Supporting Advanced Driving Assistance Systems Development and Validation - Pešek, Adam (2025)
This thesis addresses the challenges of processing and validating large-scale sensor data in the development of Advanced Driver Assistance Systems (ADAS). Modern ADAS applications rely on high-resolution data from cameras, LiDAR, RADAR, GPS, and vehicle bus systems, which must be extensively preprocessed to ensure quality, accuracy, and reliability. The main objective of the thesis is to design and implement a scalable solution for preprocessing, filtering, and visualizing ADAS data within the industrial environment of Valeo. The work includes the integration of tools for defining data processing workflows, automating their execution on a computing cluster, and building web-based applications for visual inspection of individual recordings (“microview”) and for metadata exploration and filtering across entire datasets (“macroview”). The methodological approach combines literature research, technical tool analysis, and practical implementation, supported by expert consultations. A significant part of the work focuses on workflow orchestration, data visualization, and building user-friendly interfaces for engineers working with complex ADAS data. The developed tools are evaluated through guided conversations and feedback sessions with stakeholders at Valeo to ensure usability and relevance. By combining theoretical insight with industrial application, this thesis contributes a modular framework that improves the efficiency, transparency, and scalability of ADAS data processing and validation workflows.