Pith. sign in

REVIEW 3 cited by

EDGAR: An Autonomous Driving Research Platform -- From Feature Development to Real-World Application

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2309.15492 v2 pith:HKHXC3AO submitted 2023-09-27 cs.RO

Phillip Karle , Tobias Betz , Marcin Bosk , Felix Fent , Nils Gehrke , Maximilian Geisslinger , Luis Gressenbuch , Philipp Hafemann
show 26 more authors
Sebastian Huber Maximilian Hübner Sebastian Huch Gemb Kaljavesi Tobias Kerbl Dominik Kulmer Tobias Mascetta Sebastian Maierhofer Florian Pfab Filip Rezabek Esteban Rivera Simon Sagmeister Leander Seidlitz Florian Sauerbeck Ilir Tahiraj Rainer Trauth Nico Uhlemann Gerald Würsching Baha Zarrouki Matthias Althoff Johannes Betz Klaus Bengler Georg Carle Frank Diermeyer Jörg Ott Markus Lienkamp
This is my paper · ORCID
classification cs.RO
keywords developmentautonomousresearchsoftwaredigitaledgarstackstwin
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

While current research and development of autonomous driving primarily focuses on developing new features and algorithms, the transfer from isolated software components into an entire software stack has been covered sparsely. Besides that, due to the complexity of autonomous software stacks and public road traffic, the optimal validation of entire stacks is an open research problem. Our paper targets these two aspects. We present our autonomous research vehicle EDGAR and its digital twin, a detailed virtual duplication of the vehicle. While the vehicle's setup is closely related to the state of the art, its virtual duplication is a valuable contribution as it is crucial for a consistent validation process from simulation to real-world tests. In addition, different development teams can work with the same model, making integration and testing of the software stacks much easier, significantly accelerating the development process. The real and virtual vehicles are embedded in a comprehensive development environment, which is also introduced. All parameters of the digital twin are provided open-source at https://github.com/TUMFTM/edgar_digital_twin.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. The Fidelity and Feedback Traps: The Case for Health Digital Twins as Modular Evolving Causal Systems

    stat.OT 2026-07 conditional novelty 5.5 of 10

    Health digital twins should be modular, causally valid, evolving systems judged by decision support under feedback, not by fidelity to observed trajectories.

  2. Multi-LiCa: A Motion and Targetless Multi LiDAR-to-LiDAR Calibration Framework

    cs.RO 2025-01 conditional novelty 5.0 of 10

    Multi-LiCa calibrates multiple LiDARs automatically from static scenes, with no targets or initial pose guess, using feature matching plus iterative scan merging to bridge non-overlapping fields of view.

  3. karl. -- A Research Vehicle for Automated and Connected Driving

    cs.AR 2026-02 conditional novelty 4.0 of 10

    A detailed hardware and system design of karl., an L4-capable research vehicle built on a VW T7 Multivan, with measurements of synchronization, latency, power, and vehicle control.

Pith tools