Automated driving promises great possibilities in traffic safety advancement, frequently assuming that human error is the main cause of accidents, and promising a significant decrease in road accidents through automation. However, this assumption is too simplistic and does not consider potential side effects and adaptations in the socio-technical system that traffic represents.
Thus, a differentiated analysis, including the understanding of road system mechanisms regarding accident development and accident avoidance, is required to avoid adverse automation surprises, which is currently lacking. This case study looked at a Resilience Engineering approach, using the functional resonance analysis method (FRAM) to reveal these mechanisms in an overtaking scenario a rural road to compare the contributions between the human driver and potential automation, in order to derive system design recommendations. Finally, this serves to demonstrate how FRAM can be used for a systemic function allocation for the driving task between humans and automation.
Thus, an in-depth FRAM model was developed for both agents based on document knowledge elicitation and observations and interviews in a driving simulator, which was validated by a focus group with peers. Further, the performance variabilities were identified by structured interviews with human drivers as well as automation experts and observations in the driving simulator. Then, the aggregation and propagation of variability were analysed focusing on the interaction and complexity in the system by a semi-quantitative approach combined with a Space-Time/Agency framework.
Since it is not sufficient to know only the theoretical mechanisms of the overtaking process, the next step is to create a WAD model using observations and interviews implemented in a driving simulator study which serves to update and enhance the WAI model into a more realistic overall model.
Here, a static driving simulator (see Figure 1) was used. The environment is simulated by three flat screens with a resolution of 4K covering the space from the left-side window to the right-side window of the car, which ensures a 120_ viewpoint in front. Additionally, the rear-view mirror is virtually displayed at the top of the centre screen. The side mirrors are displayed via two small monitors placed to the left and right of the subject.
The driver, seated on a default automobile seat that is adjustable in height and longitudinal direction, has a steering wheel for lateral control that can be adjusted along the axis, as well as an accelerator and brake pedal for longitudinal control. The use of a turn signal and shoulder view to the rear are not possible. Behind the steering wheel is a combination display that shows the engine speed and the current speed of the vehicle. Further, the driving simulator is equipped with automatic transmission and sound, consisting of engine, environmental, and vehicle noises that are reproduced via two speakers placed next to the pedals. During a test drive, the room was darkened to increase the immersion for the driver.
SILAB 6.0 of the Würzburg Institute for Traffic Sciences GmbH in Germany was used as the simulation software.
Figure 1 – Structure of the static driving simulator.
The information was used to build the FRAM model shown below in Figure 2.
Figure 2 – the FRAM model of the overtaking functions
Finally, design recommendations for managing performance variability were proposed in order to enhance system safety. The outcomes showed that the current automation strategy should focus on adaptive automation based on a human-automation collaboration, rather than full automation.
The study concluded that the FRAM analysis can support decision-makers in enhancing safety enriched by the identification of non-linear and complex risk.
Publication
Grabbe, N., Gales, A., Höcher, M., & Bengler, K. (2021). Functional resonance analysis in an overtaking situation in road traffic: comparing the performance variability mechanisms between human and automation. Safety, 8(1), 3. https://doi.org/10.1007/s10111-022-00701-7