Abstract

The Human Centered Design (HCD) of Partial Autonomous Driver Assistance Systems (PADAS) requires Digital Human Models (DHMs) of human control strategies for simulations of traffic scenarios. The scenarios can be regarded as problem situations with one or more (partial) cooperative problem solvers. According to their roles models can be descriptive or normative . We present new model architectures and applications and discuss the suitability of dynamic Bayesian networks as control models of traffic agents: Bayesian Autonomous Driver (BAD) models. Descriptive BAD models can be used for simulating human agents in conventional traffic scenarios with Between-Vehicle-Cooperation (BVC) and in new scenarios with In-Vehicle-Cooperation (IVC). Normative BAD models representing error free behavior of ideal human drivers (e.g. driving instructors) may be used in these new IVC scenarios as a first Bayesian approximation or prototype of a PADAS. Document type: Part of book or chapter of book

Abstract

In der kommunalpolitischen und planerischen Diskussion zum Stadtverkehr verstecken sich in vielfältigen Themen zugleich auch Fragen der Zeitgerechtigkeit. Sie werden nur punktuell von Betroffenengruppen artikuliert und sind im Sinne der Verfahrensgerechtigkeit im Planungsprozess nur selten transparent mitverhandelt. Aktuell findet dies bei der Frage von Tempo 30 auf Hauptverkehrsstraßen in Verbindung mit der Lärmminderung statt. Deutlich werden zeitpolitische Fragen beim Fahrplanangebot für Bus und Bahn in der entsprechenden zeitlichen und räumlichen Verteilung. Mit dem Planwerk des Nahverkehrsplans definieren die kommunalen Aufgabenträger den Verkehrsunternehmen die Zugangsstandards für die Fahrgäste sowie andere qualitative Rahmenvorgaben - meist verbunden mit öffentlichen Finanzierungsmechanismen. Dies bestimmt dann jeweils die Erreichbarkeiten und Teilhabechancen von Personengruppen in unterschiedlicher Weise. Durch neue Technik, wie Smartphone-Apps zur Fahrplaninformation, verbessert sich die Transparenz für eine bessere Zeiteffizienz im Sinne der persönlichen Bewirtschaftung der eigenen Zeit. The primary aim of urban transport planning is to achieve spatial and temporal accessibility in the city. Like almost no other spatial planning discipline, it thus connects spatial and temporal planning issues consciously and as a matter of course. As an explicit spatial-temporal field of action, urban transport planning faces the challenge of attaining spatial and temporal justice. However, when making decisions about balancing various interests in urban transport planning these issues often seem to play only an indirect role and are rarely consciously considered. This is due, firstly, to the complexity of the various passenger interests caused by the requirements of different user groups (related to, for instance, social, economic, ethnic, age, health or occupational factors). Secondly, the conditions within which mobility providers (in local public transport these are increasingly private transport companies) operate are changing and they are restricted in their actions by economic constraints. Both factors are dependent on the constantly changing paradigms and principles of urban and transport planning. In the last century the rise of motorisation and the principle of urban areas being divided by function and planned to suit transport needs, following the Charter of Athens, rendered acceleration the central goal in the planning of urban transport and life. In contrast, current discussions about the extension of zones where speeds are limited to 30 km/h and the improvement of infrastructure for pedestrians and cyclists suggest a focus on deceleration. The objectives of the Paris climate agreement on decarbonisation and CO2-neutrality involve further challenges for urban transport, which should become more energy efficient and environmentally friendly through model shifts to local public transport and by replacing longer trips with local mobility. This offers opportunities for improving the temporal justice of urban transport.

Abstract

Shared spaces are gaining presence in cities, where a variety of players and mobility types (pedestrians, bicycles, motorcycles, and cars) move without specifically delimited areas. This makes the traffic they comprise challenging for automated systems. The information traditionally considered (e.g., streets, and obstacle positions and speeds) is not enough to build suitable models of the environment. The required explanatory and anticipation capabilities need additional information to improve them. Social aspects (e.g., goal of the displacement, companion, or available time) should be considered, as they have a strong influence on how people move and interact with the environment. This paper presents the Social-Aware Driver Assistance System (SADAS) approach to integrate this information into traffic systems. It relies on a domain-specific modelling language for social contexts and their changes. Specifications compliant with it describe social and system information, their links, and how to process them. Traffic social properties are the formalization within the language of relevant knowledge extracted from literature to interpret information. A multi-agent system architecture manages these specifications and additional processing resources. A SADAS can be connected to other parts of traffic systems by means of subscription-notification mechanisms. The case study to illustrate the approach applies social knowledge to predict people&rsquo s movements. It considers a distributed system for obstacle detection and tracking, and the intelligent management of traffic signals. Document type: Article