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

A representation of robotic behaviors using component port arbitration

Presented at DSLRob 2014 (arXiv:cs/1411.7148)Developing applications considering reactiveness, scalability and re-usability has always been at the center of attention of robotic researchers. Behavior-based architectures have been proposed as a programming paradigm to develop robust and complex behaviors as integration of simpler modules whose activities are directly modulated by sensory feedback or input from other models. The design of behavior based systems, however, becomes increasingly difficult as the complexity of the application grows. This article proposes an approach for modeling and coordinating behaviors in distributed architectures based on port arbitration which clearly separates representation of the behaviors from the composition of the software components. Therefore, based on different behavioral descriptions, the same software components can be reused to implement different applications

Building Complex Systems with Agent-Space Architecture

Building complex systems requires a specific kind of modularity as well as incremental development. Here we introduce an architecture where basic modules are reactive agents and the data exchange among them is based on the so-called stigmergic communication through space. In this way we have connected ideas coming from the multi-agent systems and the coordination languages on the one side and ideas of the behavior-based systems on the other side. We demonstrate that this architecture manifests several interesting features which are useful for engineering of real-time systems and modeling of biological creatures or their parts. We advocate for so-called purely reactive agents which are stateless entities usually taken as too weak building blocks of systems. However, their features enable us to use a special method of incremental development (so-called subsumption method)

Model-Based Adaptive Behavior Framework for Optimal Acoustic Communication and Sensing by Marine Robots

In this paper, a hybrid data- and model-based autonomous environmental adaptation framework is presented which allows autonomous underwater vehicles (AUVs) with acoustic sensors to follow a path which optimizes their ability to maintain connectivity with an acoustic contact for optimal sensing or communication. The adaptation framework is implemented within the behavior-based mission-oriented operating suite-interval programming (MOOS-IvP) marine autonomy architecture and uses a new embedded high-fidelity acoustic modeling infrastructure, the generic robotic acoustic model (GRAM), to provide real-time estimates of the acoustic environment under changing environmental and situational scenarios. A set of behaviors that combine adaptation to the current acoustic environment with strategies that extend the decision horizon beyond that of typical behavior-based systems have been developed, implemented, and demonstrated in a series of field experiments and virtual experiments in a MOOS-IvP simulation.United States. Office of Naval Research (Grant N00014-08-1-0011)United States. Office of Naval Research (Grant N00014-08-1-0013)NATO Undersea Research Centre (NURC

Sensor networks security based on sensitive robots agents. A conceptual model

Multi-agent systems are currently applied to solve complex problems. The security of networks is an eloquent example of a complex and difficult problem. A new model-concept Hybrid Sensitive Robot Metaheuristic for Intrusion Detection is introduced in the current paper. The proposed technique could be used with machine learning based intrusion detection techniques. The new model uses the reaction of virtual sensitive robots to different stigmergic variables in order to keep the tracks of the intruders when securing a sensor network.Comment: 5 page

Effects of Behavior-Based Driver Feedback Systems on Commercial Long Haul Operator Safety

There are large economic and societal costs to commercial motor vehicle crashes. A majority of crashes are precipitated due to driver-related factors. Behavior-based systems that influence drivers with feedback from safety managers can help reduce driver-related risk factors. These systems harness the experience and knowledge of managers along with advanced driver telematics that monitor and record driver behaviors to positively influence driver safety. Safety solutions that focus on modifying driver behaviors thus hold promise for improving the safety record of commercial trucking. In this study, one such feedback system was examined by analyzing data from a commercial trucking fleet, treating the system deployment as a natural experiment. This made it possible, without experimental intervention, to compare drivers before and after system introduction, and to compare drivers that were subject to this system with those that drove with no supervisor feedback. Adverse event data were obtained for drivers in the fleet and weekly event rates were calculated taking into account driving exposure (in miles). Results show that drivers improved after receiving safety feedback and significantly more so than drivers that did not receive feedback

Unified Behavior Framework for Reactive Robot Control

Behavior-based systems form the basis of autonomous control for many robots. In this article, we demonstrate that a single software framework can be used to represent many existing behavior based approaches. The unified behavior framework presented, incorporates the critical ideas and concepts of the existing reactive controllers. Additionally, the modular design of the behavior framework: (1) simplifies development and testing; (2) promotes the reuse of code; (3) supports designs that scale easily into large hierarchies while restricting code complexity; and (4) allows the behavior based system developer the freedom to use the behavior system they feel will function the best. When a hybrid or three layer control architecture includes the unified behavior framework, a common interface is shared by all behaviors, leaving the higher order planning and sequencing elements free to interchange behaviors during execution to achieve high level goals and plans. The framework\u27s ability to compose structures from independent elements encourages experimentation and reuse while isolating the scope of troubleshooting to the behavior composition. The ability to use elemental components to build and evaluate behavior structures is demonstrated using the Robocode simulation environment. Additionally, the ability of a reactive controller to change its active behavior during execution is shown in a goal seeking robot implementation