In this study, a driver model with the multiple regression and the neural network is constructed to analyze the relationship between the driver's control action and the information that includes data of vehicle behavior and environment. Using these models, effectiveness of the information to control the action of a driver is examined. To evaluate the intelligent driver support systems, Mental Work Load (MWL) model is constructed with the multiple regression and the neural network. MWL is expressed as Heart Rate Variability (HRV). Measured HRV data and calculated HRV data with MWL model show good agreement. Effectiveness of information is examined using the MWL model. From these results, it is shown that the analytical method with the driver's MWL can be used to assess and improve the intelligent driver support systems as the next stage of this research.

This study focuses on changes in the friction characteristics of paved roads under various conditions from the viewpoint of traffic safety. In general, the braking characteristics of road vehicles are examined using the μ–s characteristics of the tires. Therefore, in our research, we used three types of limited datasets and identified them using the Magic Formula proposed by Prof. Pacejka. Based on various experiments, it was shown that changes in the road surface environment, such as dry and wet conditions, significantly affect the μ–s characteristics, and this influence varies significantly depending on the pavement conditions. In this study, as the first stage of the analysis of these influences, it was clarified that the difference in the friction characteristics of wet and dry road surfaces varies significantly depending on the pavement surface, which is based on experimental results obtained using actual roads. As this variation is closely related to the safety of actual road traffic, we used a brush model, which is a dynamic model of the road surface and tires, to clarify the differences in this characteristic.

This study focuses on the possibility of constructing a database on friction coefficients for actual roads from the viewpoint of traffic safety. A measurement algorithm is established to construct a road friction-measuring device. Next, the tires are selected for use in the measurements and their characteristics are measured using a bench tire characteristic tester. The measuring device is designed and constructed based on these characteristics. Finally, using this device, the measurement results of the road friction characteristics for two types of road surfaces are presented.

This study deals with the possibility of construction of a database on the braking friction coefficient for actual roads from the viewpoint of traffic safety, especially for automated driving, such as level 4 or higher. At these levels of automated driving, the controller needs to control the vehicle. However, the road surface condition, especially the road friction coefficient on wet roads and snowy or icy roads, changes greatly, and in some cases, changes by almost one order. Therefore, it is necessary for the controller to constantly collect environment information, such as the road friction coefficients, and prepare for emergencies, such as obstacle avoidance. However, at present, the measurement of the road friction coefficients is not systemically performed, and a method for accurately measuring has not been established. In order to improve this situation, this study examines a method for continuous measurement of the road friction characteristics, such as the μ-s characteristics. It is shown that the μ-s characteristics are continuously measured using the MF generally used in tire engineering, and the friction characteristics identified from the results are sufficiently satisfactory.

This study deals with construction of estimation algorithm for road conditions in order to predict tire-road friction ahead. Road condition sensing system consists of alebeo value, road image processing and laser rader units. Firstly, field test drives have been carried out to verify the performance of these sensors. From the result, these sensors could decide the difference of the road condition. If the road condition is complication, it is difficult to distinguish by one sensor. Next, in order to deal with these measurement results comprehensively, this estimation algorithm was constructed using a learning system with these values. Finally, it is found possibility that the proposed algorithm could estimate the difference of the road conditions efficiently.

This paper deals with the possibility for construction of the database on friction coefficient on the actual roads from the viewpoint of traffic safety. A measurement algorithm is determined in order to construct road friction measuring device. Next, the tires used for measurement are selected and the characteristics are measured using bench tire characteristic tester, and the measuring device is designed and constructed based on the characteristics. Finally, using this device, the measurement results of the road friction characteristics for two types of road surfaces are shown.