Collaborative Perception Datasets in Autonomous Driving: A Survey

no code implementations • 22 Apr 2024 • Melih Yazgan, Mythra Varun Akkanapragada, J. Marius Zoellner

This survey offers a comprehensive examination of collaborative perception datasets in the context of Vehicle-to-Infrastructure (V2I), Vehicle-to-Vehicle (V2V), and Vehicle-to-Everything (V2X).

Plausibility Verification For 3D Object Detectors Using Energy-Based Optimization

no code implementations • 2 Nov 2022 • Abhishek Vivekanandan, Niels Maier, J. Marius Zoellner

Environmental perception obtained via object detectors have no predictable safety layer encoded into their model schema, which creates the question of trustworthiness about the system's prediction.

Autonomous Vehicles Object +2

Radar Artifact Labeling Framework (RALF): Method for Plausible Radar Detections in Datasets

no code implementations • 3 Dec 2020 • Simon T. Isele, Marcel P. Schilling, Fabian E. Klein, Sascha Saralajew, J. Marius Zoellner

RALF provides plausibility labels for radar raw detections, distinguishing between artifacts and targets.

Autonomous Driving Depth Image Estimation