cause more injuries to pedestrians than side collisions. The research results can provide guidance and a basis for accident liability determination, speed limit management, vehicle safety design, and human injury mechanism analysis.

Exit ramps are accident-prone areas of freeways. One of the reasons for this is the information overload induced by destination signs, which makes them challenging to recognize and may even result in tension or mistakes. This study examined the cognitive workload that destination signs place on drivers and the compensatory behavior they use to counteract the additional workload. Forty-five subjects were recruited to conduct a simulated driving experiment. Four destination signs with varying information levels were used to load mental workload as independent variables. Workload indicators (such as the NASA-TLX questionnaire, heart rate variability and eye movements) and compensatory behavior metrics (such as speed, distance headway, time headway and the increased cognitive time due to deceleration) were collected and analyzed through a mixed design analysis of variance. The results show that destination signs with different levels of information produced various cognitive workloads. Drivers' subjective workload increased significantly with an increase in sign information; however, the heart rate variability indicators did not significantly differ due to the drivers' compensatory strategy in the cognition process. The principal compensating strategy found was the longitudinal deceleration; as the speed dropped, the distance headway and time headway were significantly increased. Although professional drivers demonstrated a significantly greater speed differential during deceleration than nonprofessional drivers, the alternative safety indicators did not show statistical differences. Additionally, driving experience had no significant impact on the increased cognitive time of compensatory strategies. On the one hand, the effort invested by drivers increased with the workload. On the other hand, when the workload exceeded a particular degree, compensatory driving behavior was triggered, which in turn reduced the workload. There was no discernible difference in the effectiveness of the compensatory strategies employed by professional drivers and nonprofessional drivers under complex destination signs. The findings could provide safety considerations for the design of traffic signs.