Investigating Dataset Distinctiveness

Just as a human might struggle to interpret another human’s handwriting, a computer vision program might fail when asked to perform one task in two different domains. To be more specific, visualize a self-driving car as a human driver who had only ever driven on clear, sunny days, during daylight hours. This driver – the self-driving car – would inevitably face a significant challenge when asked to drive when it is violently raining or foggy during the night, putting the safety of its passengers in danger. An extensive understanding of the data we use to teach computer vision models – such as those that will be driving our cars in the years to come – is absolutely necessary as these sorts of complex systems find their way into everyday human life. This study works to develop a comprehensive meaning of the style of a dataset, or the quantitative difference between cursive lettering and print lettering, with respect to the image data used in the field of computer vision. We accomplished this by asking a machine learning model to predict which commonly used dataset a particular image belongs to, based on detailed features of the images. If the model performed well when classifying an image based on which dataset it belongs to, that dataset was considered distinct. We then developed a linear relationship between this distinctiveness metric and a model’s ability to learn from one dataset and test on another, so as to have a better understanding of how a computer vision system will perform in a given context, before it is trained

Az önvezető autók - a jövő már a jelen?

Industry 4.0, the current technological revolution, includes the appearance of self-driving cars throughout the world. Developing this transport innovation has become an important goal for most automotive and software developing companies alike. They are competing with each other to reach the fully self-driving car, which can work on any road safely in all environments under any circumstances. The evolution of technology is bringing huge transformation, including changes in mobility and in vehicle usage patterns, which means a major challenge for the society. In connection with self-propelled cars it is important to focus on catching up with people as they will be users in the future. This research gives an insight into the topic of self-driving cars and its present, through the main companies. We examine people's attitudes along with their positive and negative effects and the study provides a comprehensive overview of the Zalaegerszeg automotive test track and their effects are examined within a deep interview

Older adults' reporting of specific sedentary behaviors : validity and reliability

Background: Previous questionnaires targeting older adults' sedentary time have underestimated total sedentary time, possibly by not including all relevant specific sedentary behaviors. The current study aimed to investigate the criterion validity and test-retest reliability of a new questionnaire assessing a comprehensive set of sedentary behaviors. Additionally, we examined whether the criterion validity of the questionnaire differed according to age, gender and educational level. Methods: A sample of home-dwelling Belgian older adults (>64 years, n = 508) completed a newly-developed questionnaire assessing twelve specific sedentary behaviors and wore an accelerometer for seven consecutive days as criterion measure. A subsample (n = 28) completed the questionnaire a second time to examine test-retest reliability. Data collection occurred between September 2010 and October 2012. Results: Correlational analyses examining self-reported total sitting time and accelerometer-derived sedentary time yielded a Spearman's. of 0.30. Using the Bland-Altman regression procedure, self-reported total sitting time underestimated accelerometer-derived sedentary time by -82 minutes/day for a participant with an average level of sedentary time (539 minutes/day). Corresponding 95% limits of agreement were wide (-364, 200 minutes/day). Better, but still not ideal, validity findings were observed in the younger, male and tertiary-educated subgroups. Acceptable test-retest reliability (ICC > 0.70) was found for total sitting time, TV viewing, computer use, and driving a car. Conclusion: Validity for older adults' self-reported total sitting time against accelerometer-derived sedentary time was not strong, but comparable to previous studies. However, underestimation of total sedentary time was lower compared to previous studies, possibly explained by the inclusion of additional specific sedentary behaviors. Further research is needed to develop self-report tools and objective criterion measures that accurately measure engagement in (specific) sedentary behavior(s) among different subgroups of the older population

Governing autonomous vehicles: emerging responses for safety, liability, privacy, cybersecurity, and industry risks

The benefits of autonomous vehicles (AVs) are widely acknowledged, but there are concerns about the extent of these benefits and AV risks and unintended consequences. In this article, we first examine AVs and different categories of the technological risks associated with them. We then explore strategies that can be adopted to address these risks, and explore emerging responses by governments for addressing AV risks. Our analyses reveal that, thus far, governments have in most instances avoided stringent measures in order to promote AV developments and the majority of responses are non-binding and focus on creating councils or working groups to better explore AV implications. The US has been active in introducing legislations to address issues related to privacy and cybersecurity. The UK and Germany, in particular, have enacted laws to address liability issues, other countries mostly acknowledge these issues, but have yet to implement specific strategies. To address privacy and cybersecurity risks strategies ranging from introduction or amendment of non-AV specific legislation to creating working groups have been adopted. Much less attention has been paid to issues such as environmental and employment risks, although a few governments have begun programmes to retrain workers who might be negatively affected.Comment: Transport Reviews, 201

Piloting a telemetric data tracking system to assess post-training real driving performance of young novice drivers

Evaluating the effects of driver training interventions is a difficult research task. The ultimate goal of such interventions is to make the driver safer and therefore less likely to be involved in a road crash. A particular driver training intervention can only be considered to be effective if it can show a significant reduction in the number crashes for the driver, or a significant change in driver behaviour that clearly implies safer driving. Getting accurate and comprehensive crash records is difficult and to measure post training behavioural driving changes based on selfreports (e.g., log books) may not be accurate enough to be statistically meaningful

The ‘frontal lobe’ project: A double-blind, randomized controlled study of the effectiveness of higher level driving skills training to improve frontal lobe (executive) function related driving performance in young drivers

The current study was undertaken in order to evaluate the effectiveness of higher level skills training on safe driving behaviour of 36 teenage drivers. The participants, who attended the Driver Training Research camp in Taupo (NZ) over a two week period, were 16 to 17 years old and had a valid restricted driver licence. The study focused on four main aims. Firstly, the behavioural characteristics of the sample and their attitudes to risk taking and driving were examined. Results showed that speeding was the most anticipated driving violation, and high levels of confidence were associated with a higher number of crashes and a greater propensity for risk taking. Many, often male participants, also rated their driving skills as superior to others and thought they would be less likely than others to be involved in an accident. Secondly, the relationship between driving performance and executive functioning, general ability and sustained attention was evaluated. Overall, better driving performance and more accurate self-evaluation of driving performance was related to higher levels of executive functions, in particular, working memory, and cognitive switching. In addition, higher general ability and greater ability to sustain attention were also linked to better performance on the driving related assessments. The third focus of this study was to compare the effects of both, higher level and vehicle handling skills training on driving performance, confidence levels and attitudes to risk. While both types of training improved direction control, speed choice and visual search, along with number of hazards detected and actions in relation to hazards, statistically significant improvement on visual search was seen only after higher level skills training. Vehicle handling skills training significantly improved direction control and speed choice. In addition, confidence levels in their driving skills were significantly lowered and attitudes to speeding, overtaking and close following had improved significantly in the participants after the higher level driving skills training. The final aspect to this study was to examine the effects of the training over the following 6 month period based on self-reported driving behaviour. The response rate of participants however, was not sufficient to reach any meaningful conclusion on any long-term training effects. A pilot study using GPSbased data trackers to assess post-training driving behaviour revealed some promising results for future driver training evaluation studies. The overall implications of the results are discussed in relation to improving the safety of young drivers in New Zealand