40 citations · 53 across the 10 of their papers we have counts for
4 papers · 1 filter
An online evolving framework for advancing reinforcement-learning based automated vehicle control
Teawon Han, Subramanya Nageshrao, Dimitar P. Filev +1
In this paper, an online evolving framework is proposed to detect and revise a controller's imperfect decision-making in advance. The framework consists of three modules: the evolv…
Optical Flow based Visual Potential Field for Autonomous Driving
Linda Capito, Keith Redmill, Umit Ozguner
Monocular vision-based navigation for automated driving is a challenging task due to the lack of enough information to compute temporal relationships among objects on the road. Opt…
A Multi-State Social Force Based Framework for Vehicle-Pedestrian Interaction in Uncontrolled Pedestrian Crossing Scenarios
Dongfang Yang, Keith Redmill, Umit Ozguner
Vehicle-pedestrian interaction (VPI) is one of the most challenging tasks for automated driving systems. The design of driving strategies for such systems usually starts with verif…
Integrating Deep Reinforcement Learning with Model-based Path Planners for Automated Driving
Ekim Yurtsever, Linda Capito, Keith Redmill +1
Automated driving in urban settings is challenging. Human participant behavior is difficult to model, and conventional, rule-based Automated Driving Systems (ADSs) tend to fail whe…