Data-Driven Development for Autonomous Vehicles

This technical report discusses the challenges and methodologies associated with data-driven development in the context of autonomous vehicles. It outlines the transition from advanced driver assistance systems to fully automated vehicles and identifies key hurdles such as regulatory compliance, customer acceptance, and the complexity of development processes. The report emphasizes the necessity of integrating real and synthetic data to enhance the training and validation of autonomous driving systems. It details the importance of adapting existing development processes to a data-centric approach, highlighting the need for continuous integration and deployment practices. The document also presents the concept of a data and AI loop, which is essential for managing the vast amounts of data generated by autonomous vehicles. Furthermore, it stresses the significance of automation in testing and development processes to ensure safety and efficiency in vehicle operation. The report concludes by urging automotive players to restructure their development toolchains to meet future demands.