TY  - JOUR
PY  - 2021//
TI  - Hybrid verification technique for decision-making of self-driving vehicles
JO  - Journal of Sensor and Actuator Networks
A1  - Al-Nuaimi, Mohammed
A1  - Wibowo, Sapto
A1  - Qu, Hongyang
A1  - Aitken, Jonathan
A1  - Veres, Sandor
SP  - e42
EP  - e42
VL  - 10
IS  - 3
N2  - 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>
LA  - en
SN  - 2224-2708
UR  - http://dx.doi.org/10.3390/jsan10030042
ID  - ref1
ER  -