Cyclist Interaction with Automated Vehicles – CI-AV

Cyclists pose incredible challenges for automated driving because their movements are hard to predict, and they may suffer severe injuries even from a low-speed collision with a motorized vehicle. To avoid collisions, cyclists and drivers negotiate intersections by predicting and showing each other’s intents. External interfaces may help automated vehicles showing their own intent but, unfortunately, will not help automation to predict human intent. Of course, a cyclist may keep away from an automated vehicle that shows its intent to pass first; however, this lack of interaction may not be safe nor acceptable. The alternative, automation always leaving the way to cyclists, is not acceptable (for the passengers in the automated vehicles) and is not necessarily safer either. The solution is teaching automation to predict human intent. This prediction can be done with interaction models, e.g. algorithms that describe how road-users influence each other intentions. Today, these models are still in their infancy, and their safety impact hard to prove because they are yet to be integrated into the safety assessment of the automated vehicle.

In this project, we will integrate interaction models into tools for the virtual safety assessment of automated vehicles, to prove the value of these models for transport safety. We will also use the simulations to derive the communication requirements for cooperative applications that may help the automated vehicle to have enough information about the environment to run the interaction models. To develop interaction models and their virtual safety assessment, we need real-world data. Therefore, this project will also support further development of a robot bicycle at E2 that will enable repeatable interaction experiments to collect the data that we need to develop and validate our interaction models and our tools for virtual safety assessment.