Ad-datasets: a meta-collection of data sets for autonomous driving

Bogdoll, Daniel; Schreyer, Felix; Zöllner, J. Marius

doi:10.48550/arxiv.2202.01909

Cited by 1 publication

(1 citation statement)

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“…Contrary to sensor fusion approaches, we do not perform simultaneous detection on lidar and camera data. As there is no public anomaly dataset available that consists of more than one sensor modality [15], we use the Waymo Open Perception Dataset [10] for evaluation. To this point, only qualitative evaluation is possible, since we do not have ground truth data available for classes other than vehicles, pedestrians, cyclists, and signs [16].…”

Section: Introductionmentioning

confidence: 99%

Multimodal Detection of Unknown Objects on Roads for Autonomous Driving

Bogdoll¹,

Enrico²,

Nitsche³

et al. 2022

Preprint

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Tremendous progress in deep learning over the last years has led towards a future with autonomous vehicles on our roads. Nevertheless, the performance of their perception systems is strongly dependent on the quality of the utilized training data. As these usually only cover a fraction of all object classes an autonomous driving system will face, such systems struggle with handling the unexpected. In order to safely operate on public roads, the identification of objects from unknown classes remains a crucial task. In this paper, we propose a novel pipeline to detect unknown objects. Instead of focusing on a single sensor modality, we make use of lidar and camera data by combining state-of-the art detection models in a sequential manner. We evaluate our approach on the Waymo Open Perception Dataset and point out current research gaps in anomaly detection.

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Section: Introductionmentioning

confidence: 99%