The development of trustworthy AI systems is essential for understanding object perception, sensor data exploitation, redundancy, fusion, and discrepancy resolution.
The AITHENA Use Case 1: Trustworthy Perception Systems for CCAM aims to address these critical aspects to promote reliable and explainable pedestrian detection in urban environments for CCAM applications.
- The primary objective of this initiative is to facilitate the deployment of a pedestrian detection system in a use case demonstrator. This aims to improve safety measures, especially in scenarios where pedestrian detection is within the safety critical path.
This use case aims to demonstrate a multi-faceted approach by integrating model-driven, data-driven, and sensor-driven methodologies. This comprehensive strategy ensures the reliability, explainability and transparency of the pedestrian detection system and associated AI functionalities across the software, sensor, and AI stack.