From partial and high automation to manual driving: Relationship between non-driving related tasks, drowsiness and take-over performance

•64 participants completed a simulated drive lasting 1 or 2 h using a partially (PAD) or highly automated driving system (HAD).•Drivers were allowed to bring and use their own smartphones, and they readily engaged in various NDRTs using them.•The duration of the drive did not influence take-over performance (TOP), neither during PAD nor during HAD.•In PAD, observed drowsiness and motivational appeal of NDRTs decreased TOP, while visual and mental workload increased it.•In HAD, observed drowsiness and NDRT engagement did not influence TOP. Until the level of full vehicle automation is reached, users of vehicle automation systems will be required to take over manual control of the vehicle occasionally and stay fallback-ready to some extent during the drive. Both, drowsiness caused by inactivity and the engagement in distracting non-driving related tasks (NDRTs) such as entertainment or office work have been suggested to impair the driver’s ability to safely handle these transitions of control. Thus, it is an open question whether engagement in NDRTs will impair or improve take-over performance. In a motion-based driving simulator, 64 participants completed an automated drive that lasted either one or two hours using either a partially or highly automated driving system. In the partially automated driving condition, a warning was issued after several seconds when drivers took both hands off the steering wheel, while the highly automated driving system allowed hands-off driving permanently. Drivers were allowed to bring along their smartphones and to use them during the drive. They engaged in a wide variety of NDRTs such as reading or using social media. At the end of the session, drivers had to react to a sudden lead vehicle braking event. In the partial automation condition, there was no take-over request (TOR) to notify the drivers of the braking vehicle, while in the highly automated condition, the situation happened right after the drivers had deactivated the automation in response to a TOR. The lead time of the TOR was set at 8 s. Driver’s level of drowsiness, workload (visual, mental and motoric) from carrying out the NDRT and motivational appeal of the NDRT right before the control transition were video-coded and used to predict the outcome of the braking event (i.e., reaction and system deactivation times, minimal Time-to-collision (TTC) and self-reported criticality) with a multiple regression approach. In the partial automation condition, reaction t