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Automated vehicles (AVs) aim to dramatically improve traffic safety by reducing or eliminating human error, which remains the leading cause of road crashes. However, commonly accepted standards for the ‘safe driving behaviour of machines’ are pending and urgently needed. Unless a common understanding of safety as a design value is achieved, different manufacturers’ driving styles may emerge, resulting in inconsistent, unpredictable and potentially unsafe ‘behaviour’ of AVs in certain situations. This paper aims to explore the main gaps and challenges towards establishing shared safety standards for the ‘behaviour’ of AVs, and contribute to their responsible traffic integration, by reviewing the state-of-the-art on AV safety in the core relevant disciplines: ethics of technology, safety science (engineering & human factors), and standardisation. The ethical and safety aspects investigated include the users’ perception of AV safety, the ethical trade-offs in critical decision-making contexts, the pertinence of data-driven approaches for AVs to mimic human behaviour, and the responsibilities of various actors. Moreover, the paper reviews the current safety patterns, metrics (surrogate measures of safety – SMoS) and their thresholds introduced in existing research for three use cases: mixed traffic of AV and conventional vehicles, AV interaction with pedestrians and cyclists, and transition of control from machine to human driver. The results reveal several knowledge gaps within each discipline and highlights the lack of common understanding of safety across disciplines. On the basis of the results, the paper proposes a framework for further research on AV safety, identifying concrete opportunities for interdisciplinary research, with common goals and methodologies, and explicitly indicating the path for transfer of knowledge between sectors.

Road safety research is largely focused on prediction and prevention of technical, human or organisational failures that may result in critical conflicts or crashes. Indicators of traffic risk aim to capture the passage to unsafe states. However, research in other industries has shown that it is meaningful to analyse safety along the whole spectrum of behaviours. Knowing the causes and patterns of “successful” interactions, rather than failures, could give new insights on the complexity of the system and the adaptability and resilience of its users in handling the inherent risks. The concept is known as Safety-II and has been extensively explored in the aviation, healthcare and process engineering domains. In this paper, we explore a new Safety-II paradigm for road safety research. We briefly review Safety-II applications in other sectors. We then present a Safety-II model for road safety, by means of an inverse version of Hyden's “safety pyramid”. Furthermore, we discuss a number of key road safety goals, theories, analysis methods and data sources and map them into a tentative taxonomy of Safety-I and Safety-II applications. It is concluded that there can be opportunities and benefits from adopting this new mindset, in order to complement existing approaches.

Every year, traffic accidents are responsible for more than 1,000,000 fatalities worldwide. Understanding the causes of traffic accidents and increasing safety on the road are priority issues for both legislators and the automotive industry. Recently, in Europe, the US and Japan, significant public funding has been allocated for performing large-scale naturalistic driving studies to better understand accident causation and the impact of safety systems on traffic safety. The data provided by these naturalistic driving studies has never been available before in this quantity and comprehensiveness and it promises to support a wide variety of data analyses. The volume and variety of the data also pose substantial challenges that demand new data reduction and analysis techniques. This paper presents a general procedure for the analysis of naturalistic driving data called chunking that can support many of these analyses by increasing their robustness and sensitivity. Chunking divides data into equivalent, elementary chunks of data to facilitate a robust and consistent calculation of parameters. This procedure was applied, as an example, to naturalistic driving data from the SeMiFOT study in Sweden and compared with alternative procedures from past studies in order to show its advantages and rationale in a specific example. Our results show how to apply the chunking procedure and how chunking can help avoid bias from data segments with heterogeneous durations (typically obtained from SQL queries). Finally, this paper shows how chunking can increase the robustness of parameter calculation, statistical sensitivity, and create a solid basis for further data analyses. (C) 2012 Elsevier Ltd. All rights reserved.

A considerable amount of all traffic accidents can be attributed to driving under the influence of alcohol. In particular the group of drivers aged 18-24 years is involved in many serious traffic accidents where alcohol turns out to be a major factor. In fact this age group shows about three times as many alcohol related traffic fatalities as all other categories of road users. The intervention program "Alcohol-free on the road" (Dutch: "Alcoholvrij op weg") aims to enhance young people's awareness of the effects of alcohol by letting them personally experience the effect of alcohol on their driving abilities. To this end, young drivers were invited to a closed circuit and allowed to drive first sober and then intoxicated, guided and guarded by driving instructors. Based on several other studies it was thought that a realistic experience of the effects of alcohol on driving abilities may contribute to a better understanding of the impact of alcohol and may stimulate attitudes that are needed to support the conscious decision not to drive while intoxicated.After more than ten years of running and data collection, 1200 young drivers have participated in the intervention program. In a quasi-experimental study with a non-equivalent group design, the program is evaluated in order to assess its effectiveness both with respect to the attitudes of the participants and the actual relevant behaviour in the years after the alcohol experience intervention program they attended, i.e. the incidence of actually driving under the influence of alcohol. To do this, a questionnaire was sent to a subset (415) of the participants who have completed the program, along with a control group (450), to compare attitudes and actual behaviour. In addition, the Public Prosecutor checked the files of those who responded, for the occurrence of driving under the influence of alcohol. The group that participated in the alcohol intervention program showed more awareness about the dangers of driving while intoxicated than the control group, and this group reported improved alcohol law compliance. Furthermore, less participants in the intervention program than in the control group were present in the Public Prosecutor files, respectively 0.7% and 4.2%. Hence, the alcohol driving experience intervention program might turn out to be effective and successful in decreasing driving under the influence of alcohol. Although the results of the present study are no more than suggestive, they may be considered a first step towards demonstrating the effectiveness of this type of intervention. However, the intervention is unique and warrants a more robust evaluation. A large-sized randomized controlled trial should be conducted in the next phase to confirm the findings that the intervention program is a suitable educational tool to decrease driving under the influence of alcohol. The present paper serves to raise awareness of this intervention and its potential. (C) 2010 Elsevier Ltd. All rights reserved.

Humans often make inflated or erroneous estimates of their own ability or performance. Such errors in calibration can be due to incomplete processing, neglect of available information or due to improper weighing or integration of the information and can impact our decision-making, risk tolerance, and behaviors. In the driving context, these outcomes can have important implications for safety. The current paper discusses the notion of calibration in the context of self-appraisals and self-competence as well as in models of self-regulation in driving. We further develop a conceptual framework for calibration in the driving context borrowing from earlier models of momentary demand regulation, information processing, and lens models for information selection and utilization. Finally, using the model we describe the implications for calibration (or, more specifically, errors in calibration) for our understanding of driver distraction, in-vehicle automation and autonomous vehicles, and the training of novice and inexperienced drivers.