168 research outputs found
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The Promise of VR Headsets: Validation of a Virtual Reality Headset-Based Driving Simulator for Measuring Drivers’ Hazard Anticipation Performance
The objective of the current study is to evaluate the use of virtual reality (VR) headsets to measure driving performance. This is desirable because they are several orders of magnitude less expensive and, if validated, could greatly extend the powers of simulation. Out of several possible measures of performance that could be considered for evaluating VR headsets, the current study specifically examines drivers’ latent hazard anticipation behavior both because it has been linked to crashes and because it has been shown to be significantly poorer in young drivers compared to their experienced counterparts in traditional driving simulators and in open road studies. The total time middle-aged drivers spend glancing at a latent hazard and the average duration of each glance was also compared to these same times for younger drivers using a VR headset and fixed-based driving simulator. In a between-subject design, forty-eight participants were equally and randomly assigned to one out of four experimental conditions – two young driver cohorts (18 – 21 years) and two middle-aged driver cohorts (30 – 55 years) navigating either a fixed-based driving simulator or a VR-headset-based simulator. All participants navigated six unique scenarios while their eyes were continually tracked. The proportion of latent hazards anticipated by participants which constituted the primary dependent measure was found to be greater for middle-aged drivers than young drivers across both platforms. Results also indicate that the middle-aged participants glanced longer than their younger counterparts on both platforms at latent hazards, as measured by the total glance duration but had no difference when measured by the average glance duration. Moreover, the difference in the magnitude of performance between middle-aged and younger drivers was the same across the two platforms. There were also no significant differences found for the severity of simulator sickness symptoms across the two platforms. The study provides some justification for the use of virtual reality headsets as a way of understanding drivers’ hazard anticipation behavior
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DRIVERS’ HAZARD AVOIDANCE DURING VEHICLE AUTOMATION: IMPACT OF MENTAL MODELS AND IMPLICATIONS FOR TRAINING
Advanced Driver Assistance Systems (ADAS) are vehicle automation systems that have become more accessible and prevalent in vehicles in recent years. But the introduction of such technologies introduces new human factors challenges. Past literature suggests that users of vehicle automation lack the necessary and appropriate knowledge about their automation system. This may play a negative role in their hazard avoidance abilities when driving with automation features. Improving mental models and knowledge could generally lead to safer interactions with vehicle automation systems, but any effort to develop hazard avoidance skills when driving with vehicle automation is impeded by the lack of literature regarding the subject. Moreover, it is possible hazard avoidance for vehicle automation may actually differ from that for traditional driving. For vehicle automation, system-related changes occurring internally inside one’s vehicle also impact how the system responds and controls the vehicle. Failure to recognize certain critical system changes may have disastrous consequences. Hence, it is imperative that a new framework for hazard avoidance in the new context of vehicle automation, especially for ADAS features, is conceptualized. Initially, the research focused on realizing exactly this by proposing a conceptual framework for hazard avoidance in the context of vehicle automation by making use of past literary sources on hazard avoidance for traditional driving. Next, the relationship between mental models, training, and hazard avoidance was mapped and each new behavioral construct of hazard avoidance focusing on awareness, detection, and responses based on internal events was assigned potential outcome measure. Next, an observational study was conducted with ten experienced users of Adaptive Cruise Control (ACC). Among them, five were assigned to an eye movements group and five others to a verbal responses group. The eye movement observations gave us insights into how experienced users detect and respond to hazards and how these affect their interactions and responses using their ACC systems. The verbal group also provided insights about the participants’ awareness during the drive which featured several edge-case and normal events. These observations imply that hazard avoidance behaviors actually differ in the context of ADAS compared to traditional driving. The findings from the observational study were leveraged when designing and developing a new training program where drivers would receive an immersive and realistic training experience through a Virtual Reality (VR) headset. The main objective of the training program was to improve the user’s mental models about ACC and also equip them with the necessary skills to avoid hazard during edge case events of ACC. Finally, an evaluation study was conducted with 36 novice ACC users on a driving simulator capable of simulating ACC operations. The participants were equally and randomly assigned to one of three group – the VR group that received the newly designed VR training program; the SD group that received training material with state diagram visualization of ACC and other information derived from owner’s manuals; or the BI group that received basic textual information about ACC. The participants’ mental models before and after training were measured using a mental models survey, and the simulator drive was designed to collect valuable data about the participants interactions with ACC and their hazard avoidance behaviors. Findings revealed that although the VR training program had some impact on the participants\u27 mental models and hazard avoidance behaviors, the impact was not statistically significant. However, the VR training did show significantly positive influences on the participants’ internal glance activities that detect and assess system states, during edge case events. This finding is important since one of the modules of the VR training program was carefully curated to improve driver’s glance behavior when encountering edge case events of ACC. The results also establish the relationships between training and mental models although no significant correlations were found between the participants’ mental models and their hazard avoidance behaviors. However, this does fill a major gap in literature about our understanding about hazard avoidance in the context of vehicle automation and ADAS and could be extended for ADAS features other than ACC or even higher levels of automation. The VR training program can be built upon to include more ADAS features as well leading to better training practices in a rapidly developing world where vehicle automation has become a mainstay
BIOLOGIC ARMAMENTARIUM IN PSORIASIS
Psoriasis is an autoimmune disease and further classed as a chronic inflammatory skin condition serving as global burden. Moderate to severe psoriasis can be treated with conventional therapies. Less efficacy, poor patient compliance and toxicity issues were the major problems associated with conventional therapies. The introduction of biologic therapy has great impression on psoriatic treatment duration and enhanced quality of life in psoriasis patients. The new biologic therapies are tailor made medications with goal of more specific and effective treatment; less toxicity. The biologic therapy is aimed to target antigen presentation and co-stimulation, T-cell activation and leukocyte adhesion; and pro-inflammatory cascade. They act as effective and safer substitute to traditional therapy. Secukinumab, certolizumab, itolizumab, golimumab, ustekinumab, adalimumab, infliximab etanercept, alefacept etc. are the approved biologic with global market. This review briefs about psoriasis pathogenesis, traditional treatments and biologic therapies potential
Towards a Formal Basis for Modular Safety Cases
Safety assurance using argument-based safety cases is an accepted best-practice in many safety-critical sectors. Goal Structuring Notation (GSN), which is widely used for presenting safety arguments graphically, provides a notion of modular arguments to support the goal of incremental certification. Despite the efforts at standardization, GSN remains an informal notation whereas the GSN standard contains appreciable ambiguity especially concerning modular extensions. This, in turn, presents challenges when developing tools and methods to intelligently manipulate modular GSN arguments. This paper develops the elements of a theory of modular safety cases, leveraging our previous work on formalizing GSN arguments. Using example argument structures we highlight some ambiguities arising through the existing guidance, present the intuition underlying the theory, clarify syntax, and address modular arguments, contracts, well-formedness and well-scopedness of modules. Based on this theory, we have a preliminary implementation of modular arguments in our toolset, AdvoCATE
A Formal Basis for Safety Case Patterns
By capturing common structures of successful arguments, safety case patterns provide an approach for reusing strategies for reasoning about safety. In the current state of the practice, patterns exist as descriptive specifications with informal semantics, which not only offer little opportunity for more sophisticated usage such as automated instantiation, composition and manipulation, but also impede standardization efforts and tool interoperability. To address these concerns, this paper gives (i) a formal definition for safety case patterns, clarifying both restrictions on the usage of multiplicity and well-founded recursion in structural abstraction, (ii) formal semantics to patterns, and (iii) a generic data model and algorithm for pattern instantiation. We illustrate our contributions by application to a new pattern, the requirements breakdown pattern, which builds upon our previous wor
Evidence Arguments for Using Formal Methods in Software Certification
We describe a generic approach for automatically integrating the output generated from a formal method/tool into a software safety assurance case, as an evidence argument, by (a) encoding the underlying reasoning as a safety case pattern, and (b) instantiating it using the data produced from the method/tool. We believe this approach not only improves the trustworthiness of the evidence generated from a formal method/tool, by explicitly presenting the reasoning and mechanisms underlying its genesis, but also provides a way to gauge the suitability of the evidence in the context of the wider assurance case. We illustrate our work by application to a real example-an unmanned aircraft system- where we invoke a formal code analysis tool from its autopilot software safety case, automatically transform the verification output into an evidence argument, and then integrate it into the former
Quantifying Assurance in Learning-enabled Systems
Dependability assurance of systems embedding machine learning(ML)
components---so called learning-enabled systems (LESs)---is a key step for
their use in safety-critical applications. In emerging standardization and
guidance efforts, there is a growing consensus in the value of using assurance
cases for that purpose. This paper develops a quantitative notion of assurance
that an LES is dependable, as a core component of its assurance case, also
extending our prior work that applied to ML components. Specifically, we
characterize LES assurance in the form of assurance measures: a probabilistic
quantification of confidence that an LES possesses system-level properties
associated with functional capabilities and dependability attributes. We
illustrate the utility of assurance measures by application to a real world
autonomous aviation system, also describing their role both in i) guiding
high-level, runtime risk mitigation decisions and ii) as a core component of
the associated dynamic assurance case.Comment: Author's pre-print version of manuscript accepted for publication in
the Proceedings of the 39th International Conference in Computer Safety,
Reliability, and Security (SAFECOMP 2020
Towards Quantification of Assurance for Learning-enabled Components
Perception, localization, planning, and control, high-level functions often
organized in a so-called pipeline, are amongst the core building blocks of
modern autonomous (ground, air, and underwater) vehicle architectures. These
functions are increasingly being implemented using learning-enabled components
(LECs), i.e., (software) components leveraging knowledge acquisition and
learning processes such as deep learning. Providing quantified component-level
assurance as part of a wider (dynamic) assurance case can be useful in
supporting both pre-operational approval of LECs (e.g., by regulators), and
runtime hazard mitigation, e.g., using assurance-based failover configurations.
This paper develops a notion of assurance for LECs based on i) identifying the
relevant dependability attributes, and ii) quantifying those attributes and the
associated uncertainty, using probabilistic techniques. We give a practical
grounding for our work using an example from the aviation domain: an autonomous
taxiing capability for an unmanned aircraft system (UAS), focusing on the
application of LECs as sensors in the perception function. We identify the
applicable quantitative measures of assurance, and characterize the associated
uncertainty using a non-parametric Bayesian approach, namely Gaussian process
regression. We additionally discuss the relevance and contribution of LEC
assurance to system-level assurance, the generalizability of our approach, and
the associated challenges.Comment: 8 pp, 4 figures, Appears in the proceedings of EDCC 201
Safety Case Patterns: Theory and Applications
We develop the foundations for a theory of patterns of safety case argument structures, clarifying the concepts involved in pattern specification, including choices, labeling, and well-founded recursion. We specify six new patterns in addition to those existing in the literature. We give a generic way to specify the data required to instantiate patterns and a generic algorithm for their instantiation. This generalizes earlier work on generating argument fragments from requirements tables. We describe an implementation of these concepts in AdvoCATE, the Assurance Case Automation Toolset, showing how patterns are defined and can be instantiated. In particular, we describe how our extended notion of patterns can be specified, how they can be instantiated in an interactive manner, and, finally, how they can be automatically instantiated using our algorithm
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