Detecting Intentions of Vulnerable Road Users Based on Collective
  Intelligence
  Vulnerable road users (VRUs, i.e. cyclists and pedestrians) will play an
important role in future traffic. To avoid accidents and achieve a highly
efficient traffic flow, it is important to detect VRUs and to predict their
intentions. In this article a holistic approach for detecting intentions of
VRUs by cooperative methods is presented. The intention detection consists of
basic movement primitive prediction, e.g. standing, moving, turning, and a
forecast of the future trajectory. Vehicles equipped with sensors, data
processing systems and communication abilities, referred to as intelligent
vehicles, acquire and maintain a local model of their surrounding traffic
environment, e.g. crossing cyclists. Heterogeneous, open sets of agents
(cooperating and interacting vehicles, infrastructure, e.g. cameras and laser
scanners, and VRUs equipped with smart devices and body-worn sensors) exchange
information forming a multi-modal sensor system with the goal to reliably and
robustly detect VRUs and their intentions under consideration of real time
requirements and uncertainties. The resulting model allows to extend the
perceptual horizon of the individual agent beyond their own sensory
capabilities, enabling a longer forecast horizon. Concealments,
implausibilities and inconsistencies are resolved by the collective
intelligence of cooperating agents. Novel techniques of signal processing and
modelling in combination with analytical and learning based approaches of
pattern and activity recognition are used for detection, as well as intention
prediction of VRUs. Cooperation, by means of probabilistic sensor and knowledge
fusion, takes place on the level of perception and intention recognition. Based
on the requirements of the cooperative approach for the communication a new
strategy for an ad hoc network is proposed.
