no code implementations • 13 Jun 2023 • Dianwei Chen, Ekim Yurtsever, Keith Redmill, Umit Ozguner
Recent research in pedestrian simulation often aims to develop realistic behaviors in various situations, but it is challenging for existing algorithms to generate behaviors that identify weaknesses in automated vehicles' performance in extreme and unlikely scenarios and edge cases.
no code implementations • 6 Jul 2021 • Mert Koc, Ekim Yurtsever, Keith Redmill, Umit Ozguner
Here, we propose a pedestrian emergence estimation and occlusion-aware risk assessment system for urban autonomous driving.
no code implementations • 15 Jun 2020 • Teawon Han, Subramanya Nageshrao, Dimitar P. Filev, Umit Ozguner
With the latest stochastic model and given criteria, the action-reviser module checks validity of the controller's chosen action by predicting future states.
1 code implementation • 2 Feb 2020 • Ekim Yurtsever, Linda Capito, Keith Redmill, Umit Ozguner
Automated driving in urban settings is challenging.
no code implementations • 28 Aug 2019 • Teawon Han, Dimitar Filev, Umit Ozguner
Within the framework, the evolving Finite State Machine (e-FSM), which is an online model able to (1) determine states uniquely as needed, (2) recognize states, and (3) identify state-transitions, is introduced.