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.

This position paper describes ongoing work at the Universities of Liverpool, Sheffield and Surrey in the UK on developing hybrid agent architectures for controlling autonomous systems, and specifically for ensuring that agent-controlled dynamic reconfiguration is viable. The work outlined here forms part of the Reconfigurable Autonomy research project.

The strong consistency of the predictive least squares (PLS) structure selection criterion is proved for ARMA processes. The advantage of the PLS criterion defined by Rissanen and Wertz (1985) over the BIC, φ or AIC criteria is that it is an online information criterion obtained by simple calculation. The strong consistency is proved for the case of the maximum likelihood estimator, while in simulations the likelihood estimator is substituted by its recursive maximum likelihood estimator approximation. Repeated simulations show the small sample comparison of the PLS and BIC criteria.

Quadcopter controllers are in use today and in practice they can often cope well in non adverse weather conditions such as lack of strong sudden gusts of wind around the corner of a building or no frequent demands of travel directions by remote control or a guidance law. Different payloads can alter the boundaries of the stable state space region of a drone, its flight envelop, beyond which its autopilot may not be able to regain stable control of the craft. For fixed gain autopilot controllers, reaching the boundary of the flight envelop can be caused by (1) external disturbance like gusts of wind and turbulence, (2) altered drone mass and its distribution and (3) reduction or misalignment of thrust output in the propulsion system caused ware after multiple uses of the drone. This paper introduces symbolic computation to map out the numerical boundaries of controller tolerances in terms of these three factors that affect the autopilots ability to retain stability of the craft. Proof theoretic methods are developed that can be applied to quadcopter of various nominal parameters.