11 citations · 17 across the 3 of their papers we have counts for
6 papers
Improving Top-K Decoding for Non-Autoregressive Semantic Parsing via Intent Conditioning
Geunseob Oh, Rahul Goel, Chris Hidey +4
Semantic parsing (SP) is a core component of modern virtual assistants like Google Assistant and Amazon Alexa. While sequence-to-sequence-based auto-regressive (AR) approaches are…
CVAE-H: Conditionalizing Variational Autoencoders via Hypernetworks and Trajectory Forecasting for Autonomous Driving
Geunseob Oh, Huei Peng
The task of predicting stochastic behaviors of road agents in diverse environments is a challenging problem for autonomous driving. To best understand scene contexts and produce di…
A Data-driven, Falsification-based Model of Human Driver Behavior
Nauman Sohani, Geunseob Oh, Xinpeng Wang
We propose a novel framework to differentiate between vehicle trajectories originating from human and non-human drivers by constructing a data-driven boundary using parametric sign…
HCNAF: Hyper-Conditioned Neural Autoregressive Flow and its Application for Probabilistic Occupancy Map Forecasting
Geunseob Oh, Jean-Sebastien Valois
We introduce Hyper-Conditioned Neural Autoregressive Flow (HCNAF); a powerful universal distribution approximator designed to model arbitrarily complex conditional probability dens…
Impact of Traffic Lights on Trajectory Forecasting of Human-driven Vehicles Near Signalized Intersections
Geunseob Oh, Huei Peng
Forecasting trajectories of human-driven vehicles is a crucial problem in autonomous driving. Trajectory forecasting in the urban area is particularly hard due to complex interacti…
Vehicle Energy Dataset (VED), A Large-scale Dataset for Vehicle Energy Consumption Research
G. S. Oh, David J. Leblanc, Huei Peng
We present Vehicle Energy Dataset (VED), a novel large-scale dataset of fuel and energy data collected from 383 personal cars in Ann Arbor, Michigan, USA. This open dataset capture…