Ontology based Scene Creation for the Development of Automated Vehicles

The introduction of automated vehicles without permanent human supervision demands a functional system description, including functional system boundaries and a comprehensive safety analysis. These inputs to the technical development can be identified and analyzed by a scenario-based approach. Furthermore, to establish an economical test and release process, a large number of scenarios must be identified to obtain meaningful test results. Experts are doing well to identify scenarios that are difficult to handle or unlikely to happen. However, experts are unlikely to identify all scenarios possible based on the knowledge they have on hand. Expert knowledge modeled for computer aided processing may help for the purpose of providing a wide range of scenarios. This contribution reviews ontologies as knowledge-based systems in the field of automated vehicles, and proposes a generation of traffic scenes in natural language as a basis for a scenario creation.Comment: Accepted at the 2018 IEEE Intelligent Vehicles Symposium, 8 pages, 10 figure

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

Capability-Based Routes for Autonomous Vehicles

The pursuit of vehicle automation is an ongoing trend in the automotive industry. Particularly challenging is the goal of introducing driverless autonomous vehicles (AVs) into road traffic. To realize this vision, a targeted development of autonomous driving functions is essential. However, a targeted development process is only possible if the driving functions are tailored as appropriately and completely as possible to the operational design domain (ODD). Regardless of use case, all AVs have one thing in common: driving at least one route from A to B - whether simple or complex. For operational purposes, it is therefore necessary to ensure that the driving requirements (DRs) of the potential routes within the ODD do not exceed the driving capabilities (DCs) of the AVs. Currently, there is no approach that accomplishes the identification of exceeded capabilities. This work presents a method for route-based specification of DRs and DCs for AVs. It addresses the core research question of how to identify routes with DRs that do not exceed the DCs of AVs. An initial analysis reveals the dependencies between route and DRs. Thereby, the scenery defined in the ODD is found to be a fundamental basis for the specification of behavioral requirements as part of the DRs. In combination with the applicable traffic rules, the scenery elements define the behavioral limits for AVs. These limits are specifically extracted and classified as behavioral demands from the scenery using an analysis of these combinations. To enable a route-based specification of DRs, the behavioral demands are modeled as behavior spaces and transformed into a generic map representation - the Behavior-Semantic Scenery Description (BSSD). Based on the BSSD, a method is developed that generates behavioral requirements based on the route-constrained concatenation of behavior spaces. As a result, in addition to the method itself, the associated behavioral requirements are available as a basis for the route-based specification of DRs and DCs. Constraints for the specification are defined by the developed concept for the matching of DRs and DCs. It is shown that the DRs are strongly dependent on the geometry and property of the scenery elements, so that equal behavioral requirements do not necessarily imply equal DRs. These dependencies are used for the specification enabling the definition of matching criteria for a selection of DRs and corresponding DCs. To realize the matching, a capability-based route search is developed and implemented. The route search incorporates all elaborated results of the work enabling the whole approach to be evaluated by applying it to a real road network. The evaluation shows that the identification of feasible routes for AVs based on the scenery is possible and which hurdles based on identified deficits still have to be overcome

Methodology for Specifying and Testing Traffic Rule Compliance for Automated Driving

The introduction of highly-automated driving functions promises to increase safety and comfort, but the safety validation remains an unsolved challenge. Here, the requirement is that the introduction does not reduce safety on public roads. This dissertation addresses one major aspect of road safety: traffic rule compliance. Even an automated vehicle must comply with existing traffic rules. The developed method enables automated testing of traffic rule compliance of automated driving functions. In the first part of the thesis, the state of the art for describing and formalizing behavioral rules is analyzed. A special challenge is posed by the different traffic rules depending on the traffic region. With existing approaches, a separate description and formalization of the behavior rules is necessary for each traffic region or even for individual traffic areas. This shows the necessity to develop new approaches for the abstraction and transferability of the behavioral rules in order to reduce the effort of testing and ensuring traffic rule compliance. The rule compliance criteria are to be integrated into the behavior specification within the functional specification. The objective of this thesis is to develop a method to formalize the limits of traffic rule compliance, based on which fail criteria for system testing are defined and applied. For this purpose, existing traffic rules are analyzed as a basis to identify which behavior constraints are imposed by the static traffic environment. Based on this, a semantic description that is transferable between traffic domains and that links the boundaries of traffic rule compliance to the static traffic environment is developed. The method involves deriving behavioral attributes from which the semantic behavior description is constructed. These behavioral attributes construct the behavior space that describes the boundaries of legally allowed behavior. Furthermore, methods for automated derivation of behavioral attributes from high definition maps are developed, thus extracting the behavioral requirement from an operational design domain. It is investigated which functionalities an automated vehicle has to provide to comply with the behavioral attributes. The attributes are then formalized to obtain quantifiable failure criteria of traffic rule compliance that can be used in automated testing. Finally, building on the state of the art, a test strategy for validating traffic rule conformance is presented. The explicit availability of the behavioral limits results in an advantage in the influence analysis of possible parameters for these tests. Finally, the developed method is applied to existing map material and to test drives with an automated vehicle prototype in order to investigate the practical applicability of the approach as well as the resulting gain in knowledge about traffic rule compliance testing. The developed approach allows to derive the behavioral specification with respect to traffic rule conformance as an essential part of the functional specification independent of the application domain. It is proven that the approach is able to test the traffic rule conformance of an automated vehicle in different test scenarios within an application domain. By applying the developed methodology, it was possible to identify defects in the investigated test vehicle with respect to rule understanding and compliance

No driver, No Regulation? --Online Legal Driving Behavior Monitoring for Self-driving Vehicles

Defined traffic laws must be respected by all vehicles. However, it is essential to know which behaviors violate the current laws, especially when a responsibility issue is involved in an accident. This brings challenges of digitizing human-driver-oriented traffic laws and monitoring vehicles' behaviors continuously. To address these challenges, this paper aims to digitize traffic law comprehensively and provide an application for online monitoring of legal driving behavior for autonomous vehicles. This paper introduces a layered trigger domain-based traffic law digitization architecture with digitization-classified discussions and detailed atomic propositions for online monitoring. The principal laws on a highway and at an intersection are taken as examples, and the corresponding logic and atomic propositions are introduced in detail. Finally, the digitized traffic laws are verified on the Chinese highway and intersection datasets, and defined thresholds are further discussed according to the driving behaviors in the considered dataset. This study can help manufacturers and the government in defining specifications and laws and can also be used as a useful reference in traffic laws compliance decision-making. Source code is available on https://github.com/SOTIF-AVLab/DOTL.Comment: 22 pages, 11 figure

Traffic accident predictions based on fuzzy logic approach for safer urban environments, case study: İzmir Metropolitan Area

Thesis (Doctoral)--Izmir Institute of Technology, City and Regional Planning, Izmir, 2009Includes bibliographical references (leaves: 83-88)Text in English; Abstract: Turkish and Englishxii, 119, leavesDissertation has dealt with one of the most chaotic events of an urban life that is the traffic accidents. This study is a preliminary and an explorative effort to establish an Accident Prediction Model (APM) for road safety in İzmir urban environment. Aim of the dissertation is to prevent or decrease the amount of possible future traffic accidents in İzmir metropolitan region, by the help of the developed APM. Urban traffic accidents have spatial and other external reasons independent from the vehicles or drivers, and these reasons can be predicted by mathematical models. The study deals with the factors of the traffic accidents, which are not based on the human behavior or vehicle characteristics. Therefore the prediction model is established through the following external factors, such as traffic volume, rain status and the geometry of the roads. Fuzzy Logic Modeling (FLM) is applied as a prediction tool in the study. Familiarizing fuzzy logic approach to the planning discipline is the secondary aim of the thesis and contribution to the literature. The conformity of fuzzy logic enables modeling through verbal data and intuitive approach, which is important to achieve uncertainties of planning issues

Smart traffic control for the era of autonomous driving

This thesis aims to take on the challenges to address some of the key issues in traffic control and management, including intersection protocol design, congestion measurement, selfish routing and road infrastructure automation, under the assumption that all vehicles on the road are connected and self-driving. To design and test traffic control mechanisms for AVs, we introduced a formal model to represent road networks and traffic. Based on this model, we developed a simulation system on top of an existing open-source platform (AIM4) and used it to examine a number of traffic management protocols specifically designed for traffic with fully autonomous vehicles. Simulation outcomes show that traffic management protocols for AVs can be more subtle, sensitive and variable with traffic volumes/flow rate, vehicle safe distance and road configuration. In addition, by analyzing the real-world traffic data and simulation data, we found that measuring congestion with exponential functions has considerable advantages against the traditional BPR function in certain aspects. The deployment of autonomous vehicles provides traffic management with an opportunity of choosing either centralised control or decentralised control. The price of anarchy (PoA) of autonomous decision-making for routing gives an applicable quantitative criterion for selection between them. We extended the existing research on PoA with the ˙class of exponential functions as cost functions. We found an expression for the tight upper bound of the PoA for selfish routing games with exponential cost functions. Unlike existing studies, this upper bound depends on traffic demands, with which we can get a more accurate estimation of the PoA. Furthermore, by comparing the upper-bounds of PoA between the BPR function and the exponential function, we found that the exponential functions yield a smaller upper bound than the BPR functions in relatively low traffic flows. To specify traffic management systems with autonomous roadside facilities, we propose a hybrid model of traffic assignment. This model aims to describe traffic management systems in which both vehicles and roadside controllers make autonomous decisions, therefore, are autonomous agents. We formulated a non-linear optimization problem to optimize traffic control from a macroscopic view of the road network. To avoid the complex calculations required for non-linear optimization, we proposed an approximation algorithm to calculate equilibrium routing and traffic control strategies. The simulation results show that this algorithm eventually converges to a steady state. The traffic control scheme in this steady state is an approximately optimal solution

Improving data management through automatic information extraction model in ontology for road asset management

lRoads are a critical component of transportation infrastructure, and their effective maintenance is paramount in ensuring their continued functionality and safety. This research proposes a novel information management approach based on state-of-the-art deep learning models and ontologies. The approach can automatically extract, integrate, complete, and search for project knowledge buried in unstructured text documents. The approach on the one hand facilitates implementation of modern management approaches, i.e., advanced working packaging to delivery success road management projects, on the other hand improves information management practices in the construction industry