Objective 1: Components – Enhanced reliability and performance, cost and power of FUSION components

25% less data communication required compared to state-of-the-art
30% less false-positive detections compared to separate sensing approach
Fail-operational sensor compound vs. fail silent individual sensing approaches
Power reduction of 25% through semiconductor material improvements and functional convergence in sensor modules
Up to 30% cost reduction and 10% margin improvement for perception sub-systems

Objective 2: System – Dependable embedded control by co-integration of signal processing and AI approaches for FUSION

Fail-operational system approach for ADF (SAE Level 3+) vs. fail silent advanced driver assistance system (ADAS) approaches (SAE Levels up to 2)
Proposed Certification Approach for AI based sensor fusion, diagnostic, and control

Objective 3: Architecture – Dependable embedded control by cointegration of signal processing and AI approaches for FUSION

Fail-operational system architecture demonstrator for ADF (SAE Level 3+) vs. fail silent ADAS approaches (SAE Levels to 2)
LiDAR / RADAR sensor compound demonstrator

Objective 4: Automated Driving – Fail-operational systems for urban and rural environments based on FUSION

Demonstrators for fail-operational SAE Level 3+ ADFs in urban environments

Objective 5: Competitive advantage for European industry

Increasing market share and revenue of European companies through PRYSTINE’s ground-breaking technological advancements (O1-O4)

Objective 6: Increased user acceptance of automated driving functions