АВТОМАТИЗАЦИЯ ПРОЦЕССА СОЗДАНИЯ СЦЕНАРИЕВ В ФОРМАТЕ OPENX НА ОСНОВЕ ОПЕРАЦИОННЫХ СИТУАЦИЙ ДЛЯ ТЕСТИРОВАНИЯ СИСТЕМ ADAS

Ожидается, что технология автоматизированного вождения станет ключевым аспектом для достижения более высокого уровня безопасности дорожного движения, улучшения транспортной связности в городах и за их пределами, повышения комфорта и обеспечения мобильности для заинтересованных сторон. С переходом к автоматизированному вождению ожидается дальнейшее развитие автоматизации, и в конце концов автомобили станут полностью автономными

Towards Efficient Hazard Identification in the Concept Phase of Driverless Vehicle Development

The complex functional structure of driverless vehicles induces a multitude of potential malfunctions. Established approaches for a systematic hazard identification generate individual potentially hazardous scenarios for each identified malfunction. This leads to inefficiencies in a purely expert-based hazard analysis process, as each of the many scenarios has to be examined individually. In this contribution, we propose an adaptation of the strategy for hazard identification for the development of automated vehicles. Instead of focusing on malfunctions, we base our process on deviations from desired vehicle behavior in selected operational scenarios analyzed in the concept phase. By evaluating externally observable deviations from a desired behavior, we encapsulate individual malfunctions and reduce the amount of generated potentially hazardous scenarios. After introducing our hazard identification strategy, we illustrate its application on one of the operational scenarios used in the research project UNICAR$agil$.Comment: Published in 2020 IEEE Intelligent Vehicles Symposium (IV), Las Vegas, NV, USA, October 19-November 13, 202

Survey of Bayesian Networks Applications to Intelligent Autonomous Vehicles

This article reviews the applications of Bayesian Networks to Intelligent Autonomous Vehicles (IAV) from the decision making point of view, which represents the final step for fully Autonomous Vehicles (currently under discussion). Until now, when it comes making high level decisions for Autonomous Vehicles (AVs), humans have the last word. Based on the works cited in this article and analysis done here, the modules of a general decision making framework and its variables are inferred. Many efforts have been made in the labs showing Bayesian Networks as a promising computer model for decision making. Further research should go into the direction of testing Bayesian Network models in real situations. In addition to the applications, Bayesian Network fundamentals are introduced as elements to consider when developing IAVs with the potential of making high level judgement calls.Comment: 34 pages, 2 figures, 3 table