An experiment was carried out in April 2015 to assess subtasks in the driving laboratory (mockup ’1’) of the institute. The Bachelor Thesis of Andreas Janiak included parts of the experiment, scripting for DRT and occlusion calculations and the in-depth assessment of driving metrics. For this purpose, a metric similar to the MDEV (ISO 26022, 2010) was used. This lateral metric was also adapted to calculate a longitudinal MDEV. For this thesis, other metrics are used based on ‘drifting’ (see Section 2.3).
As can be seen by the following description, the setup is quite complex. In a former experiment (December 2014), this led to unpleasant large data drop-outs, due to different errors (e.g., unnoticed network disconnections). The setup, application, subtasks, proce-dures and checks were revisited and refined afterward. Therefore, the former results are incompatible and not used within this thesis and model.
The overall laboratory situation can be seen in Figure 3.1 and Figure 3.2. After expla-nations and training, the examiner was located behind the test subject. The driving scene is a car-following scenario similar to AAM and NHTSA guidelines, adapted to German Autobahn specifications and used in several experiments at the institute (e.g., Krause et al., 2015a). For more technical details, see also the description of Figure 2.3 (p. 22).
The driving simulation was SILAB 4 (WIVW GmbH, Veitshöchheim). The mockup has one screen (55") for the driving scene and a separated LC-panel for the speedometer. The mockup has a hi-fidelity steering wheel, an accelerator pedal and a brake pedal.
Figure 3.1.: Laboratory setup subtask experiment
Eye-tracking was achieved with the head-mounted Dikablis system (titan frame, 25 fps), with two USB-frame-grabbers and the Dikablis Recorder 2.5. The tablet to simulate an IVIS was a Sony Xperia Z Ultra (6.4"). The rotary knob was a BMW spare part (order
Figure 3.2.: Laboratory setup subtask experiment
number: 6944884) with 24 indents per rotation; internally modified with an Arduino Nano and a Bluetooth module to transmit signals to the Android tablet. This small side project (modified rotary knob) was released open source (Krause, 2015c). The rotary knob was mounted into the armrest and coupled to the tablet (IVIS) via Bluetooth.
PLATO spectacles (Translucent Technologies, CA) were used for the occlusion method.
To connect the occlusion goggles to the Ethernet, an Arduino with an Ethernet-shield was programmed and connected to the PLATO driving circuit via the western-plug extension port. So, the occlusion goggles transmitted the current state (open/close) to the tablet, which enabled the tablet to record the state in protocol files. Later shutter open times for each subtask were calculated based on these files. The Arduino paced a 1.5 s open, 1.5 s closed occlusion protocol. This small side project was also released open source (Krause, 2015b).
To assess the cognitive workload, the Detection Response Task (DRT) method was used in the variation: Tactile Detection Response Task (TDRT) (ISO/DIS 17488, 2014).
The DRT continuously presents a stimulus every 3–5 seconds and the test subject has to respond quickly with a button press. The reaction time or missed reaction holds information about the cognitive workload a test subject is currently exposed to. Higher workload prolongs the reaction times. In TDRT, the stimulus is given with a vibration motor. For the experiments, the open source Ethernet Arduino DRT was used (Krause and Conti, 2015). The driving circuit was built with an TIP120 transistor and one forward diode to reduce the driving voltage of the motor. The motor was a coin vibration motor (type number: 308-100) from Precision Microdrives (UK).
To connect the different systems locally, a Linksys WRT54GL (DD-WRT) Wifi-router was used and not linked to other networks. The data traffic for the nodes in this sepa-rated local network was low. Most connections used the connection-oriented, potential slower (non-realtime) TCP instead of the stateless, fast UDP. It can be assumed that the non-realtime behavior of, e.g., the Android application itself is more severe than potential network latencies in the intentionally small and separated Local Area Network. The differ-ent connections for the differdiffer-ent measuremdiffer-ent methods are summarized (see Figure 3.3):
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∙ During occlusion measurements, the occlusion spectacles (Ethernet) transmitted their state to the tablet (WiFi), where the state was logged together with subtask performance.
∙ When driving the car-following task, the tablet (IVIS) sent subtask triggers to the eye-tracking system and the driving simulation. The required Dikablis format of triggers is mentioned in Section 3.2. In exchange, the eye-tracking systems sent back the current frame number of the recorded video file to the tablet. The tablet forwarded this information to the driving simulation. The recording of frame num-bers in the driving simulation can be seen as a fallback solution for synchronization.
During the experiments, a continuously increasing frame number (shown in the driving simulation administration panel) provided feedback to the examiner that the eye-tracking system is recording and connections are established and working.
In the Android app on the tablet, the subject number and the type of measurement are available, and are transmitted to the driving simulation for logging.
∙ In the TDRT trials, the same equipment as in car following is used. Moreover, the Ethernet-capable Arduino DRT sends the measured reaction times (or misses) to the driving simulation. These are logged together with the subtask triggers from the tablet in the driving simulation system to enable later assessment of reaction times during subtasks.