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dSPACE Strenghtens AI Expertise by Acquiring

Thanks to the leading Karlsruhe-based start-up, dSPACE can now offer its customers an enhanced product portfolio in the field of autonomous driving
dSPACE Strenghtens AI Expertise by Acquiring

dSPACE, the leading provider of solutions for the development of networked, autonomous, and electrically powered vehicles, acquires the start-up company Under the umbrella of the dSPACE group companies, will invest in the core tasks ‘artificial intelligence (AI) applications’ and ‘cloud-based tools’, further develop its existing products as an integral part of the dSPACE product range, and use the dSPACE global sales network to market its products and services.


“ is an AI technology leader with a focus on automated data analysis, data annotation, and extraction of simulation scenarios for autonomous vehicles. With these key technologies, we are strategically enhancing the dSPACE portfolio to offer our customers a unique, integrated development and test solution for autonomous driving,” says Martin Goetzeler, CEO of dSPACE.


“We see dSPACE as the ideal partner for growth as takes its next development step. Testing is the bottleneck in the development of autonomous driving, and dSPACE, as a leading development partner of the automotive industry, will give us a strong momentum with its expertise and network,” explains Marc Mengler, co-founder and CEO of


“Marc Mengler and I are looking forward to working with dSPACE to improve local customer service, accelerate our international growth, and further expand our global leadership position in solutions for providing training and validation data,” adds Philip Kessler, co-founder and CTO of


In the development and introduction of autonomous vehicles, it is crucial to detect the environment of the vehicle realistically and without faults. Other road users, traffic signs, lanes, the static roadside structures, and open spaces must be reliably identified.


For this purpose, self-learning (machine learning) algorithms, in particular deep neural networks (DNNs) based on artificial intelligence, are used in these autonomous vehicles. These algorithms must be trained and validated efficiently. Therefore, it is a requirement to analyze, annotate, and also anonymize a tremendous amount of recorded (camera, lidar, and radar) sensor data.


The quantity, quality, and diversity of this training and validation data determine the quality of the resulting DNNs. The annotation process, also called labeling, is required for classifying the objects as a reference for machine learning. Today, this process carried out manually, which is mostly time-consuming and does not always ensure the highest quality level. has proprietary expert knowledge that enables automating this process to the greatest extent possible. The company also uses self-learning algorithms to process high-quality training and validation data. The underlying key technology is also based on artificial intelligence and ensures efficient data analysis as well as precise data annotation, which guarantees high quality training data for AI-based driving algorithms. develops AI- and web-based tools for this area of application. The underlying know-how is also used to extract simulation scenarios from sensor data.


About dSPACE

dSPACE develops and distributes integrated hardware and software tools for developing and testing electronic control units. As a one-stop supplier, dSPACE is a sought-after partner and solution provider in many development areas of the automotive industry, from electromobility to vehicle networking to autonomous driving. The company’s customer base therefore includes virtually all major vehicle manufacturers and suppliers. dSPACE systems are also used in the aerospace and other industries. With approximately 1,700 employees worldwide, dSPACE is headquartered in Paderborn, Germany; has three project centers in Germany; and serves customers through regional dSPACE companies in the USA, the UK, France, Japan, China, and Croatia.


About has special expertise in the area of training and validation data, with which algorithms for autonomous driving can be efficiently trained and tested. For example, the company uses self-learning algorithms to process sensor data recorded during measurement runs. The underlying key technology is based on artificial intelligence and ensures an efficient and precise evaluation, which contributes to the high accuracy of the driving algorithms. was founded in 2017, has 45 employees, and is headquartered in Karlsruhe. For more Information, visit

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Tue. July 23rd, 2024

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