@article{ref1,
title="Hybrid verification technique for decision-making of self-driving vehicles",
journal="Journal of Sensor and Actuator Networks",
year="2021",
author="Al-Nuaimi, Mohammed and Wibowo, Sapto and Qu, Hongyang and Aitken, Jonathan and Veres, Sandor",
volume="10",
number="3",
pages="e42-e42",
abstract="The evolution of driving technology has recently progressed from active safety features and ADAS systems to fully sensor-guided autonomous driving. Bringing such a vehicle to market requires not only simulation and testing but formal verification to account for all possible traffic scenarios. A new verification approach, which combines the use of two well-known model checkers: model checker for multi-agent systems (MCMAS) and probabilistic model checker (PRISM), is presented for this purpose. The overall structure of our autonomous vehicle (AV) system consists of: (1) A perception system of sensors that feeds data into (2) a rational agent (RA) based on a belief-desire-intention (BDI) architecture, which uses a model of the environment and is connected to the RA for verification of decision-making, and (3) a feedback control systems for following a self-planned path. MCMAS is used to check the consistency and stability of the BDI agent logic during design-time. PRISM is used to provide the RA with the probability of success while it decides to take action during run-time operation. This allows the RA to select movements of the highest probability of success from several generated alternatives. This framework has been tested on a new AV software platform built using the robot operating system (ROS) and virtual reality (VR) Gazebo Simulator. It also includes a parking lot scenario to test the feasibility of this approach in a realistic environment. A practical implementation of the AV system was also carried out on the experimental testbed.<p /> <p>Language: en</p>",
language="en",
issn="2224-2708",
doi="10.3390/jsan10030042",
url="http://dx.doi.org/10.3390/jsan10030042"
}