Contributions to Proceedings:

J. Kuen, C. Schartmüller, P. Wintersberger:
"The TOR Agent: Optimizing Driver Take-Over with Reinforcement Learning";
in: "13th International Conference on Automotive User Interfaces and Interac-tive Vehicular Applications (AutomotiveUI ´21)", issued by: ACM; ACM, Leeds, UK, 2021, 6 pages.

English abstract:
Various factors influence drivers´ response to Take-Over Requests in
automated driving, and a wide range of designs have been proposed
to improve transitions. Still, little research has investigated how
systems could deliver Take-Over Requests at the best moment in
time. In this paper, we sketch the idea of a reinforcement learning
agent that learns to deliver Take-Over Requests at the right time
so that drivers´ performance gets optimized, which could help to
increase driving safety. We implemented such a system in Unity
to evaluate this approach using a simple driver model. Our agent
receives coordinates of the upcoming road segment and learns to
deliver a Take-Over Request at an appropriate moment within a
short time window. The reward function is composed to minimize
the lateral deviation in the subsequent phase of manual driving.
The initial results obtained are promising, and we will evaluate the
concept with real human users soon.

take-over requests; automated driving; conditional automation; intelligent user interfaces; adaptive automation

Electronic version of the publication:

Created from the Publication Database of the Vienna University of Technology.