In this book, Andrew Harvey sets out to provide a unified and comprehensive theory of structural time series models. Unlike the traditional ARIMA models, structural time series models consist explicitly of unobserved components, such as trends and seasonals, which have a direct interpretation. As a result the model selection methodology associated with structural models is much closer to econometric methodology. The link with econometrics is made even closer by the natural way in which the models can be extended to include explanatory variables and to cope with multivariate time series. From the technical point of view, state space models and the Kalman filter play a key role in the statistical treatment of structural time series models. The book includes a detailed treatment of the Kalman filter. This technique was originally developed in control engineering, but is becoming increasingly important in fields such as economics and operations research. This book is concerned primarily with modelling economic and social time series, and with addressing the special problems which the treatment of such series poses. The properties of the models and the methodological techniques used to select them are illustrated with various applications. These range from the modellling of trends and cycles in US macroeconomic time series to to an evaluation of the effects of seat belt legislation in the UK.

Forecasting, Structural Time Series Models and the Kalman Filter

本文对国际科学计量学杂志《Ｓｃｉｅｎｔｏｍｅｔｒｉｃｓ》１９７９－１９９１年的研究论文内容、栏目、作者及国别和编委及国别作了计量分析，揭示出科学计量学研究的重点、活动的中心及发展趋势，说明了学科带头人在发展科学计量学这门新兴学科中的作用。

国际刊物《Scientometrics》文献计量研究

Automated driving systems (ADSs) promise a safe, comfortable and efficient driving experience. However, fatalities involving vehicles equipped with ADSs are on the rise. The full potential of ADSs cannot be realized unless the robustness of state-of-the-art is improved further. This paper discusses unsolved problems and surveys the technical aspect of automated driving. Studies regarding present challenges, high-level system architectures, emerging methodologies and core functions including localization, mapping, perception, planning, and human machine interfaces, were thoroughly reviewed. Furthermore, many state-of-the-art algorithms were implemented and compared on our own platform in a real-world driving setting. The paper concludes with an overview of available datasets and tools for ADS development.

A Survey of Autonomous Driving: Common Practices and Emerging Technologies

This study investigated user acceptance, worries, and willingness to buy partially, highly, and fully automated vehicles. By means of a 63-question Internet-based survey, we collected 5,000 responses from 109 countries (40 countries with at least 25 respondents). We determined cross-national differences, and assessed correlations with personal variables, such as age, gender, and personality traits as measured with a short version of the Big Five Inventory. Results showed that respondents, on average, found manual driving the most enjoyable mode of driving. Responses were diverse: 22% of the respondents did not want to pay more than $0 for a fully automated driving system, whereas 5% indicated they would be willing to pay more than $30,000 for it, and 33% indicated that fully automated driving would be highly enjoyable. 69% of respondents estimated that fully automated driving will reach a 50% market share between now and 2050. Respondents were found to be most concerned about software hacking/misuse, and were also concerned about legal issues and safety. More neurotic people were slightly less comfortable about data transmitting, whereas agreeable persons were slightly more comfortable with that. The more developed countries (in terms of accident statistics, education, and income) were less comfortable with their vehicle transmitting data, with cross-national correlations between ρ= -0.80 and ρ= -0.90. The present results indicate the major areas of promise and concern among the international public, and could be useful for vehicle developers and other stakeholders.

/pdf/public-opinion-on-automated-driving-results-of-an-49ui4752zp.pdf

Public Opinion on Automated Driving: Results of an International Questionnaire Among 5,000 Respondents

This study investigated user acceptance, concerns, and willingness to buy partially, highly, and fully automated vehicles. By means of a 63-question Internet-based survey, we collected 5000 responses from 109 countries (40 countries with at least 25 respondents). We determined cross-national differences, and assessed correlations with personal variables, such as age, gender, and personality traits as measured with a short version of the Big Five Inventory. Results showed that respondents, on average, found manual driving the most enjoyable mode of driving. Responses were diverse: 22% of the respondents did not want to pay more than $0 for a fully automated driving system, whereas 5% indicated they would be willing to pay more than $30,000, and 33% indicated that fully automated driving would be highly enjoyable. 69% of respondents estimated that fully automated driving will reach a 50% market share between now and 2050. Respondents were found to be most concerned about software hacking/misuse, and were also concerned about legal issues and safety. Respondents scoring higher on neuroticism were slightly less comfortable about data transmitting, whereas respondents scoring higher on agreeableness were slightly more comfortable with this. Respondents from more developed countries (in terms of lower accident statistics, higher education, and higher income) were less comfortable with their vehicle transmitting data, with cross-national correlations between ρ = −0.80 and ρ = −0.90. The present results indicate the major areas of promise and concern among the international public, and could be useful for vehicle developers and other stakeholders.

/pdf/public-opinion-on-automated-driving-results-of-an-4z5lglcxwz.pdf

Public opinion on automated driving: results of an international questionnaire among 5000 respondents

Ehmi on the Vehicle or Just a Traffic Light? A Driving Simulator Study

INTRODUCTION
In the 1950s and 1960s, John Senders carried out a number of influential experiments on the monitoring of multidegree-of-freedom systems. In these experiments, participants were tasked with detecting events (threshold crossings) for multiple dials, each presenting a signal with different bandwidth. Senders' analyses showed a nearly linear relationship between signal bandwidth and the amount of attention paid to the dial, and he argued that humans sample according to bandwidth, in line with the Nyquist-Shannon sampling theorem.


OBJECTIVE
The current study tested whether humans indeed sample the dials based on bandwidth alone or whether they also use salient peripheral cues.


METHODS
A dial-monitoring task was performed by 33 participants. In half of the trials, a gaze-contingent window was used that blocked peripheral vision.


RESULTS
The results showed that, without peripheral vision, humans do not effectively distribute their attention across the dials. The findings also suggest that, when given full view, humans can detect the speed of the dial using their peripheral vision.


CONCLUSION
It is concluded that salience and bandwidth are both drivers of distributed visual attention in a dial-monitoring task.


APPLICATION
The present findings indicate that salience plays a major role in guiding human attention. A subsequent recommendation for future human-machine interface design is that task-critical elements should be made salient.

https://journals.sagepub.com/doi/pdf/10.1177/00187208231176148

Expectancy or Salience?-Replicating Senders' Dial-Monitoring Experiments With a Gaze-Contingent Window.

A common challenge with processing naturalistic driving data for many different possible driving research interests or applications is that humans may need to categorize great volumes of recorded visual information until automated algorithms might be trained to do so alone.This study, by means of the online platform CrowdFlower, investigated the potential of crowdsourcing to provide content identification categorizations of driving scene features (e.g., presence of another vehicle, straight road segments, etc.) at greater scale than a single person or a small team of researchers would be capable of. The validity and reliability of CrowdFlower results were examined, both with and without employing a set of randomly embedded controlled questions (Gold Test Questions) intermixed with experimental questions (Work Mode). In total, 200 workers from 46 countries participated in this study, and the collection of data lasted one and a half days.By employing Gold Test Questions, we found significantly more accurate and consistent responses from external workers at both a smaller and larger scale of video segment categorizations for the identification of common driving scene elements (e.g., position and behavior of other vehicles, road and signage characteristics, etc.). In terms of validity and at the small scale, an average accuracy of 91% on paired items was found with the controlled questions compared to 78% without. A difference in bias was found where without Gold Test Questions external workers returned more false positives than false negatives whereas the opposite was found true of the condition with Gold Test Questions. At the large scale (making use of the controlled questions), a random subset of categorizations returned similar levels of accuracy (95%) and a similar pattern of error bias. In terms of reliability and at the small scale, where segments were rated in triplicate redundancy, the percentage of unanimous agreement was found significantly higher when using controlled questions (90%) than without them (65%). Across the small scale of internally validated answers, more than two-thirds of any correct categorization were unanimously returned and 86% or more of any correct categorization was returned by a majority vote. Where it would be infeasible to validate every response for accuracy, similar voting reliability results were found to exist across the responses of the large scale.Overall results support compelling evidence for CrowdFlower as being able to yield valid and reliable crowdsourced categorizations of naturalistic driving scene contents in a short period of time and thus a potentially powerful and as-of-yet under-utilized resource in the toolbox of driving research and driving automation development.

Validity and reliability of naturalistic driving scene categorization judgments from crowdsourcing

This article synthesizes the latest information on the relationship between the Driver Behaviour Questionnaire (DBQ) and accidents. We show by means of computer simulation that correlations with accidents are necessarily small, because accidents are rare events. An updated meta-analysis on the zero-order correlations between the DBQ and self-reported accidents yielded an overall r of .13 (fixed-effect and random-effects models) for violations (57,480 participants; 67 samples) and .09 (fixed-effect and random-effects models) for errors (66,028 participants; 56 samples). An analysis of a previously published DBQ dataset (975 participants) showed that by aggregating across four measurement occasions, the correlation coefficient with self-reported accidents increased from .14 to .24 for violations and from .11 to .19 for errors. Our meta-analysis also showed that DBQ violations (r = .24; 6,353 participants; 20 samples) but not DBQ errors (r = −.08; 1,086 participants; 16 samples) correlated with recorded vehicle speed.

/pdf/a-quarter-of-a-century-of-the-dbq-some-supplementary-notes-2m5zz7te5e.pdf

A Quarter of a Century of the DBQ: Some Supplementary Notes on Its Validity with Regard to Accidents

Situation awareness (SA) is critical to improving takeover performance during the transition period from automated driving to manual driving. Although many studies measured SA during or after the driving task, few studies have attempted to predict SA in real time in automated driving. In this work, we propose to predict SA during the takeover transition period in conditionally automated driving using eye-tracking and self-reported data. First, a tree ensemble machine learning model, named LightGBM (Light Gradient Boosting Machine), was used to predict SA. Second, in order to understand what factors influenced SA and how, SHAP (SHapley Additive exPlanations) values of individual predictor variables in the LightGBM model were calculated. These SHAP values explained the prediction model by identifying the most important factors and their effects on SA, which further improved the model performance of LightGBM through feature selection. We standardized SA between 0 and 1 by aggregating three performance measures (i.e., placement, distance, and speed estimation of vehicles with regard to the ego-vehicle) of SA in recreating simulated driving scenarios, after 33 participants viewed 32 videos with six lengths between 1 and 20 s. Using only eye-tracking data, our proposed model outperformed other selected machine learning models, having a root-mean-squared error (RMSE) of 0.121, a mean absolute error (MAE) of 0.096, and a 0.719 correlation coefficient between the predicted SA and the ground truth. The code is available at this https URL. Our proposed model provided important implications on how to monitor and predict SA in real time in automated driving using eye-tracking data.

Joost C. F. de Winter

Papers

Ehmi on the Vehicle or Just a Traffic Light? A Driving Simulator Study

Expectancy or Salience?-Replicating Senders' Dial-Monitoring Experiments With a Gaze-Contingent Window.

Validity and reliability of naturalistic driving scene categorization judgments from crowdsourcing

A Quarter of a Century of the DBQ: Some Supplementary Notes on Its Validity with Regard to Accidents

Using Eye-tracking Data to Predict Situation Awareness in Real Time during Takeover Transitions in Conditionally Automated Driving.