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Efficient behavior and trajectory planning is one of the major challenges for automated driving. Especially intersection scenarios are very demanding due to their complexity arising from the variety of maneuver possibilities and other traffic participants. A key challenge is to generate behaviors which optimize the comfort and progress of the ego vehicle but at the same time are not too aggressive towards other traffic participants. In order to maintain real time capability for courteous behavior and trajectory planning, an efficient formulation of the optimal control problem and corresponding solving algorithms are required. Consequently, a novel planning framework is presented which considers comfort and progress as well as the courtesy of actions in a graph-based behavior planning module. Utilizing the low level trajectory generation, the behavior result can be further optimized for driving comfort while satisfying constraints over the whole planning horizon. According experiments show the practicability and real time capability of the framework.

A main difficulty in autonomous driving is the assurance of maneuver acceptability by other traffic participants. Thus, knowledge about social interaction needs to be incorporated into the motion planning process. In this paper we present a model based framework to verify the acceptance of considered maneuvers and to plan social compliant motions. Therefore, we fuse two powerful approaches, one for decisionmaking and one for planning and show how the methods benefit from each other. Our method adheres to the classical structure of decision-making with subsequent trajectory planning and is consistent in the sense that both components are subject on the same, identical parametrized driver model. The overall method is real-time capable and the resulting trajectories adhere to kinematic constraints. Thus, the approach is applicable in realworld systems.

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Overtaking is a challenging task in the field of autonomous driving, especially on roads with an opposite lane and oncoming vehicles. Since trajectory planning is repeated cyclic it is highly important to trigger the maneuver only if it is guaranteed that collision-free trajectories that satisfy kinematic constraints exist at each planning step. The goal of this paper is to present an algorithm for planning overtaking trajectories on large temporal horizons in real-time. The main idea is as follows: once overtaking is desired by the behavior module an initial trajectory is simulated using a path tracking control algorithm for lane changing combined with a classical PI-controller for approaching the target speed. The controllers are parametrized in a way that the simulated trajectory will satisfy kinematic constraints. If no collisions are detected a corridor containing the simulated trajectory is created to state constraints for a subsequent optimal control problem to relax the trajectory and smooth it to be comfortable to the vehicle passengers.