Use Case 4

AI based Traffic management

UC-4 AI based Traffic management ​
Objectives​
Objectives
To understand the causality between the AI behaviour of AVs and the network performance in terms of efficiency, safety, and sustainability.​
Define a benchmark/reference for desired/acceptable behaviour and/or minimal driving performance expectations.​
Analyse the response of AI-models to traffic management data, at strategic, tactical, and operational levels.
Rationale​
Rationale​
At transport-level, applications such as traffic management interact with AI-based systems running at vehicle-level. The capabilities and behaviour of these AI models need to be analysed in macroscopic scenarios with interaction with other AI and non-AI systems. Trustworthiness is gained by benchmarking “good behaviour” of AI models in transport-level contexts.​
Demonstrator
Demonstrator

AI Models and Traffic Management​

Aim: the capabilities of the AI models will be tested and evaluated in use cases particularly relevant for traffic management and road authorities. The decisions of AI models in response to the data are of special interest. To assess this, the behaviour of the vehicle will be benchmarked against what is considered good behaviour as defined by the rules of the road.​

Approach towards trustworthy AI:​

  • Prepare data sets with real traffic management data​
  • Real-world data will serve as a starting point, for example from deployed traffic systems, data from national access points, weather data and databases of road operators​
  • Define good behaviour patterns according to road rules​
  • Create causality, transparency and predictability indicators ​
Traffic
– AI based traffic management

AI models are analysed in macroscopic scenarios with interaction with other AI and non-AI systems. Trustworthiness is gained by benchmarking “good behaviour” of AI models in transport level contexts.

UC-4 Leader:
MAP | Transforming Mobility
Partners involved:
Ruprecht Consult