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This work aims to create a test bed for analyzing the performance characteristics of any electric vehicle motor via simulation and emulation. Given test bed can emulate the load torque which an electric motor may experience in actual drive conditions. The core part of the test bed comprises of a Permanent Magnet DC (PMDC) generator which is controlled by a special chopper circuit to emulate the mechanical characteristics of any electric vehicle. The vehicle's mechanical characteristics at various speeds is emulated by controlling the special chopper circuit connected to the DC generator which in turn controls torque the test motor will experience as both are connected via same shaft. The mass of the electric vehicle, frictional coefficients, tire radius and other functional elements are input parameters that can be entered by the user. Using these parameters, the load torque that an electric vehicle motor should experience is calculated from electrical vehicle simulation model based on the equations of various load forces acting on the vehicle for a given speed. According to the calculated torque value, chopper circuit of DC generator is controlled to emulate the torque for a given speed. The system can also be fed with a variable drive cycle. To test the effectiveness of test bed, DC motor drive is coupled to the test bed and the performance verified using MATLAB/Simulink.

Driving is a complex task concurrently drawing on multiple cognitive resources. Yet, there is a lack of studies investigating interactions at the brain-level among different driving subtasks in dual-tasking. This study investigates how visuospatial attentional demands related to increased driving difficulty interacts with different working memory load (WML) levels at the brain level. Using multichannel whole-head high density functional near-infrared spectroscopy (fNIRS) brain activation measurements, we aimed to predict driving difficulty level, both separate for each WML level and with a combined model. Participants drove for approximately 60 min on a highway with concurrent traffic in a virtual reality driving simulator. In half of the time, the course led through a construction site with reduced lane width, increasing visuospatial attentional demands. Concurrently, participants performed a modified version of the n-back task with five different WML levels (from 0-back up to 4-back), forcing them to continuously update, memorize, and recall the sequence of the previous ‘n’ speed signs and adjust their speed accordingly. Using multivariate logistic ridge regression, we were able to correctly predict driving difficulty in 75.0% of the signal samples (1.955 Hz sampling rate) across 15 participants in an out-of-sample cross-validation of classifiers trained on fNIRS data separately for each WML level. There was a significant effect of the WML level on the driving difficulty prediction accuracies [range 62.2–87.1%; χ2(4) = 19.9, p < 0.001, Kruskal–Wallis H test] with highest prediction rates at intermediate WML levels. On the contrary, training one classifier on fNIRS data across all WML levels severely degraded prediction performance (mean accuracy of 46.8%). Activation changes in the bilateral dorsal frontal (putative BA46), bilateral inferior parietal (putative BA39), and left superior parietal (putative BA7) areas were most predictive to increased driving difficulty. These discriminative patterns diminished at higher WML levels indicating that visuospatial attentional demands and WML involve interacting underlying brain processes. The changing pattern of driving difficulty related brain areas across WML levels could indicate potential changes in the multitasking strategy with level of WML demand, in line with the multiple resource theory.

Driving is a complex and cognitively demanding task. It is essential to assess the cognitive state of the driver in order to design cognitive technical systems that can adapt to different driver cognitive states. Our research attempts to assess these states using functional Near Infrared Spectroscopy (fNIRS) by measuring brain activity in a virtual reality driving simulator. However, the fNIRS brain activation measurements could be influenced by muscle activity and we wanted to investigate this phenomenon. For this, we designed a paradigm with two conditions (listen, teeth clench) which show a significant contrast in the influence of muscle activity. We observed that the muscle hemodynamic response can show a higher magnitude of signal change compared to brain hemodynamic response. The muscle hemodynamic response showed an increase in deoxygenated hemoglobin (HbR) whereas the brain hemodynamic response showed a decrease in HbR. Moreover, the dynamics of the brain and muscle hemodynamic response differed. The brain response showed the same latency for oxygenated hemoglobin (HbO) and HbR while the muscle HbR response had a slower latency compared to HbO. We concluded that the fNIRS brain activation measurements could indeed be influenced by muscle activity. We were also able to determine some characteristics of the muscle hemodynamic response.

Introduction and Aim: Driving is a goal-directed task. During traffic, blocking obstacles occur often eliciting driver frustration, and may result in more risky driving including speeding and aggressive behavior towards other traffic road participants. We envision to use ‘adaptive automation’ [1] to design driver assistance systems that prevent those maladaptive driving behavior by supporting the driver to reduce the current frustration level or increasing the level of automation. For this, it is essential that we can assess driver frustration already at an early stage. As affective states come along with a change in cognitive appraisal and a subjectively experienced feeling, brain activity seems to be a promising indicator of frustration. Thus, we aim to investigate if brain activation fNIRS measurements could be used to assess realistically occurring driver frustration. Methods: Thirteen volunteers participated in a driving simulator study, in which frustration was induced through a combination of time pressure and blocking events. A cover story asked the participants to imagine that they had to deliver a parcel to a client within six minutes with the incentive of gaining 2 € per successful delivery. In the six frustrating scenarios (FRUST), the drives were blocked by heavy traffic, while in the six non-frustrating scenarios (NOFRUST), participants could drive mostly unblocked. FNIRS data were recorded from the frontal and parietal cortices using a 32-channel neuroNIRX-system during the course of the whole experiment. Results: We subjected the individual fNIRS data to an SPM-based GLM analysis which revealed relative concentration increases of oxy-hemoglobin (HbO) and decreases of deoxy-hemoglobin (HbR) during FRUST compared to NOFRUST drives. These changes were found bilaterally in ventrolateral frontal and temporo-occipital channels in the group analysis as well as at the individual level (see Fig. 1 for group level and an example of an individual analysis. Note the much higher t-values in the individual analysis). Discussion & Conclusion: Our results indicate that brain activity patterns of frustrated drivers are discernible from non-frustrated drivers using fNIRS. The revealed brain areas displaying higher activity in the frustrating drives are in line with the relatively scarce literature on frustration-related neuroimaging results and seem to be related to cognitive appraisal, impulse control and emotion regulation processes. Since frustration is a multi-component phenomenon, it could be helpful to measure behavioral and other physiological measures in combination with fNIRS data to acquire a robust assessment of frustration. Acknowledgements: This work was funded by a grant of the Volkswagen Foundation and the Ministry of Science and Culture of Lower Saxony to the Research Centre on Critical Systems Engineering for Socio-Technical Systems. Klas Ihme and Anirudh Unni contributed equally. References [1] Parasuraman, R. (1987). Human-computer monitoring. Human Factors, 29, 695-706

Introduction and Aim: We envision that driver assistive systems which adapt their functionality to the driver’s cognitive state could be a promising approach to reduce road accidents due to human errors [1]. Workload is an important cognitive state because a cognitive overload or underload results in a decrease in human performance which may result in fatal incidents while driving. Here, we investigate if it’s possible to predict variations of cognitive workload levels (WL) from fNIRS brain activation measurements while driving to inform adaptive assistive systems in future cars. Methods: In our study, we implemented the n-back working memory task with several continuously changing load levels as a speed regulation task into a realistic driving simulation in order to control and manipulate cognitive workload. We introduced five different workload levels (i.e. 0-back to 4-back) and speed signs every 20 seconds while the participant was driving on a highway with concurrent traffic. Depending on the current n-back task, the participant was supposed to remember the previous ‘n’ speed sequences and adjust his speed accordingly. A detailed explanation for the n-back experimental paradigm can be found in [2]. FNIRS data were recorded from the frontal and parietal cortices using a 32-channel neuroNIRX-system from five participants during the course of the whole experiment which lasted for around 30 minutes. Results: We used the multivariate linear regression approach by combining fNIRS data from all channels to predict WL using the elastic net regularization model which combines the L1 and L2 penalties of the lasso and ridge regression techniques to get a continuous estimate of workload over time. Fig.1 depicts a plot of WL induced by the n-back task (red curve) and the WL predicted by the model (blue curve) for an example participant. The correlation between the two curves is almost 0.8 for a 10-fold cross-validation. For the remaining four participants, we achieved a correlation between 0.6 and 0.8 which were all statistically significant (p < 0.05). Fig.1: Ten-fold cross-validated prediction of workload from deoxyhemoglobin fNIRS measurements using multivariate regression analysis in an example participant Discussion & Conclusion: It can be seen from Fig.1 that the predicted WL more or less follows the induced WL and could be used to predict if the WL is either increasing or decreasing. There are certain regions in the blue curve where the model seems to over- or underestimate WL which may be due to the incomplete model which currently neglects WL imposed by the concurrent driving task in the changing traffic situations. As a next step, we plan to introduce some non-linearity into the workload model to get much better prediction rates. Acknowledgements: This work was funded by a grant of the Volkswagen Foundation and the Ministry of Science and Culture of Lower Saxony to the Research Centre on Critical Systems Engineering for Socio-Technical Systems. Anirudh Unni and Klas Ihme contributed equally. References [1] Parasuraman, R. (1987). Human-computer monitoring. Human Factors, 29, 695-706 [2] Unni et al. (2015). Brain activity measured with fNIRS for the prediction of cognitive workload. 6th IEEE Conference on Cognitive Infocommunications, 349-354

Adapting an automation system to the workload level of a human operator can be beneficial in many situations, e.g. at industrial workplaces or in safety-critical situations like driving and flying an aircraft. However, this requires real-time assessment of workload. We present a model-based approach for online simulation and assessment of cognitive workload, based on analysing the activities of a cognitive architecture during simulation. A driving simulator experiment was used to evaluate the approach. The cognitive workload of participants was manipulated with a variant of the n-back task as secondary task that parametrically varies memory workload. A virtual driver model was created using the cognitive architecture. The model was simulated in the same situations as the human drivers. The activities of the cognitive architecture as indicator of cognitive workload increased with increasing difficulty of the n-back task. To relate model workload to human driver workload, we compared it with neurophysiological workload measures based on functional near-infrared spectroscopy (fNIRS) brain activation as a complementary measure.

Functional near-infrared spectroscopy (fNIRS) is a versatile imagining modality whose popularity is increasing exponentially in the neuroimaging society. Our research attempts to quantify workload in a natural driving scenario with multiple parallel tasks using fNIRS. Nine young adults participated in this study where they drove in a driving simulator for a period of 100 minutes while we continuously recorded fNIRS data. We used an n-back task to induce different workload levels forcing the participants to remember the previous one, two, three or four speed signs and adjust their speed accordingly while they interact with traffic in the virtual reality driving simulator scenario. Our results indicate that measuring the hemodynamic responses from the bilateral prefrontal cortex (PFC) can be used reliably to quantify cognitive workload levels even in more complex naturalistic tasks.