Abstract
Most autonomous car control frameworks are based on a middleware layer with several independent modules that are connected by an inter-process communication mechanism. These modules implement basic actions and report events about their state by subscribing and publishing messages. Here, we propose an executive module that coordinates the activity of these modules. This executive module uses hierarchical interpreted binary Petri nets (PNs) to define the behavior expected from the car in different scenarios according to the traffic rules. The module commands actions by sending messages to other modules and evolves its internal state according to the events (messages) received. A programming environment named RoboGraph (RG) is introduced with this architecture. RG includes a graphical interface that allows the edition, execution, tracing, and maintenance of the PNs. For the execution, a dispatcher loads these PNs and executes the different behaviors. The RG monitor that shows the state of all the running nets has proven to be very useful for debugging and tracing purposes. The whole system has been applied to an autonomous car designed for elderly or disabled people.
Highlights
Autonomous vehicles are one of the greatest engineering challenges of our era
We propose an implementation of the executive layer based on Petri nets (PNs) using the RoboGraph (RG) tool [7]
Several tests have been carried out in two environments: the campus of the University of Alcalá de Henares and the closed circuit used in the International Conference on Intelligent Robots and Systems (IROS) 2018 autonomous vehicle demonstration event, both located in Madrid
Summary
Autonomous vehicles are one of the greatest engineering challenges of our era. Since the first successful demonstrations in the late 80s [1,2], great progress has been made in this field. The main contribution of this research is to provide a new approach to implement autonomous driving behaviors using a discrete event model framework Unlike previous solutions, this approach integrates the model definition with the model verification, execution and monitoring using a framework based on PNs. The advantages of using this kind of framework include: Behavior’s definition: A method to define the driving behaviors using message driving PNs. Traffic rules and behaviors are defined as PNs in a very intuitive and flexible way. Debugging: Many autonomous car software architectures, such as ROS, provide some way to store the inter-process communication messages Those messages can be used later to represent the system state progress through the PNs. The executive layer described here was implemented as part of the vehicle navigation system and applied to an autonomous car designed for elderly or disabled people described in [12].
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