Background: Merging on a highway is a complex driving task that requires a lot of interaction with other road users. During these tasks, a driver is required to evaluate gaps in space and time between the themselves and other road users and obstacles in order to arrive at the right moment to merge onto the highway. To improve safety and increase road efficiency, it is necessary to understand the decision process during merging decisions. A way of achieving this, is to understand what visual information humans use during this decision process. This study investigated the relation between gaze location and gap acceptance decisions during highway merges.
Methods: An experiment was performed in which 26 participants monitored an automated vehicle (AV) that was driving on a highway on-ramp. The participants were given the task to train the AV in whether or not to merge in front of an upcoming vehicle that was already driving on the highway. An eye tracker was used to measure gaze data, which was used to find the relation between gaze behaviour and decision outcomes and response times. A mixed-effects logistic model was used for a statistical analysis with decision outcomes as a dependent variable and different gap sizes as predictor variables. A mixed-effects linear model was used to find the relation between response times and dwell times and the different gap sizes and decision outcomes as predictor variables. For both the decision outcome and response time model, dwell time was later included to find the effect on the predictive validity.
Results: The results show that a larger time and distance gap to the upcoming vehicle relate to a higher merging probability. For larger time gaps to the on-ramp, the probability of merging was found to be smaller. It was also found that time gaps to the end of the on-ramp significantly relate to response times, with an increase of 55ms per 1s. Larger time gaps to the upcoming vehicle significantly relates to larger response times, with an increase of 64ms per 1s. No significant relation was found between response time and distance gaps to the upcoming vehicle. The response time was found to be 0.60s longer for rejected gap decisions. The time gap to the end of the on-ramp significantly relates to dwell time, with an increase of 0.56% per 1s. The distance gap to the upcoming vehicle significantly relates to dwell time, with an increase of 0.60% per 10m. The time gap to the upcoming vehicle significantly relates to dwell time, with an increase of 0.52% per 1s. The presented results show as well that a significant relation exists between gaze behaviour and decision outcomes and response times. When analysing decision outcomes and response times, the interaction between dwell time and gap sizes should be taken into account. This improved the predictive validity of the used regression models.
Conclusion: Several pieces of evidence suggest that gaze behaviour assist in understanding the human decision making process during merging. This study can serve as a basis for cognitive models that can investigate how the relation between gaze behaviour and gap sizes, decision outcomes and response times can help to understand and potentially predict gap acceptance decisions.