#Navicat premium key 2015 drivers#
#Navicat premium key 2015 driver#
#Navicat premium key 2015 simulator#

A first research objective was to find factors that affect comfort on a rather general level, such as driving situations and driving style parameters (e.g. Methodological details on the experimental design, participants, assessment of discomfort, sensors and questionnaires of the three studies are presented in Sect. Section 2.1 gives an overview on the background and aims of the project, definitions of central concepts and an overall summary of key results. In addition, privacy issues were continuously monitored for all aspects over the project lifetime. Various driving styles as well as display solutions were evaluated for reducing discomfort.

#Navicat premium key 2015 driver#

Sensors such as wearable devices, eye tracking, face tracking and motion tracking allowed for the integration of driver state data with information about the vehicle and the surroundings.

#Navicat premium key 2015 simulator#

The project KomfoPilot aimed at investigating, assessing and enhancing comfort during automated driving by two driving simulator studies and a test track study. A central human–machine interaction issue addresses the question of how automated vehicles should drive to ensure comfort and a positive driving experience.

#Navicat premium key 2015 drivers#

Future research should focus on developing training materials that are accessible to all drivers, so that drivers in lower education and income groups are also supported to understand how advanced driver assistance systems work and benefit from these technologies.Īutomated driving is expected to bring several benefits such as improved traffic safety, reduced congestions and emissions, social inclusion, enhanced accessibility and higher driving comfort. Higher income was associated with a higher percentage of correct responses on the ACC knowledge questionnaire for both owners and non-owners, and for non-owners, higher education level was also significantly associated with a higher percentage of correct responses.

As this technology is becoming more common, even non-owners may be becoming aware of common limitations owning and using ACC does not seem to result in a better system understanding. While owners had a slightly higher percentage of correct answers overall, they did not differ from non-owners in their knowledge of limitations. However, a large percentage of owners still had misperceptions about their ACC system. Results showed that owners today may have a better understanding of some of the main limitations of ACC compared with research conducted over 10 years ago. Along with knowledge of ACC features and limitations, demographic information, experience with technology, and experience with ACC (for owners) were also collected to investigate which factors predicted knowledge of ACC features and limitations. Through an online survey, this study aimed to assess ACC knowledge among ACC owners and non-owners now that this system is more widely available. Much of the existing research on drivers’ understanding of adaptive cruise control (ACC), a type of advanced driver assistance system, was conducted several years ago. The performance deficits observed for drivers with weak mental models appear to reflect uncertainty surrounding how ACC will behave in edge cases. In general, participants with strong mental models were faster than those with weak mental models to respond in edge-case situations-defined as cases where the ACC did not detect an approaching object, such as a slow-moving motorcycle.

The study compared the responses of groups of drivers, with strong and weak mental models of ACC, established through a combination of screening, training, and exposure, in edge case situations in a high-fidelity driving simulator. This simulator study examined how drivers with different mental models of adaptive cruise control performed in edge cases. To maximize safety, it is important to understand how drivers’ knowledge and understanding of these technologies-referred to as drivers’ mental models-impact performance and safety. Driver support features (DSF) have the potential to improve safety, but they also change the driver-vehicle relationship -as well as their respective roles and responsibilities.