People’s understanding of external HMI and their experiences of interacting with an Automated Delivery Vehicle in a terminal context, RISE report 2022:133

This study investigated how the participants in a scenario of a terminal for goods handling perceived to interact with an automated delivery vehicle (ADV) for loading/unloading and how they understood the external Human Machine Interfaces (eHMI), i.e., visual signals on the ADV that communicate the ADV’s behaviour, mode and intentions. The objectives were (i) to test and validate the self-driving functions; (ii) to test and validate the control room functionalities and (iii) to evaluate how the participants understood the eHMI and (iv) to evaluate how they experienced to interact with the ADV in specific situations. The eHMI communicated four messages; Acceleration (from stand still), Deceleration (to stand still), Unplanned stop and Delivery mode. The participants were introduced to the scenario and instructed to act the role of being newly employed at a terminal for loading/unloading goods. There were two situations they were asked to handle: (i) the ADV had stopped (unplanned) due to an obstacle in front which had to be removed for the ADV to drive on, and (ii) to load/unload the ADV when it had stopped at a designated place for loading/unloading. The participants marked on a 5-point scale how easy/difficult it was to understand the different eHMI (eHMI communicated Acceleration from standstill, Deceleration to standstill, Unplanned stop and Load/Unload mode) and how safe/unsafe they felt to approach and interact with the ADV. The same procedures were repeated three times. The results showed that the participants thought it was easy to understand the different eHMI on the ADV, specifically the types of eHMI that are on vehicles today, such as hazard lights and turning indicators. The results also revealed that the context, i.e. terminal scenario, the situations, and the work tasks, was a contributing factor to their understanding of the eHMI. The participants’ previous and gained experiences also contributed to their understanding of the eHMI. The participants thought it was safe to approach the ADV, for example to remove the obstacle in front of the ADV, much because they assumed that such close interactions with the ADVs could happen often and, therefore, they assumed it was safe to interact closely to the ADV. The size of the ADV (smaller than a regular car) was also mentioned as a contributing factor. The self-driving functions in the ADV were integrated in the ADV’s system architecture. A challenge was to obtain stability in the system with repeated driving cycles. The Autonomous Transport Management System (ATMS) was put in a cloud service to enable remote testing, and to facilitate repeated integration tests as well as test-cycles with the ADV. The information in the messages sent to the ADV included, for example, the coordinates for the route and the control signals for the eHMI. In addition, a function was implemented to reset the ATMS easily when it entered a faulty state. The control of the LED lights for the eHMI was managed by the main Vehicle Control Unit (VCU) which provided more accurate output from the eHMI compared to using the vehicle data.

Söderman, M., Clasen, R., Bergström, G., Collings, W.
Research area
Road user behaviour
Publication type
Project report
Year of publication