To tackle this challenge, we propose Hierarchical Vector Transformer (HiVT) for fast and accurate multi-agent motion prediction.
Ranked #13 on Motion Forecasting on Argoverse CVPR 2020
Car-following (CF) modeling, an essential component in simulating human CF behaviors, has attracted increasing research interest in the past decades.
In our study, we use real data sets and the state-of-the-art machine learning model to evaluate our attack detection scheme and the results confirm the effectiveness of our detection method.
Combinatorial optimization problems (COPs) on the graph with real-life applications are canonical challenges in Computer Science.
In this paper, we investigate the impact of two primary types of adversarial attacks, perturbation attacks and patch attacks, on the driving safety of vision-based autonomous vehicles rather than the detection precision of deep learning models.
This work presents a differentiable cloth simulator whose additional gradient information facilitates cloth-related applications.
Neural Machine Translation (NMT) has achieved significant breakthrough in performance but is known to suffer vulnerability to input perturbations.
The computational design of soft underwater swimmers is challenging because of the high degrees of freedom in soft-body modeling.
Inspired by Projective Dynamics (PD), we present Differentiable Projective Dynamics (DiffPD), an efficient differentiable soft-body simulator based on PD with implicit time integration.
Over the last decade, two competing control strategies have emerged for solving complex control tasks with high efficacy.