The provision of a barrier-free road infrastructure is the basis for the development of an inclusive society in order to guarantee mobility for everyone. However, reality shows that mobility for disabled persons is often associated with major challenges.
The KIMONO-EF project, funded by the German Federal Ministry of Digital Affairs and Transport under the funding guideline "A future-proof sustainable mobility system by automated driving and connectivity", is dedicated to reduce challenges in traffic by using technical systems.
The situation for disabled persons in the field of mobility, especially at the interfaces between non-motorized and (motorized) individual traffic as well as public transport is often challenging regarding traffic safety. Pedestrian traffic at traffic signals and the transition from pedestrian traffic to public transport at stops are particularly relevant. Accessibility measures are often limited to major structural changes. Recently, however, smartphone-based applications have become increasingly available for people with special needs. Therefore, there are services to recommend barrier-free routes through individual cities and municipalities. Other approaches additionally offer the request of extended signal phases via smartphone in cooperation with road operators. However, most solutions require the ability to use a smartphone in challenging situations and they can only be used regionally. Therefore, a more generally useful technology is necessary.
Goals of the research project
The goal of the project "AI- and M2M-based optimization of safety and comfort for disabled persons in non-motorized individual traffic in the local area of ERFURT" is to increase traffic safety and comfort of people with disabilities at traffic signal controlled intersections and at the transitions from non-motorized MIV to public traffic by automatic detection. This enables the automatic intelligent adaptation of LSA controls and the information of the driving personnel in public traffic. In addition, information can also be transmitted to vehicle drivers in MIV. In the future, this type of information will also be essential for automated vehicles. The information should be provided via V2X interfaces. These measures at intersections and public transport stops further advance the connected and automated traffic system (vehicle and infrastructure) and at the same time contribute to an inclusive society.
In the project, solutions will be developed that automatically identify disabled persons using AI-based sensors and predict their behavior. Based on this information, supportive measures will be initiated, such as phase extension, triggering signals for people with visual impairments, or forwarding information to the public traffic vehicle or driver.