text: Making the right decision in traffic is a challenging task that is highly dependent on individual preferences as well as the surrounding environment. Therefore it is hard to model solely based on expert knowledge. In this work we use Deep Reinforcement Learning to learn maneuver decisions based on a compact semantic state representation. This ensures a consistent model of the environment across scenarios as well as a behavior adaptation function, enabling on-line changes of desired behaviors without re-training. The input for the neural network is a simulated object list similar to that of Radar or Lidar sensors, superimposed by a relational semantic scene description. The state as well as the reward are extended by a behavior adaptation function and a parameterization respectively. With little expert knowledge and a set of mid-level actions, it can be seen that the agent is capable to adhere to traffic rules and learns to drive safely in a variety of situations.
  @cite_18: 
  @cite_34: This paper presents a novel cooperative-driving prediction and planning framework for dynamic environments based on the methods of game theory. The proposed algorithm can be used for highly automated driving on highways or as a sophisticated prediction module for advanced driver-assistance systems with no need for intervehicle communication. The main contribution of this paper is a model-based interaction-aware motion prediction of all vehicles in a scene. In contrast to other state-of-the-art approaches, the system also models the replanning capabilities of all drivers. With that, the driving strategy is able to capture complex interactions between vehicles, thus planning maneuver sequences over longer time horizons. It also enables an accurate prediction of traffic for the next immediate time step. The prediction model is supported by an interpretation of what other drivers intend to do, how they interact with traffic, and the ongoing observation. As part of the prediction loop, the proposed planning strategy incorporates the expected reactions of all traffic participants, offering cooperative and robust driving decisions. By means of experimental results under simulated highway scenarios, the validity of the proposed concept and its real-time capability is demonstrated.
  @cite_13: 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.
  @cite_33: Recently, automated driving has more and more been transformed from an exciting vision into hands on reality by prototypes. While drivers are used to assistance and maybe even automation for driving within a lane, it is exciting to dare a step ahead: Deciding and executing tactical maneuvers like lane changes in automated vehicles without any human interaction. In this paper, we present our approach for tactical behavior planning for lane changes. We present a way to tackle perception uncertainties and how to achieve provident, prediction-based behavior planning. For this, we introduce a novel framework to plan in high-dimensional, mixed-integer state spaces in real-time. Our approach is evaluated not only in simulation, but also in real traffic. The implementation has recently been demonstrated to the public in the Audi A7 piloted driving concept vehicle, driving from Stanford to the Consumer Electronics Show (CES) 2015 in Las Vegas.
summary: Due to the limitations of state machines, current research has expanded on the initial efforts by creating more complex and formal models: A mixture of POMDP, stochastic non-linear MPC and domain knowledge can be used to generate lane change decisions in a variety of traffic scenarios @cite_33 . Capturing the mutual dependency of maneuver decisions between different agents, planning can be conducted with foresight @cite_13 @cite_18 . While @cite_13 plans only the next maneuver focusing on the reduction of collision probabilities between all traffic participants, @cite_34 explicitly addresses longer planning horizons and the replanning capabilities of others.