This study aimed to construct driver models for overtaking behavior using long short-term memory (LSTM). During the overtaking maneuver, an ego vehicle changes lanes to the overtaking lane while paying attention to both the preceding vehicle in the travel lane and the following vehicle in the overtaking lane and returns to the travel lane after overtaking the preceding vehicle in the travel lane. This scenario was segregated into four phases in this study: Car-Following, Lane-Change-1, Overtaking, and Lane-Change-2. In the Car-Following phase, the ego vehicle follows the preceding vehicle in the travel lane. Meanwhile, in the Lane-Change-1 phase, the ego vehicle changes from the travel lane to the overtaking lane. Overtaking is the phase in which the ego vehicle in the overtaking lane overtakes the preceding vehicle in the travel lane. Lane-Change-2 is the phase in which the ego vehicle changes lanes from the overtaking lane to the travel lane after overtaking the preceding vehicle in the travel lane. We analyzed and evaluated the accuracy of the models for the entire experimental scenario and for each model of Lane-Change-1, Overtaking and Lane-Change-2. The constructed models were also evaluated for safety in lane-change situations based on the WP.29 steering equipment regulation of the UNECE, which was defined as Scritical in the Automatically Commanded Steering Function (ACSF) Category C of R79 in WP.29 of UNECE. In the results, the Lane-Change-2 model was identified to be the most accurate of the four phases and the model accuracy was found to be due to epochs and learning rate. All the constructed models satisfied the WP.29 regulation of UNECE. The constructed models which reflected individual characteristics are expected to be applied to ADAS and automated driving technology that conduct overtaking maneuvers.
