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.

For the development of new types of assistance systems for motorbikes, the focus is increasingly on the rider himself. Warning functions or interventions into the current trajectory to avoid accidents require, in addition to the consideration of driving dynamic limits, an exact description of the riding abilities of the respective person, or the typical behaviour of the rider (Prokop, 2017). In Scherer et al (Scherer, 2022), a model for the prediction of motorbike driving dynamics data taking into account rider influence is presented on the basis of measurement data on a closed-off test track. The modelling assumes neither rider nor vehicle specific parameters to be known. For the modelling, parameterisable mathematical functions are used to describe the speed- and roll angle progression. Significant differences between different riders are thus visible in the test track investigations. In contrast to previous scientific approaches with stochastic evaluation of riding ability f.e. in (Magiera, 2020), with the model from (Scherer, 2022) it is possible to interpret measurement data using clear rider-specific parameters, to approximate their course and to compare them across riders. Example parameters are correction amplitudes, trends and limit values. Furthermore, the parameters enable a reliable identification of critical manoeuvres. Based on the describable roll angle and speed, an approach for calculating the future vehicle position was developed in (Scherer, 2022). The calculation is based on quasi-stationary cornering, which is extended by dynamic domains through correction factors. Furthermore, the correction factors allow an estimation of the riding style and additionally represent the individuality of the rider. In the current paper, the applicability of this rider model to real traffic data is investigated. For this purpose, individual so-called evaluation curves are defined as examples, which are located on typical motorbike routes in Germany. Selected riders ride the same route several times, taking into account as constant as possible environmental conditions. Individuals repeat this investigation several times at intervals of several months and with different vehicles in order to make influences due to so-called seasonal effects or vehicle-specific behaviour investigable. In this paper, initial findings are presented regarding the influence on, or change in, riders' behaviour due to the choice of vehicle or the time of the measurement. An assessment is given of the extent to which the model of rider behaviour developed under laboratory conditions (Scherer, 2022) can be transferred to real traffic situations. This results in an estimation of the expected accuracy of the trajectory prediction under real conditions. As a result of this investigation, the applicability of the rider model to real traffic data is shown. Figure 1 (above) shows an example of the roll angles and speed curves of three different riders passing through an evaluation curve six times repeatedly with the same vehicle. Here, the repeatability of one person's riding style can already be seen, as well as differences from other riders. Figure 1 (down) shows the parameterisation generated from the measurement data shown. Here, as well, a rider specificity is visible in the coefficients of the parameterisation functions. In particular, rider and curve specific influences are evident in the roll angle coefficients. The application of the model to estimate the future position shows that an application to real driving data is possible. As a quality criterion for the evaluation of the overall accuracy, the lateral offset between the calculated and the measured trajectory is considered as a function of the distance travelled. The overall error between the measured data investigated in this paper and the position resulting from the estimation is in more than 85% of the manoeuvres with a maximum lateral deviation of less than 2%. Compared to the results presented in (Scherer, 2022) based on the measurement data from the test person study on closed terrain with an error of less than 1.5 %, this paper provides evidence of the applicability of the trajectory prediction under real conditions. With the model presented here, rider-dependent trajectory predictions through a section of road ahead, taking into account an uncertainty interval, will also be possible in real traffic situations in the future.