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.
CoCar NextGen: a Multi-Purpose Platform for Connected Autonomous Driving Research
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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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A Data-Driven Novelty Score for Diverse In-Vehicle Data Recording
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.