Use Case 2

AI extended Situational Awareness / Understanding

UC-2 AI extended Situational Awareness / Understanding​
Rationale​
Rationale​
Information from perception layers, communications and map information is merged into Local Dynamic Maps (LDM), to interconnect layers (from static to dynamic) to reach accurate and complete knowledge about the scene. AI models can be used to learn to predict possible evolutions of the scene and predict collisions or other situations. Trusted AI is needed to understand what data was used to train the predictors, and which edge-cases are covered.​
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.

UC-2 Demonstrator 1 Leader:
vicomtech
Partners involved:
TUE / Einhoven
UC-2 Demonstrator 2 Leader:
Virtual Vehicle Research
Partners involved:
Siemens
Infineon
TTTechAuto
Continental