Decision-Making for Automated Vehicles Using a Hierarchical Behavior-Based Arbitration Scheme

Behavior planning and decision-making are some of the biggest challenges for highly automated systems. A fully automated vehicle (AV) is confronted with numerous tactical and strategical choices. Most state-of-the-art AV platforms implement tactical and strategical behavior generation using finite state machines. However, these usually result in poor explainability, maintainability and scalability. Research in robotics has raised many architectures to mitigate these problems, most interestingly behavior-based systems and hybrid derivatives. Inspired by these approaches, we propose a hierarchical behavior-based architecture for tactical and strategical behavior generation in automated driving. It is a generalizing and scalable decision-making framework, utilizing modular behavior blocks to compose more complex behaviors in a bottom-up approach. The system is capable of combining a variety of scenario- and methodology-specific solutions, like POMDPs, RRT* or learning-based behavior, into one understandable and traceable architecture. We extend the hierarchical behavior-based arbitration concept to address scenarios where multiple behavior options are applicable but have no clear priority against each other. Then, we formulate the behavior generation stack for automated driving in urban and highway environments, incorporating parking and emergency behaviors as well. Finally, we illustrate our design in an explanatory evaluation

Providing over-the-horizon awareness to driver support systems

Vehicle-to-vehicle communications is a promising technique for driver support systems to increase traffic safety and efficiency. A proposed system is the Congestion Assistant [1], which aims at supporting drivers when approaching and driving in a traffic jam. Studies have shown great potential for the Congestion Assistant to reduce the impact of congestion, even at low penetration. However, these studies assumed complete and instantaneous availability of information regarding position and velocity of vehicles ahead. In this paper, we introduce a system where vehicles collaboratively build a so-called TrafficMap, providing over-the-horizon awareness. The idea is that this TrafficMap provides highly compressed information that is both essential and sufficient for the Congestion Assistant to operate. Moreover, this TrafficMap can be built in a distributed way, where only a limited subset of the vehicles have to alter it and/or forward it in the upstream direction. Initial simulation experiments show that our proposed system provides vehicles with a highly compressed view of the traffic ahead with only limited communication

Coordinated Formation Control for Intelligent and Connected Vehicles in Multiple Traffic Scenarios

In this paper, a unified multi-vehicle formation control framework for Intelligent and Connected Vehicles (ICVs) that can apply to multiple traffic scenarios is proposed. In the one-dimensional scenario, different formation geometries are analyzed and the interlaced structure is mathematically modelized to improve driving safety while making full use of the lane capacity. The assignment problem for vehicles and target positions is solved using Hungarian Algorithm to improve the flexibility of the method in multiple scenarios. In the two-dimensional scenario, an improved virtual platoon method is proposed to transfer the complex two-dimensional passing problem to the one-dimensional formation control problem based on the idea of rotation projection. Besides, the vehicle regrouping method is proposed to connect the two scenarios. Simulation results prove that the proposed multi-vehicle formation control framework can apply to multiple typical scenarios and have better performance than existing methods