CLLE, IRIT  – Toulouse University


driving automation, discomfort, drivenger, passenger, scenario

Although it is key to improving acceptability, there is sparse scientific literature on the experience of humans as passengers in partially automated cars. The first study introduced investigated the influence of road type, weather conditions, traffic congestion level, vehicle speed, and human factors (e.g., trust in automated cars) on passenger comfort in an automated car classified as Level 3 according to the Society of Automotive Engineers (SAE). Results showed that comfort was negatively affected by driving in downtown (vs. highway), heavy rain, and congested traffic. Interaction analyses showed that reducing the speed of the vehicle improved comfort in these two last conditions. Results also showed that the most comfortable participants had the higher level of trust in automated cars. This study suggests that optimizing comfort in automated cars should take account of both driving conditions and human profiles. Hence a personalization approach should be favored over a one-for-all.Hence, in a second study, we will investigate the benefits of adapting the behavior of the automated car to the user in a driving simulator experiment. In other words, we will investigate the influence of automated driving style familiarity on automated cars acceptability and take-over performance.  

Scientific goal

Improving scientific knowledge in cognitive psychology and ergonomics regarding the interaction between human and automated cars.