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This document is intended to give an overview of the validation of the human subject study, conducted in the driving simulator of the Chair of Traffic and Transportation Psychology (Verkehrspsychologie – VPSY) of the Technische Universität Dresden (TUD), as well of the validation of the stochastic traffic simulation developed in the AutoDrive project by the Chair of Automotive Engineering (Lehrstuhl Kraftfahrzeugtechnik – LKT) of TUD. Furthermore, the evaluation process of a C-AEB (Cooperative-Automatic Emergency Brake) system is demonstrated. The main purpose was to compare the driving behaviour of the study participants and the driving behaviour of the agents in the traffic simulation with real world data. Based on relevant literature, a validation concept was designed and real world data was collected using drones and stationary cameras. By means of qualitative and quantitative analysis it could be shown, that the driving simulator study shows realistic driving behaviour in terms of mean speed. Moreover, the stochastic traffic simulation already reflects reality in terms of mean and maximum speed of the agents. Finally, the performed evaluation proofed the suitability of the developed stochastic simulation for the assessment process. Furthermore, it could be shown, that a C-AEB system improves the traffic safety for the chosen test-scenarios.

Within the AutoDrive project, openPASS is used to develop a cognitive stochastic traffic flow simulation for urban intersections and highway scenarios, which are described in deliverable D1.14. The deliverable D2.16 includes the customizations of the framework openPASS that are required to provide a basis for the development and implementation of the driver behavior model and the evaluated safety function. The trial design for the evaluation of the safety functions is described. Furthermore, the design of the driver behavior study is introduced to parameterize and validate the underlying driver behavior model.

This document is intended to give an overview of the human subject study in a driving simulator that was conducted by the Chair of Traffic and Transportation Psychology (Verkehrspsychologie – VPSY) of the Technische Universität Dresden (TUD) to provide the Chair of Automotive Engineering (Lehrstuhl Kraftfahrzeugtechnik – LKT) of TUD with the necessary input for the validation of a stochastic traffic simulation, especially for the parameterization, consolidation, and validation of driver behaviour models. VPSY planned, conducted, and analysed a driving simulator study. The main purpose of the study was to analyse driving behaviour and gaze data at intersections in urban areas. Based on relevant literature, a simulated driving environment was created, in which a sample of drivers passed a variety of intersections. Considering different driver states, driving tasks, and traffic situations, the collected data provide detailed information about human gaze and driving behaviour when approaching and crossing intersections. The collected data was transferred to LKT for the development of the stochastic traffic simulation.

As part of the AutoDrive project, OpenPASS is used to develop a cognitive-stochastic traffic flow simulation for urban intersection scenarios described in deliverable D1.14. The deliverable D4.20 is about the design of the modules for the stochastic traffic simulation. This initially includes an examination of the existing traffic simulations described in chapter 2. Subsequently, the underlying tasks of the driver when crossing an intersection are explained. The main part contains the design of the cognitive structure of the road user (chapter 4.2) and the development of the cognitive behaviour modules (chapter 4.3).

As part of the AutoDrive project, the OpenPASS framework is used to develop a cognitive-stochastic traffic flow simulation for urban intersection scenarios described in deliverable D1.14. This framework was adapted and further developed. The deliverable D5.13 deals with the construction of the stochastic traffic simulation. At this point of the process, the theoretical design aspects of D4.20 are implemented. D5.13 explains the operating principles of the different modules. This includes the foundations, boundary conditions, and mathematical theory of the traffic simulation.

Motorradfahrer gehören zu den am meisten gefährdeten Verkehrsteilnehmern im Straßenverkehr. Häufig ist die Unfallursache ein Kontrollverlust auf Landstraßen, der durch Ausnutzung des physikalischen Potenzials in Form von größeren Schräglagenwinkeln vermieden werden könnte. Gleichzeitig sind in der Realität gefahrene Schräglagen über eine größere Gruppe von Fahrern und eine längere Strecke unbekannt, was vor allem auf die erforderliche spezielle Messtechnik zurückzuführen ist. Der Schwerpunkt liegt daher auf der Entwicklung eines kostengünstigen Messverfahrens zur Messung der Schräglagenwinkel von Motorrädern. Smartphones zeichnen sich in der Regel durch integrierte Inertialsensoren aus, die für die Erfassung der Fahrdynamik von Motorrädern geeignet sind. Mit Hilfe einer auf die Anforderungen zugeschnittenen Smartphone-App zur Erfassung von Messdaten auf dem Motorrad werden die Daten der Sensoren aufgezeichnet. Anschließend werden mittels einer Offline-Auswertung die Drehwinkel zwischen dem Smartphone und dem Motorrad-Koordinatensystem bestimmt, die inertialen Messdaten transformiert und der Schräglagenwinkel berechnet. Ein wesentlicher Bestandteil ist die Validierung der entwickelten Messkette durch einen Vergleich der Ergebnisse mit einem hochpräzisen Messsystem. Dieser wurde auf verschiedenen Strecken zur Bestimmung der Datenqualität durchgeführt. Als Machbarkeitsstudie diente eine Probandenstudie, die die Praxistauglichkeit der Messkette bestätigte. Die Studienergebnisse werden zusätzlich auszugsweise dargestellt und diskutiert. Die erfolgreiche Validierung auf verschiedenen Strecken, die Praxistauglichkeit der Datenerfassung und die Genauigkeit des Messsystems ermutigen dazu, die Smartphone-App auf ein größeres Panel von Testpersonen auszurollen und damit Daten über ein größeres Fahrerkollektiv zu erheben.:Introduction, State of Research, Methods, Measurement Chain Verification, Results and Discussion, Conclusion Motorcyclists are among the most vulnerable road users in road traffic. Often, the cause of accidents is a loss of control on rural roads which could be averted by making use of the physical potential in terms of larger lean angles. At the same time, in reality driven lean angles over a larger group of riders and a longer route are unknown which is mainly due to the special measuring technology required. The focus is therefore on the development of a low-cost measurement method for measuring the lean angles of motorcycles. Smartphones are usually characterized by integrated inertial sensors, which are suitable for the acquisition of motorcycle driving dynamics. Employing a smartphone app tailored to the requirements for collecting measurement data on the motorcycle, the data of the sensors are recorded. During the offline evaluation, the rotation angles between the smartphone and the motorcycle coordinate system are determined, the inertial measurement data are transformed and the roll angle is calculated. An essential part is the alignment of the developed measurement chain with a high-precision measurement system. This was carried out on different routes and thus the data quality was determined. As a feasibility study, a test person study with several participants was carried out, which confirmed the practical suitability of the measurement chain. Hence, the study outcomes are briefly shown and discussed. The successful validation on different routes, the practical suitability of the data acquisition and the accuracy of the measurement system encourage to roll out the smartphone app to a larger panel of test persons and thus to collect data on a larger driver collective.:Introduction, State of Research, Methods, Measurement Chain Verification, Results and Discussion, Conclusion

Objective Healthy subjects can learn to use cognitive-emotional strategies to suppress their spinal nociception, quantified by the nociceptive flexor reflex (RIII reflex), when given visual RIII feedback. This likely reflects learned activation of descending pain inhibition. Here, we investigated if training success persists 4 and 8 months after the end of RIII feedback training, and if transfer (RIII suppression without feedback) is possible. Methods 18 and 8 subjects who had successfully completed feedback training were investigated 4 and 8 months later. Results At 4 months, RIII suppression during feedback and transfer was similar to that achieved at the final RIII feedback training session (to 50 ± 22%, 53 ± 21% and 52 ± 21% of baseline, all differences n.s.). At 8 months, RIII suppression was somewhat (not significantly) smaller in the feedback run (to 64 ± 17%) compared to the final training session (56 ± 19%). Feedback and transfer runs were similar (to 64 ± 17% vs. 68 ± 24%, n.s.). Concomitant reductions in pain intensity ratings were stable at 4 and 8 months. Conclusions RIII feedback training success was completely maintained after 4 months, and somewhat attenuated 8 months after training. Transfer was successful. Significance These results are an important pre-requisite for application of RIII feedback training in the context of clinical pain.

The requirements for a driver driving through a traffic situation increase with the complexity of the infrastructure, the number of participants, their possible actions and the environmental conditions. Thereby, the human being as the controller of the vehicle is normally able to analyze complex situations and to act accordingly.

We have recently shown that subjects can learn to use cognitive-emotional strategies to suppress their spinal nociceptive flexor reflex (RIII reflex) under visual RIII feedback and proposed that this reflects learned activation of descending pain inhibition. Here, we investigated whether learned RIII suppression also affects supraspinal nociception and whether previous relaxation training increases success. Subjects were trained over 3 sessions to reduce their RIII size by self-selected cognitive-emotional strategies. Two groups received true RIII feedback (with or without previous relaxation training) and a sham group received false feedback (15 subjects per group). RIII reflexes, late somatosensory evoked potentials (SEPs), and F-waves were recorded and pain intensity ratings collected. Both true feedback groups achieved significant (P < 0.01) but similar RIII suppression (to 79% ± 21% and 70% ± 17% of control). Somatosensory evoked potential amplitude (100-150 milliseconds after stimulation) was reduced in parallel with the RIII size (r = 0.57, P < 0.01). In the sham group, neither RIII size nor SEP amplitude was significantly reduced during feedback training. Pain intensity was significantly reduced in all 3 groups and also correlated with RIII reduction (r = 0.44, P < 0.01). F-wave parameters were not affected during RIII suppression. The present results show that learned RIII suppression also affects supraspinal nociception as quantified by SEPs, although effects on pain ratings were less clear. Lower motor neuron excitability as quantified by F-waves was not affected. Previous relaxation training did not significantly improve RIII feedback training success.

Background Descending pain modulatory systems control transmission of nociceptive information at the spinal level, and their activity can be modified by cognitive and emotional processes. Thus, it may be possible to learn using cognitive–emotional strategies to specifically target descending pathways in order to achieve pain reduction. Methods The present study used visual feedback of the nociceptive flexor reflex (RIII reflex) to train healthy subjects over three sessions to reduce their spinal nociception (RIII reflex size) by self-selected cognitive–emotional strategies. The study included two feedback groups (fixed vs. random stimulation intervals) and a control group without feedback (15 subjects each). Results While all three groups successfully reduced their RIII reflexes (p