40 citations · 53 across the 8 of their papers we have counts for
6 papers · 1 filter
Using Collision Momentum in Deep Reinforcement Learning Based Adversarial Pedestrian Modeling
Dianwei Chen, Ekim Yurtsever, Keith Redmill +1
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 i…
A Formal Safety Characterization of Advanced Driver Assist Systems in the Car-Following Regime with Scenario-Sampling
Bowen Weng, Minghao Zhu, Keith Redmill
The capability to follow a lead-vehicle and avoid rear-end collisions is one of the most important functionalities for human drivers and various Advanced Driver Assist Systems (ADA…
Pedestrian Emergence Estimation and Occlusion-Aware Risk Assessment for Urban Autonomous Driving
Mert Koc, Ekim Yurtsever, Keith Redmill +1
Avoiding unseen or partially occluded vulnerable road users (VRUs) is a major challenge for fully autonomous driving in urban scenes. However, occlusion-aware risk assessment syste…
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…
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…