Search Results for author:
Found 2 papers, 0 papers with code
Considering Human Factors in Risk Maps for Robust and Foresighted Driver Warning
In this paper, we therefore propose a warning system that uses human states in the form of driver errors and can warn users in some cases of upcoming risks several seconds earlier than the state of the art systems not considering human factors.
Importance Filtering with Risk Models for Complex Driving Situations
Based on the results, we can further derive a novel filter architecture with multiple filter steps, for which risk models are recommended for each step, to further improve the robustness.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
