Canonical queries as a query answering device (Information Science)

Issued as Annual reports [nos. 1-2], and Final report, Project no. G-36-60

Expert System and a Rule Set Development Method for Urban Behaviour Planning

Today, autonomous vehicles have the capacity to achieve fully autonomous driving in predefined environments. This ability can be in part attributed to advancements in motion planning, which plans the vehicle’ behaviours and navigation through complex environments. This thesis introduces a novel hierarchical expert system architecture along with a rule set development method for expanding an operational design domain. In the method, the knowledge engineering is tool-assisted and supports semi-automatic rule creation based on test cases. Additionally, the method incorporates a qualitative analyzer that probes the maintainability and the run time efficiency of the rule set. Moreover, the proposed architecture and method are successfully applied to implement a behavioural planner for an actual autonomous vehicle. The thesis also describes additional strategies to address noisy perception, avoid jittery behaviour, and improve the overall run time efficiency, which were necessary to achieve satisfactory performance of the planner on the road. This system was tested and proven effective in an open road test, which involved over 110 kilometres of autonomous driving in populated urban environments. During the open road test, 58 interventions were required due to perception noise or limitations arising by the small range of the lidar sensor. Finally, the strengths and weaknesses of the proposed methodology and architecture, along with an outlook on the role rule-based planning in autonomous driving, are discussed

Ontology based data warehousing for mining of heterogeneous and multidimensional data sources

Heterogeneous and multidimensional big-data sources are virtually prevalent in all business environments. System and data analysts are unable to fast-track and access big-data sources. A robust and versatile data warehousing system is developed, integrating domain ontologies from multidimensional data sources. For example, petroleum digital ecosystems and digital oil field solutions, derived from big-data petroleum (information) systems, are in increasing demand in multibillion dollar resource businesses worldwide. This work is recognized by Industrial Electronic Society of IEEE and appeared in more than 50 international conference proceedings and journals

A software architecture for electro-mobility services: a milestone for sustainable remote vehicle capabilities

To face the tough competition, changing markets and technologies in automotive industry, automakers have to be highly innovative. In the previous decades, innovations were electronics and IT-driven, which increased exponentially the complexity of vehicle’s internal network. Furthermore, the growing expectations and preferences of customers oblige these manufacturers to adapt their business models and to also propose mobility-based services. One other hand, there is also an increasing pressure from regulators to significantly reduce the environmental footprint in transportation and mobility, down to zero in the foreseeable future. This dissertation investigates an architecture for communication and data exchange within a complex and heterogeneous ecosystem. This communication takes place between various third-party entities on one side, and between these entities and the infrastructure on the other. The proposed solution reduces considerably the complexity of vehicle communication and within the parties involved in the ODX life cycle. In such an heterogeneous environment, a particular attention is paid to the protection of confidential and private data. Confidential data here refers to the OEM’s know-how which is enclosed in vehicle projects. The data delivered by a car during a vehicle communication session might contain private data from customers. Our solution ensures that every entity of this ecosystem has access only to data it has the right to. We designed our solution to be non-technological-coupling so that it can be implemented in any platform to benefit from the best environment suited for each task. We also proposed a data model for vehicle projects, which improves query time during a vehicle diagnostic session. The scalability and the backwards compatibility were also taken into account during the design phase of our solution. We proposed the necessary algorithms and the workflow to perform an efficient vehicle diagnostic with considerably lower latency and substantially better complexity time and space than current solutions. To prove the practicality of our design, we presented a prototypical implementation of our design. Then, we analyzed the results of a series of tests we performed on several vehicle models and projects. We also evaluated the prototype against quality attributes in software engineering