6 accepted papers
In many different fields interactions between objects play a critical role in determining their behavior. Graph neural networks (GNNs) have emerged as a powerful tool for modeling interactions, although often at the cost of adding considerable complexity and latency. In this paper, we consider the p…
In this work, we present LaserFlow, an efficient method for 3D object detection and motion forecasting from LiDAR. Unlike the previous work, our approach utilizes the native range view representation of the LiDAR, which enables our method to operate at the full range of the sensor in real-time witho
Robust real-time detection and motion forecasting of traffic participants is necessary for autonomous vehicles to safely navigate urban environments. In this paper, we present RV-FuseNet, a novel end-to-end approach for joint detection and trajectory estimation directly from time-series LiDAR data.…
A commonly-used representation for motion prediction of actors is a sequence of waypoints (comprising positions and orientations) for each actor at discrete future time-points. While regressing waypoints is simple and flexible, it can exhibit unrealistic higher-order derivatives (such as acceleratio…
In this paper, we present LiRaNet, a novel end-to-end trajectory prediction method which utilizes radar sensor information along with widely used lidar and HD maps. Automotive radar provides rich, complementary information, allowing for longer range vehicle detection as well as instantaneous radial
In this paper, we present LaserNet, a computationally efficient method for 3D object detection from LiDAR data for autonomous driving. The efficiency results from processing LiDAR data in the native range view of the sensor, where the input data is naturally compact. Operating in the range view invo…