Zobrazeno 1 - 10
of 42
pro vyhledávání: '"Berrio, Julie"'
Autonomous vehicles are being tested in diverse environments worldwide. However, a notable gap exists in evaluating datasets representing natural, unstructured environments such as forests or gardens. To address this, we present a study on localisati
Externí odkaz:
http://arxiv.org/abs/2411.16931
Autor:
Dao, Minh-Quan, Caesar, Holger, Berrio, Julie Stephany, Shan, Mao, Worrall, Stewart, Frémont, Vincent, Malis, Ezio
Occlusion presents a significant challenge for safety-critical applications such as autonomous driving. Collaborative perception has recently attracted a large research interest thanks to the ability to enhance the perception of autonomous vehicles v
Externí odkaz:
http://arxiv.org/abs/2404.06256
A comprehensive understanding of 3D scenes is crucial in autonomous vehicles (AVs), and recent models for 3D semantic occupancy prediction have successfully addressed the challenge of describing real-world objects with varied shapes and classes. Howe
Externí odkaz:
http://arxiv.org/abs/2403.01644
This paper introduces InverseMatrixVT3D, an efficient method for transforming multi-view image features into 3D feature volumes for 3D semantic occupancy prediction. Existing methods for constructing 3D volumes often rely on depth estimation, device-
Externí odkaz:
http://arxiv.org/abs/2401.12422
Despite the continual advances in Advanced Driver Assistance Systems (ADAS) and the development of high-level autonomous vehicles (AV), there is a general consensus that for the short to medium term, there is a requirement for a human supervisor to h
Externí odkaz:
http://arxiv.org/abs/2310.11608
Deploying 3D detectors in unfamiliar domains has been demonstrated to result in a significant 70-90% drop in detection rate due to variations in lidar, geography, or weather from their training dataset. This domain gap leads to missing detections for
Externí odkaz:
http://arxiv.org/abs/2308.05988
For smart vehicles driving through signalised intersections, it is crucial to determine whether the vehicle has right of way given the state of the traffic lights. To address this issue, camera based sensors can be used to determine whether the vehic
Externí odkaz:
http://arxiv.org/abs/2307.07196
Practical Collaborative Perception: A Framework for Asynchronous and Multi-Agent 3D Object Detection
Autor:
Dao, Minh-Quan, Berrio, Julie Stephany, Frémont, Vincent, Shan, Mao, Héry, Elwan, Worrall, Stewart
Occlusion is a major challenge for LiDAR-based object detection methods. This challenge becomes safety-critical in urban traffic where the ego vehicle must have reliable object detection to avoid collision while its field of view is severely reduced
Externí odkaz:
http://arxiv.org/abs/2307.01462
We introduce Multi-Source 3D (MS3D), a new self-training pipeline for unsupervised domain adaptation in 3D object detection. Despite the remarkable accuracy of 3D detectors, they often overfit to specific domain biases, leading to suboptimal performa
Externí odkaz:
http://arxiv.org/abs/2304.02431
Every autonomous driving dataset has a different configuration of sensors, originating from distinct geographic regions and covering various scenarios. As a result, 3D detectors tend to overfit the datasets they are trained on. This causes a drastic
Externí odkaz:
http://arxiv.org/abs/2209.06407