Core Ontologies for Safe Autonomous Driving

Abstract. Representing the knowledge of driving environments in a structured machine-readable format is necessary for safe autonomous driving. We use ontologies to represent the knowledge of maps, driving paths, and driving environments to improve safety for smart vehicles. In this paper, we introduce core ontologies that are used for developing Advanced Driver Assistance Systems. The ontologies can be reused and extended for constructing Knowledge Base for smart vehicles as well as for implementing different types of Advanced Driver Assistance Systems

Internet of robotic things : converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms

The Internet of Things (IoT) concept is evolving rapidly and influencing newdevelopments in various application domains, such as the Internet of MobileThings (IoMT), Autonomous Internet of Things (A-IoT), Autonomous Systemof Things (ASoT), Internet of Autonomous Things (IoAT), Internetof Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc.that are progressing/advancing by using IoT technology. The IoT influencerepresents new development and deployment challenges in different areassuch as seamless platform integration, context based cognitive network integration,new mobile sensor/actuator network paradigms, things identification(addressing, naming in IoT) and dynamic things discoverability and manyothers. The IoRT represents new convergence challenges and their need to be addressed, in one side the programmability and the communication ofmultiple heterogeneous mobile/autonomous/robotic things for cooperating,their coordination, configuration, exchange of information, security, safetyand protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrencyrequire new ideas for integrating the intelligent “devices”, collaborativerobots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing,self-repair of resources, changing resource state, (re-) configurationand context based IoT systems for service implementation and integrationwith IoT network service composition are of paramount importance whennew “cognitive devices” are becoming active participants in IoT applications.This chapter aims to be an overview of the IoRT concept, technologies,architectures and applications and to provide a comprehensive coverage offuture challenges, developments and applications

A review of key planning and scheduling in the rail industry in Europe and UK

Planning and scheduling activities within the rail industry have benefited from developments in computer-based simulation and modelling techniques over the last 25 years. Increasingly, the use of computational intelligence in such tasks is featuring more heavily in research publications. This paper examines a number of common rail-based planning and scheduling activities and how they benefit from five broad technology approaches. Summary tables of papers are provided relating to rail planning and scheduling activities and to the use of expert and decision systems in the rail industry.EPSR

Defining Safe Training Datasets for Machine Learning Models Using Ontologies

Machine Learning (ML) models have been gaining popularity in recent years in a wide variety of domains, including safety-critical domains. While ML models have shown high accuracy in their predictions, they are still considered black boxes, meaning that developers and users do not know how the models make their decisions. While this is simply a nuisance in some domains, in safetycritical domains, this makes ML models difficult to trust. To fully utilize ML models in safetycritical domains, there needs to be a method to improve trust in their safety and accuracy without human experts checking each decision. This research proposes a method to increase trust in ML models used in safety-critical domains by ensuring the safety and completeness of the model’s training dataset. Since most of the complexity of the model is built through training, ensuring the safety of the training dataset could help to increase the trust in the safety of the model. The method proposed in this research uses a domain ontology and an image quality characteristic ontology to validate the domain completeness and image quality robustness of a training dataset. This research also presents an experiment as a proof of concept for this method where ontologies are built for the emergency road vehicle domain

Automated Scenario Generation Using Halton Sequences for the Verification of Autonomous Vehicle Behavior in Simulation

As autonomous vehicles continue to develop, verifying their safety remains a large hurdle to mass adoption. One component of this is testing, however it has been shown that it is impractical to statistically prove an autonomous vehicle’s safety using real-world testing alone. Therefore, simulation tools and other virtual testing methods are being employed to assist with the verification process. Testing in simulation still faces some of the challenges of the real world, such as the difficulty in exhaustively testing the system in all scenarios it will encounter. Manual scenario creation is time consuming and does not guarantee scenario coverage. Pseudo-random scenario generation is a faster option, but still does not ensure coverage of the state space. Therefore, this study proposes the use of Halton sequences to automatically generate scenarios for autonomous vehicle testing in simulation. It compares these scenarios against a set of pseudo-randomly generated scenarios and assesses the performance of each method to cover the simulation state space and provide an accurate depiction of the capabilities of the system-under-test. These tests are carried out in the CARLA simulation environment on an open source, published driving model called “Learning by Cheating” which takes place as the system-under-test. This study concludes that the scenario set generated by the Halton sequence is better at providing an accurate representation of the capabilities of the system-under-test than the pseudo-random scenario generation method

The pre-scientific concept of a "soul": A neurophenomenological hypothesis about its origin.

In this contribution I will argue that our traditional, folk-phenomenological concept of a "soul� may have its origins in accurate and truthful first-person reports about the experiential content of a specific neurophenomenological state-class. This class of phenomenal states is called the "Out-of-body experience� (OBE hereafter), and I will offer a detailed description in section 3 of this paper. The relevant type of conscious experience seems to possess a culturally invariant cluster of functional and phenomenal core properties: it is a specific kind of conscious experience, which can in principle be undergone by every human being. I propose that it probably is one of the most central semantic roots of our everyday, folk-phenomenological idea of what a soul actually is