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

Domain Knowledge Distillation from Large Language Model: An Empirical Study in the Autonomous Driving Domain

Engineering knowledge-based (or expert) systems require extensive manual effort and domain knowledge. As Large Language Models (LLMs) are trained using an enormous amount of cross-domain knowledge, it becomes possible to automate such engineering processes. This paper presents an empirical automation and semi-automation framework for domain knowledge distillation using prompt engineering and the LLM ChatGPT. We assess the framework empirically in the autonomous driving domain and present our key observations. In our implementation, we construct the domain knowledge ontology by "chatting" with ChatGPT. The key finding is that while fully automated domain ontology construction is possible, human supervision and early intervention typically improve efficiency and output quality as they lessen the effects of response randomness and the butterfly effect. We, therefore, also develop a web-based distillation assistant enabling supervision and flexible intervention at runtime. We hope our findings and tools could inspire future research toward revolutionizing the engineering of knowledge-based systems across application domains.Comment: Accepted by ITSC 202

Ingénierie des exigences pour la conception d'architectures de sécurité de systèmes embarqués distribués

During the last ten years, the impact of security concerns on the development and exploration of distributed embedded systems never ceased to grow. This is mainly related to the fact that these systems are increasingly interconnected and thus vulnerable to attacks, and that the economic interest in attacking them has simultane- ously increased. In such a context, requirement engineering methodologies and tools have become necessary to take appropriate decisions regarding security early on. Security requirements engineering should thus strongly support the elicitation and specifica- tion of software security issues and solutions well before designers and developers are committed to a particular implementation. However, and that is especially true in embedded systems, security requirements should not be considered only as the abstract expression of a set of properties independently from the system architecture or from the threats and attacks that may occur. We believe this consideration is of utmost importance for security requirements engineering to be the driving force behind the design and implementation of a secure system. We thus describe in this thesis a security engineering requirement methodology depending upon a constant dialog between the design of system functions, the requirements that are attached to them, the design and development of the system architecture, and the assessment of the threats to system assets. Our approach in particular relies on a knowledge-centric approach to security requirement engineering, applicable from the early phases of system conceptualization to the enforcement of security requirements.Au cours des dix dernières années, l’impact des questions de sécurité sur le développement et la mise en oeuvre des systèmes embarqués distribués n’a jamais cessé de croître. Ceci est principalement lié à l’interconnexion toujours plus importante de ces systèmes qui les rend vulnérables aux attaques, ainsi qu’à l’intérêt économique d’attaquer ces systèmes qui s’est simultanément accru. Dans un tel contexte, méthodologies et outils d’ingénierie des exigences de sécurité sont devenus indispensables pour prendre des décisions appropriées quant a` la sécurité, et ce le plus tôt possible. L’ingénierie des exigences devrait donc fournir une aide substantielle à l’explicitation et à la spécification des problèmes et solutions de sécurité des logiciels bien avant que concepteurs et développeurs ne soient engagés dans une implantation en particulier. Toutefois, et c’est particulièrement vrai dans les systèmes embarqués, les exigences de sécurité ne doivent pas être considérées seulement comme l’expression abstraite d’un ensemble de propriétés indépendamment de l’architecture système ou des menaces et des attaques qui pourraient y survenir. Nous estimons que cette considération est d’une importance capitale pour faire de l’ingénierie des exigences un guide et un moteur de la conception et de la mise en œuvre d’un système sécurisé. Notre approche s’appuie en particulier sur une approche centrée sur les connaissances de l’ingénierie des exigences de sécurité, applicable dès les premières phases de conception du système jusqu’à la mise en application des exigences de sécurité dans l’implantation

Non-Line of Sight Test Scenario Generation for Connected Autonomous Vehicle

Connected autonomous vehicles (CAV) level 4-5 use sensors to perceive their environment. These sensors are able to detect only up to a certain range and this range can be further constrained by the presence of obstacles in its path or as a result of the geometry of the road, for example, at a junction. This is termed as a non-line of sight (NLOS) scenario where the ego vehicle (system under test) is unable to detect an oncoming dynamic object due to obstacles or the geometry of the road. A large body of work now exist which proposes methods for extending the perception horizon of CAV’s using vehicular communication and incorporating this into CAV algorithms ranging from obstacle detection to path planning and beyond. Such proposed new algorithms and entire systems needs testing and validating, which can be conducted through primarily two ways, on road testing and simulation. On-road testing can be extremely expensive and time-consuming and may not cover all possible test scenarios. Testing through simulation is inexpensive and has a better scenario space coverage. However, there is currently a dearth in simulated testing techniques that provides the environment to test technologies and algorithms developed for NLOS scenarios. This thesis puts forward a novel end-to-end framework for testing the abilities of a CAV through simulated generation of NLOS scenarios. This has been achieved through following the development process of Functional, Logical and Concrete scenarios along the V-model-based development process in ISO 26262. The process begins with the representation of the NLOS environment (including the digital environment) knowledge as a scalable ontology where Functional and Logical scenarios stand for different abstraction levels. The proposed new ontology comprises of six layers: ‘Environment’, ‘Road User’, ‘Object Type’, ‘Communication Network’, ‘Scene’ and ‘Scenario’. The ontology is modelled and validated in protégé software and exported to OWL API where the logical scenarios are generated and validated. An innumerable number of “concrete” scenarios are generated as a result of the possible combinations of the values from the domains of each concept’s attributes. This research puts forward a novel genetic- algorithm (GA) approach to search through the scenario space and filter out safety critical test scenarios. A critical NLOS scenario is one where a collision is highly likely because the ego vehicle was unable to detect an obstacle in time due to obstructions present in the line-of-sight of the sensors or created due to the road geometry. The metric proposed to identify critical scenarios which also acts as the GA’s fitness function uses the time-to-collision (TTC) and total stopping time (TST) metric. These generated critical scenarios and proposed fitness function have been validated through MATLAB simulation. Furthermore, this research incorporates the relevant knowledge of vehicle-to-vehicle (V2V) communication technologies in the proposed ontology and uses the communication layer instances in the MATLAB simulation to support the testing of the increasing number of approaches that uses communications for alerting oncoming vehicles about imminent danger, or in other word, mitigating an otherwise critical scenario

Towards intelligent transport systems: geospatial ontological framework and agent simulation

In an Intelligent Transport System (ITS) environment, the communication component is of high significance as it supports interactions between vehicles and the roadside infrastructure. Existing studies focus on the physical capability and capacity of the communication technologies, but the equally important development of suitable and efficient semantic content for transmission has received notably less attention. Using an ontology is one promising approach for context modelling in ubiquitous computing environments. In the transport domain, an ontology can be used both for context modelling and semantic contents for vehicular communications. This research explores the development of an ontological framework implementing a geosemantic messaging model to support vehicle-to-vehicle communications. To develop an ontology model, two scenarios (an ambulance situation and a breakdown on the motorway) are constructed to describe specific situations using short-range communication in an ITS environment. In the scenarios, spatiotemporal relations and semantic relations among vehicles and road facilities are extracted and defined as classes, objects, and properties/relations in the ontology model. For the ontology model, some functions and query templates are also developed to update vehicles’ movements and to provide some logical procedures that vehicles need to follow in emergency situations. To measure the effects of the vehicular communication based on the ontology model, an agent-based approach is adopted to dynamically simulate the moving vehicles and their communications following the scenarios. The simulation results demonstrate that the ontology model can support vehicular communications to update each vehicle’s context model and assist its decision-making process to resolve the emergency situations. The results also show the effect of vehicular communications on the efficiency trends of traffic in emergency situations, where some vehicles have a communication device, and others do not. The efficiency trends, based on the percentage of vehicles having a communication device, can be useful to set a transition period plan for implanting communication devices onto vehicles and the infrastructure. The geospatial ontological framework and agent simulation may contribute to increase the intelligence of ITS by supporting data-level and application-level implementation of autonomous vehicle agents to share knowledge in local contexts. This work can be easily extended to support more complex interactions amongst vehicles and the infrastructure

Cognitive radio network in vehicular ad hoc network (VANET): a survey

Cognitive radio network and vehicular ad hoc network (VANET) are recent emerging concepts in wireless networking. Cognitive radio network obtains knowledge of its operational geographical environment to manage sharing of spectrum between primary and secondary users, while VANET shares emergency safety messages among vehicles to ensure safety of users on the road. Cognitive radio network is employed in VANET to ensure the efficient use of spectrum, as well as to support VANET’s deployment. Random increase and decrease of spectrum users, unpredictable nature of VANET, high mobility, varying interference, security, packet scheduling, and priority assignment are the challenges encountered in a typical cognitive VANET environment. This paper provides survey and critical analysis on different challenges of cognitive radio VANET, with discussion on the open issues, challenges, and performance metrics for different cognitive radio VANET applications

Cognitive radio network in vehicular ad hoc network (VANET): a survey

Cognitive radio network and vehicular ad hoc network (VANET) are recent emerging concepts in wireless networking. Cognitive radio network obtains knowledge of its operational geographical environment to manage sharing of spectrum between primary and secondary users, while VANET shares emergency safety messages among vehicles to ensure safety of users on the road. Cognitive radio network is employed in VANET to ensure the efficient use of spectrum, as well as to support VANET’s deployment. Random increase and decrease of spectrum users, unpredictable nature of VANET, high mobility, varying interference, security, packet scheduling, and priority assignment are the challenges encountered in a typical cognitive VANET environment. This paper provides survey and critical analysis on different challenges of cognitive radio VANET, with discussion on the open issues, challenges, and performance metrics for different cognitive radio VANET applications

Human-Intelligence and Machine-Intelligence Decision Governance Formal Ontology

Since the beginning of the human race, decision making and rational thinking played a pivotal role for mankind to either exist and succeed or fail and become extinct. Self-awareness, cognitive thinking, creativity, and emotional magnitude allowed us to advance civilization and to take further steps toward achieving previously unreachable goals. From the invention of wheels to rockets and telegraph to satellite, all technological ventures went through many upgrades and updates. Recently, increasing computer CPU power and memory capacity contributed to smarter and faster computing appliances that, in turn, have accelerated the integration into and use of artificial intelligence (AI) in organizational processes and everyday life. Artificial intelligence can now be found in a wide range of organizational systems including healthcare and medical diagnosis, automated stock trading, robotic production, telecommunications, space explorations, and homeland security. Self-driving cars and drones are just the latest extensions of AI. This thrust of AI into organizations and daily life rests on the AI community’s unstated assumption of its ability to completely replicate human learning and intelligence in AI. Unfortunately, even today the AI community is not close to completely coding and emulating human intelligence into machines. Despite the revolution of digital and technology in the applications level, there has been little to no research in addressing the question of decision making governance in human-intelligent and machine-intelligent (HI-MI) systems. There also exists no foundational, core reference, or domain ontologies for HI-MI decision governance systems. Further, in absence of an expert reference base or body of knowledge (BoK) integrated with an ontological framework, decision makers must rely on best practices or standards that differ from organization to organization and government to government, contributing to systems failure in complex mission critical situations. It is still debatable whether and when human or machine decision capacity should govern or when a joint human-intelligence and machine-intelligence (HI-MI) decision capacity is required in any given decision situation. To address this deficiency, this research establishes a formal, top level foundational ontology of HI-MI decision governance in parallel with a grounded theory based body of knowledge which forms the theoretical foundation of a systemic HI-MI decision governance framework