A new method and results for analyzing decision-making processes in automated driving on highways

Abstract

While automated driving and advanced drivers’ assistant systems (ADAS) become increasingly widespread, the human machine interaction for these technologies gains in importance. In today’s traffic, some vehicles are capable of driving partially, conditionally or highly automated, at least in certain traffic situations, such as driving on developed highways. Nevertheless, these technologically advanced systems are not the only participants in traffic. With the interplay of more or less technologically advanced vehicles and humans on bikes and on foot, complex situations can arise that exceed the capabilities of an automated system and requires human cognition as a part of the solution. Although ADAS and automation solutions take this into account and try to compensate for the resulting effects, encounters with ambiguous situations can emerge. Furthermore, automation systems heavily rely on sensors and are therefore vulnerable to ambient conditions and situations that might limit the performance of the used sensor technology. For this reason, the (human) driver is still required for supervising the situation and often also as a fallback level in the case the technical system reaches or exceeds its performance restrictions. Guiding a vehicle, such as a car with partial or conditional automation, entails a different kind of driver vehicle interaction and cooperation between driver and automation as the one that is needed in the case of manual driving. For analyzing the decision making process of a human-machine-system with such an advanced automation during a typical driving situation like an takeover situation on a highway, a study addressing partially and conditionally/highly automated driving was conducted. The experiment with 30 participants consisted of three rounds with varying conditions in the driving simulator. During and after each round, participants were asked to answer several questions. For this purpose, a questionnaire has been developed to measure the relevant dimensions of the investigated driving situation. These were perceived utility, perceived time consumption, perceived safety, user satisfaction, perceived usability, and perceived dominance (control over the vehicle guidance). The evaluation of the driving experiment shows that the level of automation as well as the volume of traffic have a significant effect on the decision-making behavior and on the individual perception when driving on a highway. This means that during automated driving, humans perceive and judge the driving situation differently. As a consequence, they tend to use the remaining decision authority for other purposes than when driving manually.