Eitan Kosman
YOU?
Author Swipe
View article: Stochasticity in Motion: An Information-Theoretic Approach to Trajectory Prediction
Stochasticity in Motion: An Information-Theoretic Approach to Trajectory Prediction Open
In autonomous driving, accurate motion prediction is crucial for safe and efficient motion planning. To ensure safety, planners require reliable uncertainty estimates of the predicted behavior of surrounding agents, yet this aspect has rec…
View article: Motion Forecasting via Model-Based Risk Minimization
Motion Forecasting via Model-Based Risk Minimization Open
Forecasting the future trajectories of surrounding agents is crucial for autonomous vehicles to ensure safe, efficient, and comfortable route planning. While model ensembling has improved prediction accuracy in various fields, its applicat…
View article: Bosch Street Dataset: A Multi-Modal Dataset with Imaging Radar for Automated Driving
Bosch Street Dataset: A Multi-Modal Dataset with Imaging Radar for Automated Driving Open
This paper introduces the Bosch street dataset (BSD), a novel multi-modal large-scale dataset aimed at promoting highly automated driving (HAD) and advanced driver-assistance systems (ADAS) research. Unlike existing datasets, BSD offers a …
View article: Reviving Life on the Edge: Joint Score-Based Graph Generation of Rich Edge Attributes
Reviving Life on the Edge: Joint Score-Based Graph Generation of Rich Edge Attributes Open
Graph generation is integral to various engineering and scientific disciplines. Nevertheless, existing methodologies tend to overlook the generation of edge attributes. However, we identify critical applications where edge attributes are e…
View article: Can you text what is happening? Integrating pre-trained language encoders into trajectory prediction models for autonomous driving
Can you text what is happening? Integrating pre-trained language encoders into trajectory prediction models for autonomous driving Open
In autonomous driving tasks, scene understanding is the first step towards predicting the future behavior of the surrounding traffic participants. Yet, how to represent a given scene and extract its features are still open research questio…
View article: GraphVid: It Only Takes a Few Nodes to Understand a Video
GraphVid: It Only Takes a Few Nodes to Understand a Video Open
We propose a concise representation of videos that encode perceptually meaningful features into graphs. With this representation, we aim to leverage the large amount of redundancies in videos and save computations. First, we construct supe…
View article: Automatic Recognition of Oil Spills Using Neural Networks and Classic Image Processing
Automatic Recognition of Oil Spills Using Neural Networks and Classic Image Processing Open
Oil spill events are one of the major risks to marine and coastal ecosystems and, therefore, early detection is crucial for minimizing environmental contamination. Oil spill events have a unique appearance in satellite images created by Sy…
View article: LSP : Acceleration and Regularization of Graph Neural Networks via Locality Sensitive Pruning of Graphs
LSP : Acceleration and Regularization of Graph Neural Networks via Locality Sensitive Pruning of Graphs Open
Graph Neural Networks (GNNs) have emerged as highly successful tools for graph-related tasks. However, real-world problems involve very large graphs, and the compute resources needed to fit GNNs to those problems grow rapidly. Moreover, th…
View article: Vision-Guided Forecasting -- Visual Context for Multi-Horizon Time Series Forecasting
Vision-Guided Forecasting -- Visual Context for Multi-Horizon Time Series Forecasting Open
Autonomous driving gained huge traction in recent years, due to its potential to change the way we commute. Much effort has been put into trying to estimate the state of a vehicle. Meanwhile, learning to forecast the state of a vehicle ahe…
View article: Vision-Guided Forecasting -- Visual Context for Multi-Horizon Time\n Series Forecasting
Vision-Guided Forecasting -- Visual Context for Multi-Horizon Time\n Series Forecasting Open
Autonomous driving gained huge traction in recent years, due to its potential\nto change the way we commute. Much effort has been put into trying to estimate\nthe state of a vehicle. Meanwhile, learning to forecast the state of a vehicle\n…