Use Case 1
Trustworthy Perception Systems for CCAM
UC-1 Trustworthy Perception Systems for CCAM
Rationale
Rationale
Perception components receive raw data from sensors (e.g., cameras, LIDAR, ultrasonics), and apply AI methods to extract objective information (e.g., presence, distance and classes of surrounding objects, driver status, etc.). Trusted AI is needed to understand which objects (classes) are perceived, how different sensor information is used (redundancy, fusion), and how possible discrepancies are solved.
Objectives
Objectives
Enable use in safety-critical applications where pedestrian detection is in the safety-critical path. Enabling the system to be used in an open context.
Demonstrator
Demonstrator
Reliable Pedestrian Detection in Urban Environments
Aim: Enable use in safety-critical applications where pedestrian detection is in the safety-critical path. Enabling the system to be used in an open context.
Approach towards trustworthy AI:
- Reliable pedestrian detection based on multiple sensor modalities (redundancy) and by using safety mechanisms. Time Coherent multisensor fusion
- Explainable Layers: Neural Network Intermediate Representations
- Visualization of conflicting perception and training on data with bad lighting/bad weather
Perception
– Trustworthy Perception Systems for CCAM
Trusted AI is needed to understand which objects are perceived? How different sensor information is used? How possible discrepancies are solved?
UC-1 Leader:
Partners involved:
