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      University of Skövde, link to startpage

      Intention recognition for real-time automotive 3D situation awareness

      Research Group Skövde Artificial Intelligence Lab
      Resarch Environment Informatics

      Intention recognition for real-time automotive 3D situation awareness

      Research Group Skövde Artificial Intelligence Lab
      Resarch Environment Informatics

      Quick Facts

      Full project name

      IRRA - Intention recognition for real-time automotive 3D situation awareness

      Duration

      January 2019 – December 2022

      Partners

      Volvo Car Corporation (coordinator), the Swedish Transport Administration (Trafikverket), RISE SICS, Smart Eye AB

      Financing

      VINNOVA (Swedish Governmental Agency for Innovation Systems)

      Intention recognition is the task of inferring an agent´s intention based on its previous actions. It is crucial for human social intelligence which in turn enables understanding of, and for the ability to predict, other humans´ behaviours, such as for example other drivers´ intent to overtake, stop, turn, or switch lanes. For making situation-based decisions, both autonomous and human drivers need to take the intentions of surrounding vehicles into account. This is especially true in a mix of autonomous and human drivers.

      Expected results and effects

      Existing algorithms and models for intention recognition need to be improved w ith respect to accuracy, robustness, transparency and scalability, in order to meet the requirements of the Swedish automotive industry and Trafikverket. It is an open research question how to achieve this level of maturity. This lack of knowledge is a bottleneck for the automotive industry prohibiting the creation of novel advanced and intelligent automotive services and products based on social intelligence and intention recognition.

      Planned approach and implementation

      Selection of user cases based on important industrial and societal application areas for IR: drivers, vehicles, and system Improvement of existing algorithms for IR using state-ofthe- art in machine learning, computer vision, multi-agent system, automatic derivation, location and new sensor technology Evaluation of results is done through proof-of-concept implementation, vehicle-based tests, and through publication Knowledge transfer is ensured through an industrial doctoral student, workshops, and publications

      Project leader

      Senior Lecturer in Computer Science

      Participating Researchers

      Published: 1/9/2020
      Edited: 1/9/2020
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