Jacob Langner
Center for Information Technology
19 Papers
9 Citations
Jacob Langner is an academic researcher from Center for Information Technology. The author has contributed to research in topics: Computer science & Autoencoder. The author has an hindex of 4, co-authored 13 publications.
Chat about Author
Papers
Estimating the Uniqueness of Test Scenarios derived from Recorded Real-World-Driving-Data using Autoencoders
Jacob Langner,Johannes Bach,Lennart Ries,Stefan Otten,Marc Holzapfel,Eric Sax +5 more
- 26 Jun 2018
TL;DR: An automated selection algorithm for test scenarios based on relevant environmental parameters is presented, and the achieved testset reduction and thereby the saving potential in simulation time is demonstrated on a dataset including several thousand test kilometers.
56
Data-driven development, a complementing approach for automotive systems engineering
Johannes Bach,Jacob Langner,Stefan Otten,Marc Holzapfel,Eric Sax +4 more
- 01 Oct 2017
TL;DR: This paper reflects the current practice on the example of the Automotive SPICE process reference for system and software development in the automotive domain and contemplates on opportunities of consistent usage of recorded vehicle data throughout all phases of automotive development.
30
Logical Scenario Derivation by Clustering Dynamic-Length-Segments Extracted from Real-World-Driving-Data
Jacob Langner,Hannes Grolig,Stefan Otten,Marc Holzapfel,Eric Sax +4 more
- 03 May 2019
TL;DR: This work identifies the need to extract scenarios including the static environment from recorded real-world-driving-data, and presents an approach, that solves the problem to extract dynamic-length-segments containing a single scenario.
22
A Driving Scenario Representation for Scalable Real-Data Analytics with Neural Networks
Lennart Ries,Jacob Langner,Stefan Otten,Johannes Bach,Eric Sax +4 more
- 09 Jun 2019
TL;DR: In this paper, a representation of a scenario is defined as a top-view grid, comprising the dynamic objects and the static environment, thereby allowing a consistent interpretation of all relevant aspects of a driving scenario.
14
Leveraging Regular Expressions for Flexible Scenario Detection in Recorded Driving Data
Philip Elspas,Jacob Langner,Michael Aydinbas,Johannes Bach,Eric Sax +4 more
- 12 Oct 2020
TL;DR: A framework as a general and modular processing pipeline to tackle common challenges of real world data is suggested and regular expressions are used as a deterministic method to identify semantically meaningful sequences.
12