Situation assessment and semantic maneuver planning under consideration of uncertainties for cooperative vehicles in heterogeneous traffic scenarios

• Applicant: Professor Dr. J. Marius Zöllner, since 11/2019
• Subject Area: Traffic and Transport Systems, Intelligent and Automated Traffic ; Automation, Mechatronics, Control Systems, Intelligent Technical Systems, Robotics
• Term: from 2015 to 2023
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 273310216

The aim of the subsequent application “Situation assessment and semantic maneuver planning under consideration of uncertainties for cooperative vehicles in heterogeneous traffic scenarios” is the research of machine learning methods using real data for implicit cooperative situation assessment and implicit cooperative decision-making in heterogeneous scenarios. This should be done by establishing the necessary generalization with reference to reality in order to improve the anticipatory capabilities of automated vehicles and thus proactively avoid critical situations.While the capabilities of automated vehicles are constantly evolving, they still lack an essential component that will distinguish them from humans for the time being - the ability of (implicit) cooperation. Unlike today's automated vehicles, human drivers include the (subtle) actions and intentions of other drivers in their decisions, and are thus able to demand or offer cooperation even without explicit communication. Although many research projects have addressed cooperative driving, especially in recent years, the focus is on explicit cooperation. Since neither all vehicles will have the necessary technical solution in the foreseeable future to enable communication between vehicles and with the infrastructure, nor will algorithms be standardized to such an extent that communicated environmental information and behavioral decisions will be considered uniformly, automated vehicles should be able to cooperate with other vehicles even without communication.For this reason, concepts and methods are being researched that do not require explicit communication and can therefore be applied to traffic situations in the real world. Furthermore, the modeling of the problem allows an extension of the situation assessment and decision-making to heterogeneous scenarios with cyclists as well as pedestrians, which are specifically being researched.Since automated driving and especially implicit cooperation are highly complex, it is not practicable to completely model the corresponding understanding or behavior by experts. Instead, the focus is on machine learning methods to solve the challenges of assessing the situation and making decisions.Current machine learning approaches offer the possibility to generalize over a high-dimensional state space and thus reduce the complexity of understanding and decision making in different traffic situations.Although safe learning is a major challenge, these methods can be used to accelerate the convergence of model-driven approaches by using them as heuristics or as an initial solution.

• DFG Programme: Priority Programmes
• Subproject of: SPP 1835: Kooperativ interagierende Automobile
• Ehemaliger Antragsteller: Professor Dr.-Ing. Rüdiger Dillmann, until 11/2019