40 citations · 53 across the 10 of their papers we have counts for
11 papers
On the Generalizability of Motion Models for Road Users in Heterogeneous Shared Traffic Spaces
Fatema T. Johora, Dongfang Yang, Jörg P. Müller +1
Modeling mixed-traffic motion and interactions is crucial to assess safety, efficiency, and feasibility of future urban areas. The lack of traffic regulations, diverse transport mo…
Sub-Goal Social Force Model for Collective Pedestrian Motion Under Vehicle Influence
Dongfang Yang, Fatema T. Johora, Keith A. Redmill +2
In mixed traffic scenarios, a certain number of pedestrians might coexist in a small area while interacting with vehicles. In this situation, every pedestrian must simultaneously r…
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…
An Online Evolving Framework for Modeling the Safe Autonomous Vehicle Control System via Online Recognition of Latent Risks
Teawon Han, Dimitar Filev, Umit Ozguner
An online evolving framework is proposed to support modeling the safe Automated Vehicle (AV) control system by making the controller able to recognize unexpected situations and rea…
Driving Intention Recognition and Lane Change Prediction on the Highway
Teawon Han, Junbo Jing, Umit Ozguner
This paper proposes a framework to recognize driving intentions and to predict driving behaviors of lane changing on the highway by using externally sensable traffic data from the…
Combining Social Force Model with Model Predictive Control for Vehicle's Longitudinal Speed Regulation in Pedestrian-Dense Scenarios
Dongfang Yang, Ümit Özgüner
In pedestrian-dense traffic scenarios, an autonomous vehicle may have to safely drive through a crowd of pedestrians while the vehicle tries to keep the desired speed as much as po…
Top-view Trajectories: A Pedestrian Dataset of Vehicle-Crowd Interaction from Controlled Experiments and Crowded Campus
Dongfang Yang, Linhui Li, Keith Redmill +1
Predicting the collective motion of a group of pedestrians (a crowd) under the vehicle influence is essential for the development of autonomous vehicles to deal with mixed urban sc…
Model predictive trajectory optimization and tracking for on-road autonomous vehicles
Peng Liu, Brian Paden, Umit Ozguner
Motion planning for autonomous vehicles requires spatio-temporal motion plans (i.e. state trajectories) to account for dynamic obstacles. This requires a trajectory tracking contro…