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Nicolas du Lac, CEO, Intempora
Modern methods for developing, testing and validating complex software functions for advanced driver assistance systems (ADAS) and autonomous driving (AD), such as perception, positioning, and decision-making, require vast amounts of sensor data to be gathered. Once the data is collected, it must be post-processed for AI algorithm training and validation so that functions can be executed in real-time in the field.
In this free webinar, Intempora CEO Nicolas du Lac discusses how RTMaps software, and potentially other technologies from the dSPACE data-driven development toolchain, can help engineers across the various stages of the development and testing cycles – from data collection to “as-fast-as-possible” data replay, including real-time execution in vehicles.
Key topics and takeaways:
- Easy-to-use, modular and powerful software framework for speeding up complex AD software development and testing tasks
- Guidelines to optimize your IT infrastructure for training, testing and validation of algorithms against large amounts of data
- End-to-end toolchain for data-driven development
