activity
20172024
most citedSpatially-Aware Graph Neural Networks for Relational Behavior Forecasting from Sensor Data

51 citations · 54 across the 3 of their papers we have counts for

collaborators

5 papers

cs.AI20241 cited

Improving Agent Behaviors with RL Fine-tuning for Autonomous Driving

Zhenghao Peng, Wenjie Luo, Yiren Lu +4

A major challenge in autonomous vehicle research is modeling agent behaviors, which has critical applications including constructing realistic and reliable simulations for off-boar…

cs.CV2020

Implicit Latent Variable Model for Scene-Consistent Motion Forecasting

Sergio Casas, Cole Gulino, Simon Suo +3

In order to plan a safe maneuver an autonomous vehicle must accurately perceive its environment, and understand the interactions among traffic participants. In this paper, we aim t…

cs.CV2020

The Importance of Prior Knowledge in Precise Multimodal Prediction

Sergio Casas, Cole Gulino, Simon Suo +1

Roads have well defined geometries, topologies, and traffic rules. While this has been widely exploited in motion planning methods to produce maneuvers that obey the law, little wo…

cs.CV201951 cited

Spatially-Aware Graph Neural Networks for Relational Behavior Forecasting from Sensor Data

Sergio Casas, Cole Gulino, Renjie Liao +1

In this paper, we tackle the problem of relational behavior forecasting from sensor data. Towards this goal, we propose a novel spatially-aware graph neural network (SpAGNN) that m…

cs.RO20172 cited

Generalizing Informed Sampling for Asymptotically Optimal Sampling-based Kinodynamic Planning via Markov Chain Monte Carlo

Daqing Yi, Rohan Thakker, Cole Gulino +2

Asymptotically-optimal motion planners such as RRT* have been shown to incrementally approximate the shortest path between start and goal states. Once an initial solution is found,…