DISRUPT - Decentralized Intelligent System for Road User Prediction and Tracking

We present DISRUPT, a research project to develop a cooperative traffic perception and prediction system based on networked infrastructure and vehicle sensors. Decentralized tracking and prediction algorithms are used to estimate the dynamic state of road users and predict their state in the near future. Compared to centralized approaches, which currently dominate traffic perception, decentralized algorithms offer advantages such as greater flexibility, robustness and scalability. Mobile sensor boxes are used as infrastructure sensors and the locally calculated state estimates are communicated in such a way that they can augment local estimates from other sensor boxes and/or vehicles. In addition, the information is transferred to a cloud that collects the local estimates and provides traffic visualization functionalities. The prediction module then calculates the future dynamic state based on neurocognitive behavior models and a measure of a road user's risk of being involved in dangerous situations. Based on this measure, alerts are generated and transmitted to road users equipped with an accident prevention app. An important component of DISRUPT is the development of a digital twin for testing and optimizing the overall system and its individual components. The main feature of the digital twin is the simulation of a photorealistic virtual copy of the test field environment. This enables the simulation of radar, infrared and conventional visible light cameras, which are combined with simulated data transmission delays to replicate the real system as accurately as possible. The plug-and-play design of the digital twin, together with a toolset for running and analyzing numerous simulations, enables efficient and thorough testing of the tracking and prediction algorithms. In particular, the digital twin enables the generation of hazard scenarios that are very unlikely to be observed in everyday traffic.