Implementing Autonomous Driving Behaviors Using a Message Driven Petri Net Framework

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.

Sensors and System for Vehicle Navigation

“…Machine learning methods will help to improve the accuracy of integrated data, assuming that the training set and the analyzed data come from the same devices and systems. López, J. et al [11] provide a new approach to implement autonomous driving behaviors using a discrete event model framework. The article deals with the typical problems of the executive layer in the autonomous decision-making system for cars.…”

Challenges in Application of Petri Nets in Manufacturing Systems

“…Generalised nondeterministic batch Petri net (GNBPN) models are combined with a dynamic estimation of intersection turning movement counts for predicting the road traffic flow in [69]. A message-driven PN framework with hierarchical interpreted binary PNs for implementing autonomous driving behaviours is introduced in [70]. The decision-making process in the context of energy and environmental management is represented as a PN model in [71].…”

Efficient Local Navigation Approach for Autonomous Driving Vehicles

“…The maximum linear velocity is the minimum of all the maximum velocities. There are two new restrictions on that value: the first one comes from the behavior decision layer [33] and the second one from the road curvature. The behavior decision layer sets a limit based on the traffic rules (traffic signs and limits depending on the type of road) and the specific situation (for example, if there is a pedestrian close to the car).…”