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CoCar NextGen: a Multi-Purpose Platform for Connected Autonomous Driving Research

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arxiv 2404.17550 v1 pith:DOY36FQ3 submitted 2024-04-26 cs.RO

Marc Heinrich , Maximilian Zipfl , Marc Uecker , Sven Ochs , Martin Gontscharow , Tobias Fleck , Jens Doll , Philip Schörner
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Christian Hubschneider Marc René Zofka Alexander Viehl J. Marius Zöllner
This is my paper
classification cs.RO
keywords drivingplatformautomatedbuildcocarnextgenresearchsensor
verification ladder T0 review T1 audit T2 compute T3 formal
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Real world testing is of vital importance to the success of automated driving. While many players in the business design purpose build testing vehicles, we designed and build a modular platform that offers high flexibility for any kind of scenario. CoCar NextGen is equipped with next generation hardware that addresses all future use cases. Its extensive, redundant sensor setup allows to develop cross-domain data driven approaches that manage the transfer to other sensor setups. Together with the possibility of being deployed on public roads, this creates a unique research platform that supports the road to automated driving on SAE Level 5.

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Cited by 2 Pith papers

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

  1. Functionality Assessment Framework for Autonomous Driving Systems using Subjective Networks

    cs.RO 2025-06 conditional novelty 5.0 of 10

    Presents a graph-based framework using subjective logic to combine individual component assessments into an overall functionality statement for autonomous driving software.

  2. A Data-Driven Novelty Score for Diverse In-Vehicle Data Recording

    cs.CV 2025-07 conditional novelty 4.0 of 10

    An online Mahalanobis-distance novelty filter, updated with streaming data, selects a smaller traffic-sign training set that can outperform the full dataset and random sampling.

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