Search Results for author: Alexander Carballo

Found 9 papers, 4 papers with code

DRUformer: Enhancing the driving scene Important object detection with driving relationship self-understanding

no code implementations11 Nov 2023 Yingjie Niu, Ming Ding, Keisuke Fujii, Kento Ohtani, Alexander Carballo, Kazuya Takeda

The DRUformer is a transformer-based multi-modal important object detection model that takes into account the relationships between all the participants in the driving scenario.

object-detection Object Detection

Predictive World Models from Real-World Partial Observations

1 code implementation12 Jan 2023 Robin Karlsson, Alexander Carballo, Keisuke Fujii, Kento Ohtani, Kazuya Takeda

By extending HVAEs to cases where complete ground truth states do not exist, we facilitate continual learning of spatial prediction as a step towards realizing explainable and comprehensive predictive world models for real-world mobile robotics applications.

Continual Learning Open-Ended Question Answering +1

ViCE: Improving Dense Representation Learning by Superpixelization and Contrasting Cluster Assignment

1 code implementation24 Nov 2021 Robin Karlsson, Tomoki Hayashi, Keisuke Fujii, Alexander Carballo, Kento Ohtani, Kazuya Takeda

Recent self-supervised models have demonstrated equal or better performance than supervised methods, opening for AI systems to learn visual representations from practically unlimited data.

Contrastive Learning Domain Generalization +4

Characterization of Multiple 3D LiDARs for Localization and Mapping using Normal Distributions Transform

no code implementations3 Apr 2020 Alexander Carballo, Abraham Monrroy, David Wong, Patiphon Narksri, Jacob Lambert, Yuki Kitsukawa, Eijiro Takeuchi, Shinpei Kato, Kazuya Takeda

In this work, we present a detailed comparison of ten different 3D LiDAR sensors, covering a range of manufacturers, models, and laser configurations, for the tasks of mapping and vehicle localization, using as common reference the Normal Distributions Transform (NDT) algorithm implemented in the self-driving open source platform Autoware.

Benchmarking

LIBRE: The Multiple 3D LiDAR Dataset

no code implementations13 Mar 2020 Alexander Carballo, Jacob Lambert, Abraham Monrroy-Cano, David Robert Wong, Patiphon Narksri, Yuki Kitsukawa, Eijiro Takeuchi, Shinpei Kato, Kazuya Takeda

In this work, we present LIBRE: LiDAR Benchmarking and Reference, a first-of-its-kind dataset featuring 10 different LiDAR sensors, covering a range of manufacturers, models, and laser configurations.

Benchmarking

A Survey of Autonomous Driving: Common Practices and Emerging Technologies

no code implementations12 Jun 2019 Ekim Yurtsever, Jacob Lambert, Alexander Carballo, Kazuya Takeda

Automated driving systems (ADSs) promise a safe, comfortable and efficient driving experience.

Robotics Systems and Control Systems and Control

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