Model Checking for Decision Making System of Long Endurance Unmanned Surface Vehicle

This work aims to develop a model checking method to verify the decision making system of Unmanned Surface Vehicle (USV) in a long range surveillance mission. The scenario in this work was captured from a long endurance USV surveillance mission using C-Enduro, an USV manufactured by ASV Ltd. The C-Enduro USV may encounter multiple non-deterministic and concurrent problems including lost communication signals, collision risk and malfunction. The vehicle is designed to utilise multiple energy sources from solar panel, wind turbine and diesel generator. The energy state can be affected by the solar irradiance condition, wind condition, states of the diesel generator, sea current condition and states of the USV. In this research, the states and the interactive relations between environmental uncertainties, sensors, USV energy system, USV and Ground Control Station (GCS) decision making systems are abstracted and modelled successfully using Kripke models. The desirable properties to be verified are expressed using temporal logic statement and finally the safety properties and the long endurance properties are verified using the model checker MCMAS, a model checker for multi-agent systems. The verification results are analyzed and show the feasibility of applying model checking method to retrospect the desirable property of the USV decision making system. This method could assist researcher to identify potential design error of decision making system in advance

Formal Estimation of Collision Risks for Autonomous Vehicles: A Compositional Data-Driven Approach

In this work, we propose a compositional data-driven approach for the formal estimation of collision risks for autonomous vehicles (AVs) while acting in a stochastic multi-agent framework. The proposed approach is based on the construction of sub-barrier certificates for each stochastic agent via a set of data collected from its trajectories while providing an a-priori guaranteed confidence on the data-driven estimation. In our proposed setting, we first cast the original collision risk problem for each agent as a robust optimization program (ROP). Solving the acquired ROP is not tractable due to an unknown model that appears in one of its constraints. To tackle this difficulty, we collect finite numbers of data from trajectories of each agent and provide a scenario optimization program (SOP) corresponding to the original ROP. We then establish a probabilistic bridge between the optimal value of SOP and that of ROP, and accordingly, we formally construct the sub-barrier certificate for each unknown agent based on the number of data and a required level of confidence. We then propose a compositional technique based on small-gain reasoning to quantify the collision risk for multi-agent AVs with some desirable confidence based on sub-barrier certificates of individual agents constructed from data. For the case that the proposed compositionality conditions are not satisfied, we provide a relaxed version of compositional results without requiring any compositionality conditions but at the cost of providing a potentially conservative collision risk. Eventually, we also present our approaches for non-stochastic multi-agent AVs. We demonstrate the effectiveness of our proposed results by applying them to a vehicle platooning consisting of 100 vehicles with 1 leader and 99 followers. We formally estimate the collision risk by collecting data from trajectories of each agent.Comment: This work has been accepted at IEEE Transactions on Control of Network System

Validation of Perception and Decision-Making Systems for Autonomous Driving via Statistical Model Checking

International audienceAutomotive systems must undergo a strict process of validation before their release on commercial vehicles. With the increased use of probabilistic approaches in autonomous systems, standard validation methods are not applicable to this end. Furthermore, real life validation, when even possible, implies costs which can be obstructive. New methods for validation and testing are thus necessary. In this paper, we propose a generic method to evaluate complex probabilistic frameworks for autonomous driving. The method is based on Statistical Model Checking (SMC), using specifically defined Key Performance Indicators (KPIs), as temporal properties depending on a set of identified metrics. By studying the behavior of these metrics during a large number of simulations via our statistical model checker, we finally evaluate the probability for the system to meet the KPIs. We show how this method can be applied to two different subsystems of an autonomous vehicle: a perception system and a decision-making approach. An overview of these two systems is given to understand related validation challenges. Extensive validation results are then provided for the decision-making case

Validation of Perception and Decision-Making Systems for Autonomous Driving via Statistical Model Checking

Automotive systems must undergo a strict process of validation before their release on commercial vehicles. With the increased use of probabilistic approaches in autonomous systems, standard validation methods are not applicable to this end. Furthermore, real life validation, when even possible, implies costs which can be obstructive. New methods for validation and testing are thus necessary. In this paper, we propose a generic method to evaluate complex probabilistic frameworks for autonomous driving. The method is based on Statistical Model Checking (SMC), using specifically defined Key Performance Indicators (KPIs), as temporal properties depending on a set of identified metrics. By studying the behavior of these metrics during a large number of simulations via our statistical model checker, we finally evaluate the probability for the system to meet the KPIs. We show how this method can be applied to two different subsystems of an autonomous vehicle: a perception system and a decision-making approach. An overview of these two systems is given to understand related validation challenges. Extensive validation results are then provided for the decision-making case

Trajectory planning based on adaptive model predictive control: Study of the performance of an autonomous vehicle in critical highway scenarios

Increasing automation in automotive industry is an important contribution to overcome many of the major societal challenges. However, testing and validating a highly autonomous vehicle is one of the biggest obstacles to the deployment of such vehicles, since they rely on data-driven and real-time sensors, actuators, complex algorithms, machine learning systems, and powerful processors to execute software, and they must be proven to be reliable and safe. For this reason, the verification, validation and testing (VVT) of autonomous vehicles is gaining interest and attention among the scientific community and there has been a number of significant efforts in this field. VVT helps developers and testers to determine any hidden faults, increasing systems confidence in safety, security, functional analysis, and in the ability to integrate autonomous prototypes into existing road networks. Other stakeholders like higher-management, public authorities and the public are also crucial to complete the VTT process. As autonomous vehicles require hundreds of millions of kilometers of testing driven on public roads before vehicle certification, simulations are playing a key role as they allow the simulation tools to virtually test millions of real-life scenarios, increasing safety and reducing costs, time and the need for physical road tests. In this study, a literature review is conducted to classify approaches for the VVT and an existing simulation tool is used to implement an autonomous driving system. The system will be characterized from the point of view of its performance in some critical highway scenarios.O aumento da automação na indústria automotiva é uma importante contribuição para superar muitos dos principais desafios da sociedade. No entanto, testar e validar um veículo altamente autónomo é um dos maiores obstáculos para a implantação de tais veículos, uma vez que eles contam com sensores, atuadores, algoritmos complexos, sistemas de aprendizagem de máquina e processadores potentes para executar softwares em tempo real, e devem ser comprovadamente confiáveis e seguros. Por esta razão, a verificação, validação e teste (VVT) de veículos autónomos está a ganhar interesse e atenção entre a comunidade científica e tem havido uma série de esforços significativos neste campo. A VVT ajuda os desenvolvedores e testadores a determinar quaisquer falhas ocultas, aumentando a confiança dos sistemas na segurança, proteção, análise funcional e na capacidade de integrar protótipos autónomos em redes rodoviárias existentes. Outras partes interessadas, como a alta administração, autoridades públicas e o público também são cruciais para concluir o processo de VTT. Como os veículos autónomos exigem centenas de milhões de quilómetros de testes conduzidos em vias públicas antes da certificação do veículo, as simulações estão a desempenhar cada vez mais um papel fundamental, pois permitem que as ferramentas de simulação testem virtualmente milhões de cenários da vida real, aumentando a segurança e reduzindo custos, tempo e necessidade de testes físicos em estrada. Neste estudo, é realizada uma revisão da literatura para classificar abordagens para a VVT e uma ferramenta de simulação existente é usada para implementar um sistema de direção autónoma. O sistema é caracterizado do ponto de vista do seu desempenho em alguns cenários críticos de autoestrad