Use Case 2
AI extended Situational Awareness / Understanding
UC-2 AI extended Situational Awareness / Understanding
Rationale
Rationale
Demonstrator 1
Demonstrator 1
Collision prediction with Hybrid AI data fusion models
Aim: Test robustness and trustworthiness in difficult perception situations such as occlusion and bad lighting/weather conditions, with specific predictive collision detection situations.
Approach towards trustworthy AI:
- Implementing data fusion for robustness against occlusions – handle continuity / time consistency (hybrid AI models of Work Package 3)
- Training on data with bad lighting/bad weather and other edge-cases (real and synthetic data from Work Package 2 activities)
Demonstrator 2
Demonstrator 2
Robust Prediction modules for Robo-taxi in urban environment
Aim: Trustworthy and robust prediction of traffic participant’s indented motion enabling a safe and predictable operation of robo-taxis.
Approach towards trustworthy AI:
- Optimal combination of AI and physics-based approaches enabling large-prediction horizons together with computational efficient implementations
- Validation for trustworthiness of AI through the usage of XiL testing methodologies together with test data collection and analysis
- Seamless transition from virtual and simulation environments to the embedded, real-time platforms using container-based development approach
- Safety SW platform capable of embedded real-time container execution and fault-tolerant decision-making subsystem concept
Understanding
– AI extended situation awareness and understanding
AI needs to understand communications, map information and perception layers. This includes understanding local dynamic maps (LDM) with accurate and complete knowledge about the scene.