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A Double-Level Model Checking Approach for an Agent-Based Autonomous Vehicle and Road Junction Regulations
 
 
Article

Hybrid Verification Technique for Decision-Making of Self-Driving Vehicles

1
Department of Automatic Control and Systems Engineering, The University of Sheffield, Sheffield S10 2TN, UK
2
Department of Electronic Engineering, State Polytechnic of Malang, Jawa Timur 65141, Indonesia
*
Author to whom correspondence should be addressed.
Academic Editors: Lei Shu, Rafael C. Cardoso, Angelo Ferrando, Daniela Briola, Claudio Menghi and Tobias Ahlbrecht
J. Sens. Actuator Netw. 2021, 10(3), 42; https://doi.org/10.3390/jsan10030042
Received: 17 March 2021 / Revised: 22 June 2021 / Accepted: 23 June 2021 / Published: 29 June 2021
(This article belongs to the Special Issue Agents and Robots for Reliable Engineered Autonomy)
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. View Full-Text
Keywords: self-driving vehicle; formal verification; model checking; rational agent; decision-making; ROS self-driving vehicle; formal verification; model checking; rational agent; decision-making; ROS
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MDPI and ACS Style

Al-Nuaimi, M.; Wibowo, S.; Qu, H.; Aitken, J.; Veres, S. Hybrid Verification Technique for Decision-Making of Self-Driving Vehicles. J. Sens. Actuator Netw. 2021, 10, 42. https://doi.org/10.3390/jsan10030042

AMA Style

Al-Nuaimi M, Wibowo S, Qu H, Aitken J, Veres S. Hybrid Verification Technique for Decision-Making of Self-Driving Vehicles. Journal of Sensor and Actuator Networks. 2021; 10(3):42. https://doi.org/10.3390/jsan10030042

Chicago/Turabian Style

Al-Nuaimi, Mohammed, Sapto Wibowo, Hongyang Qu, Jonathan Aitken, and Sandor Veres. 2021. "Hybrid Verification Technique for Decision-Making of Self-Driving Vehicles" Journal of Sensor and Actuator Networks 10, no. 3: 42. https://doi.org/10.3390/jsan10030042

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