A maximum likelihood method for driver-specific critical-gap estimation

In this work, we introduce a maximum likelihood (ML) method to estimate the smallest accepted gap of a specific driver, the so-called critical gap. Previous methods, like Troutbeck's or Raff's method, are well known and widely used but require a consistently behaving driver, which is usually not met. The methods will be investigated for the personalization of an intersection assistant, which we are currently developing. We start with the hypothesis that the size that constitutes an acceptable gap is driver-dependent. To test this hypothesis, we have performed a simulator study with 9 participants. The results reveal that there is a significant inter-individual difference in the critical gap between the drivers, as postulated. Next we investigate how well we can predict which gap the driver will take when we use the previously estimated personalized critical gap versus a driver independent one. Using the personalized gap compared to a driver independent one reduces the error from 11.8 % to 9 8 %. These results are a clear indication that personalization can be utilized to increase the effectiveness and usability of an intersection assistant.