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Abstract-In this work, a framework for motion prediction of vehicles and safety assessment of traffic scenes is presented. The developed framework can be used for driver assistant systems as well as for autonomous driving applications. In order to assess the safety of the future trajectories of the vehicle, these systems require a prediction of the future motion of all traffic participants. As the traffic participants have a mutual influence on each other, the interaction of them is explicitly considered in this framework, which is inspired by an optimization problem. Taking the mutual influence of traffic participants into account, this framework differs from the existing approaches which consider the interaction only insufficiently, suffering reliability in real traffic scenes. For motion prediction, the collision probability of a vehicle performing a certain maneuver, is computed. Based on the safety evaluation and the assumption that drivers avoid collisions, the prediction is realized. Simulation scenarios and real-world results show the functionality.

Abstract-In this paper energy optimal solutions for the approach of red traffic lights are derived. As cars waste most of the fuel in city traffic and especially in queuing at traffic lights, the presented framework provides solutions to save fuel and to protect the environment. The solutions are obtained using the definition of spent physical work which has to be minimized. It covers both cases, that the time of switching of the traffic lights is known and that the time of switching can only be modeled as a stochastic process. For a known time of switching a continuous solution is derived using Pontryagin Minimum Principle; in the stochastic case a modified Bellmann equation is formulated. The latter is solved with dynamic programming techniques. The presented solutions can be used for autonomous driving as well as for driving assistant systems. Simulation results show the potential savings using the presented approach.

In this paper the states of inevitable collision for mobile robots are determined using reachable set theory. With this theory the safety for robotic platforms can be guaranteed, still allowing maximum flexibility for navigation. Making use of reachability analysis, limitations due to input sampling as in previous approaches are avoided. Using reachability analysis the obstacles are grown in the state space. The mathematical background is shown in this paper and an exemplary algorithm is given for static environments. This implementation can handle arbitrary environments with multiple obstacles and different high-dimensional linear and non-linear system dynamics including the car-like kinematic model. By means of experimental results in simulated environments, the validity of the proposed concept is shown.