Active safety systems for powered two wheelers (PTWs) are considered a key pillar to further reduce the number of accidents and thus of injured riders and fatalities. Enhanced awareness for the current riding situation is required to improve the performance of current systems as well as to enable new ones; this includes the detection of the rider’s intention – the action that is planned by the rider for the short-term future. The prediction of a continuous trajectory for the upcoming seconds of the ride is one way to express rider intention. Our work pursues the prediction of the PTW lateral dynamic state by means of a roll angle trajectory over the upcoming 4 s of riding. It thus considers the special vehicle dynamics characteristics of single-track vehicles that negotiate bends at a roll angle compared to cars. A deep learning (DL) prediction model that is based on a Long-Short Term Memory (LSTM) layer is optimized and trained for this task using a broad on-road riding dataset that focuses on the rural road environment. Inputs to the prediction model are PTW internal signals only, that are measurements of vehicle dynamics, rider inputs, and rider behavior. The latter two groups of signals are non-common for current series production PTWs and were especially added to our test bike before gathering the riding data set. The prediction performance of the optimized DL model is compared to a simple heuristic algorithm using multiple metrics in the roll angle and position trajectory domain. Evaluation on a representative test data set shows a significantly improved detection of rider intention by the DL model in all metrics. Reasonable lateral trajectory accuracy is achieved for at least 2 s of the total 4 s prediction horizon in 99 % of all test cases, given the chosen model architecture and input features. Furthermore, the feature importance of the especially added non-common measurement signals of steering and rider behavior is investigated in an ablation study. It reveals the importance of steering signals in the first second of the prediction horizon whereas the rider behavior signals aid trajectory prediction performance for up to 2.5 s.

This project examines the practices of six Norwegian municipalities, which in a va-riety of ways are trying to enable mentally disabled residents to build or buy their own homes. The survey has confirmed some previous findings: the financing model operat-ed by the Norwegian State Housing Bank makes it possible for many mentally disabled persons to purchase homes on the ordinary housing market. However, even though private developers are without exception friendly, this appears to be a complicated and resource-demanding process. The cases show how municipalities can enable better processes in different ways. Leaving home can be a major upheaval both for young mentally disabled people and their parents, entailing changes in the young person’s re-sponsibilities, personal economy, and sense of independence. The overall objective is achieving inclusivity. Some of the homes are quite ordinary and are located among other similar homes, but our findings indicate that to date, residents experience little or no positive interaction with their other neighbors. Natural meeting places and commu-nal areas dedicated to entire neighborhoods occur only rarely in new housing projects. If we are to meet the objectives of inclusivity and participation, we have to expand our focus and not only look at the homes but also at the neighborhood in which the resi-dents shall be included.

In the past years, cargo bicycles in different configurations have gained popularity for many use cases. Their configurations differ substantially. Single-track cargo bicycles and their kinematics are linked closely to conventional bicycles. The kinematics of inverted tricycles, so-called tadpole trikes, are different. In this work, we model the motion for such a tadpole tricycle with articulated steering in order to predict the kinematic potential of such a vehicle. A single-track model for vehicle kinematics is implemented and compared to a planar model that incorporates a term for the lean (or roll) angle. To do so, the connection between steering and lean angle is calculated by the help of wheel flop. This is validated by inversing the modelling process and optimizing the geometrical approach function with the help of naturalistic cycling studies. The tricycle used for this study is measured experimentally in order to find the parameters for the model. It is then equipped with measuring devices and we present our instrumented tadpole cargo tricycle. By the help of it, we validate the two presented kinematic models for the motion of the tadpole tricycle with real world measuring data for given driving scenarios. These models are impinged with data from our experimental driving maneuvers. It is shown that our derived kinematic models hold reasonably well against the measurements for short term predictions during driving scenarios below the limits of driving dynamics. For the performed test scenarios, we compare the experimentally measured trajectory with the simulated ones and quantify the error. It is shown that a planar model that incorporates lean performs minimally better compared to a single-track model. We discuss model limitations as well as potential inaccuracies caused by the used measuring devices on our instrumented cargo tricycle. With the help of the kinematic models, motion prediction of tadpole cargo tricycles can be undertaken. The range for which the implemented planar models are considered to be valid is depicted by the range of forward speeds until the liftoff condition. For motion prediction, a single-track model is considered feasible, as the more complicated planar model with lean does not substantially outperform it. For maneuvers at the limits of driving dynamics, more sophisticated dynamic models are needed, as the simple kinematic models presented in this work are not sufficient for this kind of tasks.

This contribution presents an analysis of the vertical tyre stiffness of 20” bicycle tyres as usually mounted on bicycle carriers for the transport of children. The current research contributes to the science on bicycle comfort with the focus on the next generation cyclists. Two different methods to measure vertical or radial tyre stiffness of bicycle tyres are presented – a dynamic approach on a dynamic press and a static approach. Parameters modified are tyre inflation pressure and vertical load in the static experiment. In the dynamic experiment additionally dynamic load and frequency are varied. The dynamic experiments are performed on two different tyres. The same tyres are also used for the static experiments and completed with a third tyre, which is a clincher version of the narrow foldable tyre. The tyres are made for 406mm rim diameter as usually for bicycle carriers since the comfort of children in bicycle transportation is the larger scope behind the experiments. The main findings are as follows: • The stiffness of the tyres is in a range of 31 N / mm to 147 N / mm. It must be considered that values below 50 N /mm are related to extremely low inflation pressure that probably do not work reliably because the rim will puncture the tube. • Tyre inflation pressure is the main factor that controls the vertical stiffness. • Type of tyre (balloon vs. narrow tyre) hardly affects the stiffness. • The dynamic stiffness at 1 Hz is slightly higher than the static stiffness. • With increasing excitation frequency the stiffness increases, however, this effect is non-linear and varies between 3.7% at high pressure in the narrow tyre and up to 20% at low pressure in the balloon tyre. • Similarly, there is a trend to higher stiffness with increasing vertical load in a magnitude of 20% increase.

In mobile environments where recording devices and subjects are in motion, integrating data collected from multiple devices requires precise location and time information. Given that high-precision satellite positioning technology provides centimeter-level accuracy and that movement speeds in mobile environments are around several 10 m/s, the required time accuracy is under 1 millisecond. However, achieving this time accuracy with commonly used devices is not typically feasible. This paper describes a basic architecture to realize time synchronization with less than one millisecond error with an independent recorder using high-precision timing pulses (1-PPS signal) output by a GNSS receiver. Next, we propose a method to precisely identify the image capture time using an optical beacon combining multiple point light sources called GNSS Clock Beacon (GCB). The time of image capture can be determined from GCB images with an accuracy less than or equal to the exposure duration. Finally, we describe an example implementation of a mobile recording system that can be mounted on a motorcycle, which can record time-synchronized data and video with high accuracy using multiple data loggers and video equipment.

An increasing number of researchers have started to focus on motorcyclists due to their increased risk of accidents, their vulnerability, and the limited possibilities to enhance their passive safety. An important tool hereby is the use of motorcycle simulators. They can be used to evaluate human-machine interfaces, design future advanced rider assistance systems like forward collision warnings, or determine an optimal ergonomic position to reduce mental loads and stress. However, compared to the automotive sector, only a few simulators exist, but they differ greatly. To the author's knowledge, no recent systematic overview of the existing motorcycle simulators exists. Therefore, this literature review provides an overview of the current state-of-the-art powered two-wheeler simulators based on 151 publications. The review describes 13 simulators in detail, including their prioritized research areas, conducted studies, strengths and limitations, development over the years, and validation. A tabular overview of the simulators can be found in the supplementary materials or requested.

The purpose of this paper is to review research related to motorcycling conducted in postwar Japan, a country that was somewhat closed both linguistically and regionally. After World War II, many aeronautical engineers worldwide lost their jobs and moved on to other fields of study, especially in Japan, where aeronautical engineer jobs, including research, were banned and many aeronautical engineers shifted their research focus on transportation machinery, especially automotive engineering. Against this background, Japanese two-wheeled vehicles-related research has developed in its own unique way, while retaining a strong influence from aeronautical engineering. Because of the wide base of research on motorcycle kinematics, we first presented the literature for each study in the same line of research together. They are summarized in the following four areas： (1) Experimental studies dealing with motorcycle motion and problem extraction. (2) Research dealing with theoretical aspects such as the construction of equations of motion to solve experimental problems and to look at motion from the aspect of characteristics estimation. (3) Research on various human-related issues, such as human control behavior modeling, vibration characteristics of the human body, HMI, and so on. (4) Research on motorcycles as control objects and research focused on control systems. Although there are many studies that straddle these two categories, they were generally grouped into one or the other.

In this article, an improvement in a system for position measurement of a motorcycle is presented. Position measurement of a mo- torcycle when running presents a difficult problem because loading of measurement equipment would cause changes in the vehicle’s mass and its moment of inertia. Therefore, this paper proposes a novel measurement method that uses omnidirectional cameras to acquire angles relative to fixed camera positions. The method is based on a general stereoscopic positioning approach. The results of previous research have shown that a simple measurement method using image processing techniques could be ap- plied to the position measurement of a motorcycle when running on a figure-of-eight-shaped course around two omnidirectional cameras. The main weakness of this method using two omnidirectional cameras is the large error that occurs near the camera baseline (i.e., the line connecting the two cameras), particularly in the baseline direction. To improve position measurement preci- sion, the author has added two more omnidirectional cameras to the system. Running tests of the proposed system using a real mo- torcycle were executed on a paved area. The experimental results showed that the proposed measurement method is sufficiently accurate to allow it to check the locus of a motorcycle running on a figure-of-eight-shaped course.

Optimizing the performance of racing motorcycles is a central goal for competition teams. The necessity to ensure driver stability and a good level of grip in the widest possible range of riding conditions makes it necessary for tires to work in the right temperature window, capable of ensuring the highest interaction force between tire and road. Specifically, the internal temperature of the tire is a parameter that can be difficult to measure and control but has a significant impact on motorcycle performance and, also, on driver stability. Deepening knowledge of internal tire temperature in racing motorcycles can improve performance optimization on the track and finding the right motorcycle setup. In this work, a physical thermal model is adopted for an activity concerning the development of a moto-student vehicle, to predict the racing motorcycle setup allowing the tire to work in a thermal window that optimizes grip and maximizes tire life. More in detail, a focus has been placed on the effects of the motorcycle’s wheelbase and pivot height variations on internal tire temperatures. Indeed, the stability and handling of the vehicle are highly dependent on the geometric properties of the chassis. Several values of such quantities have been tested in a properly implemented vehicle model developed in the “VI-BikeRealTime” environment, validated by outdoor tests, able to provide forces acting on the tires, slip indices, and speeds, needed by the thermal model as inputs. Through the analysis of the internal temperatures calculated by the model, reached by the various layers of the tire, it has been possible to investigate which of the simulated conditions cause a too-fast thermal activation of the tire and which of them can avoid overheating and underheating phenomena. Lately, this research has delved into the correlation between motorcycle riders' paths and temperature fluctuations with the aim of comprehending how minor alterations in routine maneuvers may influence tire energy activation, particularly in the context of racing and qualifying conditions.

To develop advanced motorcycle assistance systems, the focus is shifting towards the rider's abilities. A model in (Scherer et al. 2022) predicts motorcycle dynamics influenced by riders without specific rider or vehicle parameters. It employs mathematical functions to describe speed and roll angle changes, revealing differences among riders. Unlike previous stochastic approaches, this model allows clear interpretation of measurement data with rider-specific parameters like correction amplitudes and trends, aiding critical maneuver identification. The paper investigates applying this rider model to real traffic data. For this purpose, three riders (two experienced frequent riders and one inexperienced infrequent rider) on two different vehicles (Honda CBF 1000 and BMW K1200R Sport) were recorded and examined on a sample basis using a validated low-cost measurement technique with a total amount of n = 40 measurements. Taking into account evaluation curves suitable for proving the methodology, two consecutive country road curves were selected with a respective change in direction (equivalent to a yaw angle change of the vehicle between entering and exiting the curve) of approx. 180°. These were each driven through 5 times by all three riders under constant conditions in good, summer weather and road conditions. In addition, one of the riders drove through them in wintry and less than optimal road conditions at the beginning of the season. Initial findings assess the model's transferability to real traffic. The investigation results show its applicability, with rider-specific riding styles and parameterization functions, as well as the need to repeat the study with a large number of samples. The model accurately predicts future positions, with over 85% of maneuvers having less than a 2% lateral deviation. This demonstrates applicability under real conditions, confirming its efficacy beyond the closed terrain test in (Scherer et. al., 2022). In the future, this model will enable rider-dependent trajectory predictions with uncertainty intervals in real traffic situations.