When changing lanes, the driver chooses his or her behavior by predicting the future behavior of surrounding vehicles. In this case, the optimal behavior depends on the extent to which the driver is aware of the behavior of surrounding vehicles. In this study, we formulated the lane change behavior under such incomplete information based on an extensive-form game. Focusing on the difference in cognitive ability between automated and manual vehicles, we attempted to analyze the lane change behavior and traffic conditions under mixed conditions. In addition, we proposed a method to improve traffic conditions in mixed traffic situations through cooperative control of automated vehicles. In order to guarantee the incentive to participate in the cooperative control from the viewpoint of social acceptability, a dynamic pricing method based on auction theory and its theoretical properties are presented.

This study proposes a non-parametric estimation method of the survival function using general measurement data. We have proposed an estimation method using measurement data based on the LBS (Length-biased Sampling), assuming the observation opportunity is continuously as well as uniformly distributed. This study relaxes this assumption and propose a method that can be applied to more general measurement data. However, this study does not consider left-censored data. Compareing the proposed LBS method with one based on NS (Natural Sampling), it is revealed that the likelihood function to be maximized is the same for both methods under the uniform measurement. Lastly, the proposed method is validated and the applicability of the method in practice is discussed.