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