In the context of the AITHENA project, when it comes to making AI more trustworthy and human-centric, data management and privacy are at the heart of the issue. In Work Package 2 we have 3 different tasks that deal with the whole data lifecycle management and data...
Automated vehicles (AVs) currently behave differently from human-driven vehicles. This different behaviour has an impact on traffic dynamics. To understand the extent of this impact, it is necessary to study the behaviour of AVs at the traffic level. In order to...
The AITHENA Use Case 3: Trustworthy and Human understandable Decision-making is related to the task of vehicle guidance in an automated driving software stack. The local environment is perceived by the vehicle’s sensors and perception algorithms, and the...
In the AITHENA Use Case 2: AI extended Situational Awareness/Understanding information from perception layers, communications and map information is fused into Local Dynamic Maps (LDM) to link layers (from static to dynamic) to achieve accurate and complete knowledge...
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...
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