User studies by driving simulators in the era of automated vehicle

In order to increase safety in the road and improve the user experience in the vehicle, the user studies have been conducted by researchers and practitioners in the automobile industry over the decades. Also, over time, the technology and design inside the car have changed and are leading to a faster, safer, and more comfortable user experience in driving, thanks to the results gained from the user studies. On the other side, the boosting automated driving technology gives new challenges to user studies in the validation of new technologies from the user’s perspective, improving the acceptance, employing the right usage, and so on. Laboratory driving simulation becomes one of the main methods for user studies because of its safety, ease of control, and precision in the scene restoration. In this paper, a typical fixed-base driving simulator will be introduced with a user interaction model in order to help the researchers to define the user study scope in each vehicle automation level and even predict the potential user study issues in the future autonomous vehicle technology and scenario. The strategy in the current study is to treat the different levels of automation in vehicles differently. Three case studies are provided accordingly from the low-automated to semi-autonomous driving and eventually fully autonomous driving. Each one addresses some of the critical points that should be paid attention to, in the user studies of the corresponding automation level, applying the previous model. In the low automation condition, the case study showed the effectiveness of the proposed method in the verification of olfactory modality interaction in the driver’s attention maintenance. The case study in the semi-automation condition demonstrated the capacity of the current method of capturing the user’s behavior changes in the take-over task, which is the most critical scenario in conditional autonomous driving. And the last case study showed the possibility to conduct comfort-related user studies in the full automation condition using the method, by monitoring the cognitive workload of users under different autonomous driving styles