A project in Austria aims to show that there will be fewer collisions between vehicles and cyclists when cooperative intelligent transport systems (C-ITS) technology is introduced.
The Bike2CAV initiative, which ran for almost three years until April 30, 2023, tested a method for the cooperative detection of collision risks in a pilot project in Salzburg and developed warning concepts for cyclists.
ITS-G5, bike localization, cameras and lidar sensors, and roadside sensors with cameras were used, and Austrian and German researchers say the scheme validated wireless communication channels between different vehicles, bikes and infrastructure under real conditions for the first time – in three scenarios at two test crossings in rural and urban areas.
A networked, automated vehicle and a new type of networked research bicycle were used in the tests at the test crossings Weiserstraße/Gabelsbergerstraße in Salzburg, which are equipped with smart sensors, and on the B158 in the Salzburg municipality of Koppl.
The research consortium consisted of the Salzburg Research Forschungsgesellschaft (project management); AIT – Austrian Institute of Technology, Center for Vision, Automation & Control; University of Salzburg, Institute for Geoinformatics; Kapsch TrafficCom; Mobile solutions for cyclists; Boréal bicycles; and KFV – Board of Trustees for Road Safety.
Testing different data processing methods
Different data processing methods were tested, from the self-localization and detection of road users, the recognition of collision risks and the generation and transmission of warning messages to communication with cyclists and other road users.
“An important finding was that cyclists often use the infrastructure at the inner-city junction investigated differently than intended,” says Martin Loidl from the University of Salzburg. “This is probably due to the fact that the planning is primarily based on the needs of motor vehicle traffic.”
In addition to two GNSS receivers built into the Holoscene bike, the accuracy of a smartphone and a high-precision sensor mounted on the helmet were also examined. The goal was a deviation of less than 50 cm with a reliability of 99.9%.
In 2015 Statistics Austria recorded 6,901 traffic accidents involving bicycles – in 2021 it was 9,578, with between 32 and 50 cyclists losing their lives every year during this period.
The accidents with other vehicles mostly happened when turning at an intersection, with the bicycle usually going straight.
“In addition, there are a large number of near misses that do not appear in any accident statistics,” says project manager Cornelia Zankl from Salzburg Research.
“That’s why we wanted to use our research to better assess these risks so that we can take action before something happens.”
The desired location accuracy is “very demanding” due to the dense development and a railway underpass, says Zankl. The researchers found a lateral deviation of 0.5 m with 95% reliability in rural areas, with less than 2 m and 95% reliability in built-up areas.
Active detection via ITS-G5
Equipping bicycles with V2X technology enables automated vehicles to have active detection via ITS-G5 in addition to passive detection via environment sensors.
“Bikes like this are not yet available on the market, but a proof-of-concept prototype was tested in the project,” says Louis P. Huard, CEO of Boréal Bikes.
“Our camera-based AI detection system for detecting and classifying motor vehicles and pedestrians has been expanded and optimized for the detection of cyclists,” says Alexander Paier from Kapsch TrafficCom.
“In addition, the draft of the message format Collective Perception Message for the transmission of information from detected road users was successfully tested for V2X communication.”
“For a reliable movement prediction, the visual determination of body posture and the recognition of hand signals are particularly important,” says Martin Fletzer from AIT – Austrian Institute of Technology.
Various warning modes – acoustic, optical and tactile warning signals – were designed and tested with the help of a navigation app on the smartphone, vibration on the handlebars and acoustic signals in the helmet. “Especially in situations where a vehicle is approaching from behind, the cyclists found acoustic warnings to be particularly helpful,” says Zankl.
Although the tests have been successful, further development is needed, she points out. “In summary, we can confirm that collision risks can be detected cooperatively with the chosen approach. However, the connection of different data sources and the processing of the large amounts of data was still very time-consuming.”
The Road Safety Council (KFV) checked whether the cooperatively recognized situations were actually dangerous for cyclists and whether they were effectively warned of a danger. “In the field test, we managed to generate a good selection of typical dangerous situations for cyclists. In 27 of the 30 trips, a warning of an actually dangerous situation for cyclists was sent to the road users involved,” confirms Hatun Atasayar, safety expert at KFV.
The research was funded by the Federal Ministry for Climate Protection, Environment, Energy, Mobility, Innovation and Technology.
