The emerging technology of automated vehicles (AV) has been rapidly advancing and is accompanied by various positive and negative potentials. The new technology is expected to affect costs mainly by reducing the number of collisions and travel time, as well as improving fuel efficiency and parking benefits. On the other hand, safety outcomes from AV deployment is a critical issue. Ensuring the safety of AVs requires a multi-disciplinary approach that monitors every aspect of these vehicles. The California Department of Motor Vehicles has mandated that AV collision reports be made public in recent years. This study collected the scanned collision reports filed by different manufacturers that are assessing AVs in California (September 2014 to May 2019). The collected data offers critical information on AV collision frequencies and associated contributing factors. This study provides an in-depth exploratory analysis of the critical variables. We demonstrated a variational inference algorithm for Bayesian latent class models. The Bayesian latent class model identified six classes of collision patterns. Classes associated with turning, multi-vehicle collisions, dark lighting conditions with streetlights, and sideswipe and rear-end collisions were also associated with a higher proportion of injury severity levels. The authors anticipate that these results will provide a significant contribution to the area of AV and safety outcomes.

Automated vehicle collisions in California: Applying Bayesian latent class model

In a context of increasing automation of road transport, many researchers have been dedicated to analyse the risks and safety implications of resuming the manual control of a vehicle after a period of automated driving. This paper performs a systematic review about drivers’ performance during takeover manoeuvres in driving simulator, a tool that is widely used in the evaluation of automated systems to reproduce risky situations that would not be possible to test in real roads. The main objectives are to provide a framework for the main strategies, experimental conditions and results obtained by takeover research using driving simulation, as well as to find whether different approaches may lead to different outcomes. First, a literature search following the PRISMA statement guidelines and checklist resulted in 36 relevant papers, which were described in detail according to the type of scenarios and takeover events, drivers’ engagement in secondary tasks and the assessed takeover performance measures. Then, those papers were included in a meta-analysis combining PAM clustering and ANOVA techniques to find patterns among the experimental conditions and to determine if those patterns have influence on the observed takeover performance. Less complex experiments without secondary task engagement and conducted in low-fidelity simulators are associated with lower takeover times and crash rates. The takeover time increases with the time budget of the first alert, which reduces the pressure for a driver’s quick intervention.

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Takeover performance evaluation using driving simulation: a systematic review and meta-analysis

In today’s dynamic complex cyber environments, Cyber Threat Intelligence (CTI) and the risk of cyberattacks are both increasing. This means that organizations need to have a strong understanding of both their internal CTI and their external CTI. The potential for cybersecurity knowledge graphs is evident in their ability to aggregate and represent knowledge about cyber threats, as well as their ability to manage and reason with that knowledge. While most existing research has focused on how to create a full knowledge graph, how to utilize the knowledge graph to tackle real-world industrial difficulties in cyberattack and defense situations is still unclear. In this article, we give a quick overview of the cybersecurity knowledge graph’s core concepts, schema, and building methodologies. We also give a relevant dataset review and open-source frameworks on the information extraction and knowledge creation job to aid future studies on cybersecurity knowledge graphs. We perform a comparative assessment of the many works that expound on the recent advances in the application scenarios of cybersecurity knowledge graph in the majority of this paper. In addition, a new comprehensive classification system is developed to define the linked works from 9 core categories and 18 subcategories. Finally, based on the analyses of existing research issues, we have a detailed overview of various possible research directions.

/pdf/recent-progress-of-using-knowledge-graph-for-cybersecurity-gv3ag2m9.pdf

Recent Progress of Using Knowledge Graph for Cybersecurity

This study examined the effects of interruptions and the use of cognitive decision-making tools on ethical decision-making. Participants completed a structured cognitive tool, an unstructured decis...

Ethical decision-making interrupted: Can cognitive tools improve decision-making following an interruption?

https://doi.org/10.1016/j.cose.2022.103008

Perspectives of Non-Expert Users on Cyber Security and Privacy: An Analysis of Online Discussions on Twitter

We compared time-to-collision (TTC) judgments between automated and manual driving to determine whether automation affected only responses (i.e., braking) or also affected visual perception (i.e., TTC estimation). Automation presumably frees cognitive resources because drivers do not have to control the vehicle. Those resources may be reallocated to processing visual information (e.g., optic flow) relevant for judgments of TTC. With a driving simulator, participants completed drives using manual or automated driving and responded to a rapidly decelerating vehicle using either the brake pedal or a button on the steering wheel (TTC judgment). They also completed a cognitive secondary task during half of the drives. Results suggest that automation can affect perceptual judgments (e.g., TTC estimation) in addition to driving responses (e.g., braking). TTC judgments were more accurate, and brake reaction times were faster, during automated driving than manual driving. This occurred even while performing a cognitively demanding secondary task, suggesting that participants used resources freed by automation to process visual information relevant to TTC judgments rather than complete non-driving tasks. TTC judgments were more accurate during automated driving than manual driving, presumably because automation freed up cognitive resources, allowing participants to assign those resources to processing of visual information (e.g., optic flow) relevant to judgments about collisions. To realize the safety benefit suggested by the results, automated systems should be designed so that cognitive resources freed by automation are assigned to information relevant to the driving task and not to non-driving tasks.

Does automated driving affect time-to-collision judgments?

Automation presumably frees cognitive resources because drivers do not have to control the vehicle. Those resources may be reallocated to processing visual information relevant to driving, such as ...

Does automated driving affect time-to-collision judgments?:

Cyber-physical systems posit a complex number of security challenges due to interconnection of heterogeneous devices having limited processing, communication, and power capabilities. Additionally, the conglomeration of both physical and cyber-space further makes it difficult to devise a single security plan spanning both these spaces. Cyber-security researchers are often overloaded with a variety of cyber-alerts on a daily basis many of which turn out to be false positives. In this paper, we use machine learning and natural language processing techniques to predict the consequences of cyberattacks. The idea is to enable security researchers to have tools at their disposal that makes it easier to communicate the attack consequences with various stakeholders who may have little to no cybersecurity expertise. Additionally, with the proposed approach researchers’ cognitive load can be reduced by automatically predicting the consequences of attacks in case new attacks are discovered. We compare the performance through various machine learning models employing word vectors obtained using both tf-idf and Doc2Vec models. In our experiments, an accuracy of 60% was obtained using tf-idf features and 57% using Doc2Vec method for models based on LinearSVC model.

Predicting Consequences of Cyber-Attacks

We aimed to understand whether warning message design recommendations address the reasons why non-experts choose to not protect themselves from cybersecurity threats. Toward that end, we synthesize...

Do Warning Message Design Recommendations Address Why Non-Experts Do Not Protect Themselves from Cybersecurity Threats? A Review

Cyber-physical systems posit a complex number of security challenges due to interconnection of heterogeneous devices having limited processing, communication, and power capabilities. Additionally, the conglomeration of both physical and cyber-space further makes it difficult to devise a single security plan spanning both these spaces. Cyber-security researchers are often overloaded with a variety of cyber-alerts on a daily basis many of which turn out to be false positives. In this paper, we use machine learning and natural language processing techniques to predict the consequences of cyberattacks. The idea is to enable security researchers to have tools at their disposal that makes it easier to communicate the attack consequences with various stakeholders who may have little to no cybersecurity expertise. Additionally, with the proposed approach researchers' cognitive load can be reduced by automatically predicting the consequences of attacks in case new attacks are discovered. We compare the performance through various machine learning models employing word vectors obtained using both tf-idf and Doc2Vec models. In our experiments, an accuracy of 60% was obtained using tf-idf features and 57% using Doc2Vec method for models based on LinearSVC model.

Natalie R. Lodinger

Papers

Does automated driving affect time-to-collision judgments?

Does automated driving affect time-to-collision judgments?:

Predicting Consequences of Cyber-Attacks

Do Warning Message Design Recommendations Address Why Non-Experts Do Not Protect Themselves from Cybersecurity Threats? A Review

Cyber-Attack Consequence Prediction.