DeCoInt2

The DeCoInt2 project (Detecting Intention of Vulnerable Road Users based on Collective Intelligence as a Basis for Automated Driving) is concerned with intention detection of vulnerable road users (VRUs) in automated driving using cooperative technologies. Individual mobility will still be an important aspect of future traffic and automated driving will make a key contribution. It has the potential to increase safety as well as traffic flow and to decrease environmental pollution and resource consumption. Especially in urban areas, VRUs, e.g., pedestrians and cyclists, will still play an important role in the mixed traffic of tomorrow. For an accident-free and highly efficient traffic flow with automated vehicles it is not just important to perceive VRUs but it is also essential that their intentions are detected and analyzed in a similar way as humans do it when they drive and forecast VRU trajectories. The reliable and robust perception of VRUs and their intentions with a multi-modal sensor system (e.g., video cameras, laser scanners, accelerometers and gyroscopes in mobile devices) in real-time is a big challenge. Going far beyond existing work we follow a holistic, cooperative approach to forecast movements of humans (e.g., when will a standing cyclist start to move forward) and to forecast their trajectories (e.g., will she turn left). Heterogeneous, open sets of agents (collaboratively interacting vehicles, infrastructure, and VRUs themselves, if equipped with common mobile devices) exchange information to determine individual models of their surrounding environment which allow for an accurate forecast of VRU basic movements and trajectories. Occlusions, implausibility, and inconsistencies are resolved using the collective intelligence of cooperating agents.  We develop new methods by considering and combining novel signal processing and modeling techniques with machine learning based pattern recognition approaches. The cooperation of agents will be investigated on several levels including the VRU perception level, on the level of recognized trajectories, or on the level of already detected intentions. A communication strategy to exchange required information in ad hoc networks of cooperating partners will be proposed.  The techniques are evaluated with real data using a research vehicle, a research intersection with public traffic, and a number of mobile devices.