Motorcyclists are among the most vulnerable road users. Without a protective body, even the slightest carelessness can have serious consequences. A forgiving road is therefore unavoidable, say the traffic safety experts at the AIT Center for Mobility Systems in Vienna. They developed a motorcycle that can use sensors and algorithms to identify dangerous road locations. The results can be used in traffic safety technology.
Complex driving dynamics
There is certainly a need for action: The number of fatal traffic accidents involving passenger cars fell to 400 in 2018, the lowest level recorded in traffic records. However, the number of motorcycle accidents has continued to rise (Source: accident statistics 2018 of the Austrian Federal Ministry of Transport). In general, it is the complex driving dynamics and physics that lead to driving errors, especially among inexperienced motorcyclists. But certain factors, such as insufficient road grip, are not predictable even for experienced riders.
Highly specialized test vehicle
In the viaMotorrad project, experts from the AIT Center for Mobility developed a highly specialized test vehicle together with the Institute of Mechanics and Mechatronics at the Vienna University of Technology. The object runs under the name Motorcycle Probe Vehicle, MoProVe for short. It is a motorbike equipped with technology and equipped with a high-precision measuring system. As a mobile laboratory, it serves to analyze the interaction between vehicle and road and to investigate driving dynamics and vehicle safety. In the project, six road sections were tested with five different types of motorcyclists from beginners to racers, explains Florian Hainz, Press Officer at the AIT Center for Mobility.
The aim of the project is to:
- Better understand the causes of motorcycle accidents
- Identify risky stretches of road before accidents occur
- Provide road owners with a tool that enables the efficient, cost-effective and sustainable mitigation of hazardous areas.
The analysis of the measurement data can also be used by industry, adds Hainz. It could be used to develop vehicle assistants that warn motorcyclists of dangers in good time.
Acquisition of vehicle signals and data
The special feature of MoProVe are two independently working measuring instruments. These complement each other and enable precise operation and precise coverage of the parameters to be measured. The main modules of both measuring systems are six-axis motion detectors (IMU), GPS antennas, CAN interfaces and data loggers. Both systems collect vehicle signals and data such as wheel speed, throttle position and brake system pressure.
- One system is commonly used for vehicle motion (via dual GPS antennas and a DGPS station)
- Another system is mainly used on and off the chassis and steering system level.
Use in flowing traffic
MoProVe is approved for road use and can therefore be used in flowing traffic. To record the traffic as well as details on the situation and environment, additional video cameras were installed.
The collected measurement data is set in the context of external parameters such as weather, traffic volume and route environment. The analysis is carried out using novel machine learning methods.
The post-processing of the measurement data makes it possible to extrapolate the system status to higher speeds or other environmental conditions. Accidents can be simulated by a combination of experiments and mathematical methods. This avoids dangerous driving maneuvers or driving situations that cannot be reproduced.
Objective identification
The project has already been successfully completed. On six motorcycle routes it was demonstrated that safety can be measured objectively and that MoProVe has great potential for accident prevention.
The results confirm that the Motorcycle Prove Vehicle is able to objectively identify road sections that are dangerous for motorcyclists. Hainz: “The results were compared with the actual accident situation.” The data from Road Safety Inspections showed that serious accidents had indeed often occurred at the identified danger points in the past. Plus, MoProVe makes it possible to forecast future danger points.
The project runs under the name viaMotorrad and was funded by the Road Safety Fund (VSF) of the Federal Ministry of Transport, Infrastructure and Technology (BMVIT).
Additional information:
Virtual traffic simulation to help prepare the road network for automated vehicles
Autonomous vehicles could reduce accidents by 22%.
