Generating a Risk Profile for Car Insurance Policyholders: A Deep Learning Conceptual Model

In recent years, technological improvements have provided a variety of new opportunities for insurance companies to adopt telematics devices in line with usage-based insurance models. This paper sheds new light on the application of big data analytics for car insurance companies that may help to estimate the risks associated with individual policyholders based on complex driving patterns. We propose a conceptual framework that describes the structural design of a risk predictor model for insurance customers and combines the value of telematics data with deep learning algorithms. The model’s components consist of data transformation, criteria mining, risk modelling, driving style detection, and risk prediction. The expected outcome is our methodology that generates more accurate results than other methods in this area

Simulations on Consumer Tests: A Perspective for Driver Assistance Systems

This article discusses new challenges for series development regarding the vehicle safety that arise from the recently published AEB test protocol by the consumer-test-organisation EuroNCAP for driver assistance systems [6]. The tests from the test protocol are of great significance for an OEM that sells millions of cars each year, due to the fact that a positive rating of the vehicle-under-test (VUT) in safety relevant aspects is important for the reputation of a car manufacturer. The further intensification and aggravation of the test requirements for those systems is one of the challenges, that has to be mastered in order to continuously make significant contributions to safety for high-volume cars. Therefore, it is to be shown how a simulation approach may support the development process, especially with tolerance analysis. This article discusses the current stage of work, steps that are planned for the future and results that can be expected at the end of such an analysis.Comment: 6 pages, 5 figure, Proceedings of International Workshop on Engineering Simulations for Cyber-Physical Systems (ES4CPS '14

Interactive Imitation Learning in Robotics: A Survey

Interactive Imitation Learning (IIL) is a branch of Imitation Learning (IL) where human feedback is provided intermittently during robot execution allowing an online improvement of the robot's behavior. In recent years, IIL has increasingly started to carve out its own space as a promising data-driven alternative for solving complex robotic tasks. The advantages of IIL are its data-efficient, as the human feedback guides the robot directly towards an improved behavior, and its robustness, as the distribution mismatch between the teacher and learner trajectories is minimized by providing feedback directly over the learner's trajectories. Nevertheless, despite the opportunities that IIL presents, its terminology, structure, and applicability are not clear nor unified in the literature, slowing down its development and, therefore, the research of innovative formulations and discoveries. In this article, we attempt to facilitate research in IIL and lower entry barriers for new practitioners by providing a survey of the field that unifies and structures it. In addition, we aim to raise awareness of its potential, what has been accomplished and what are still open research questions. We organize the most relevant works in IIL in terms of human-robot interaction (i.e., types of feedback), interfaces (i.e., means of providing feedback), learning (i.e., models learned from feedback and function approximators), user experience (i.e., human perception about the learning process), applications, and benchmarks. Furthermore, we analyze similarities and differences between IIL and RL, providing a discussion on how the concepts offline, online, off-policy and on-policy learning should be transferred to IIL from the RL literature. We particularly focus on robotic applications in the real world and discuss their implications, limitations, and promising future areas of research

Criticality Metrics for Automated Driving: A Review and Suitability Analysis of the State of the Art

The large-scale deployment of automated vehicles on public roads has the potential to vastly change the transportation modalities of today's society. Although this pursuit has been initiated decades ago, there still exist open challenges in reliably ensuring that such vehicles operate safely in open contexts. While functional safety is a well-established concept, the question of measuring the behavioral safety of a vehicle remains subject to research. One way to both objectively and computationally analyze traffic conflicts is the development and utilization of so-called criticality metrics. Contemporary approaches have leveraged the potential of criticality metrics in various applications related to automated driving, e.g. for computationally assessing the dynamic risk or filtering large data sets to build scenario catalogs. As a prerequisite to systematically choose adequate criticality metrics for such applications, we extensively review the state of the art of criticality metrics, their properties, and their applications in the context of automated driving. Based on this review, we propose a suitability analysis as a methodical tool to be used by practitioners. Both the proposed method and the state of the art review can then be harnessed to select well-suited measurement tools that cover an application's requirements, as demonstrated by an exemplary execution of the analysis. Ultimately, efficient, valid, and reliable measurements of an automated vehicle's safety performance are a key requirement for demonstrating its trustworthiness

Patterns and Determinants of Economic Reform in Transition Economies: 1990-1998

The paper begins by presenting a framework for evaluating transition. The framework identifies categories of influences or "determinants of transition" and how they interact to produce short-term impacts, intermediate outcomes, and long-term socio-economic performance. Among the determinants are the so-called "initial conditions" of transition. The initial conditions describe the situation a country finds itself at the start of the process and are a mixture of geographic fixed characteristics, hard-to-change institutional and economic conditions, and relatively easy-to-change policy conditions. The paper then uses the initial conditions to create a country cluster typology, which is used throughout the rest of the paper. Focusing on the "cluster" as the central unit of analysis underscores our belief that this method greatly simplifies the analysis while at the same time illuminating common features that would otherwise be obscured by country-specific details. This approach also recognizes that the inter-cluster differences are so profound as to make it senseless to compare, say capital market developments in Poland with the Kyrgyz Republic; their initial conditions are just too different. While it is difficult to draw lessons between countries in different clusters, the opposite is true within clusters. By specifically controlling for common initial conditions among countries within a cluster we find ourselves with a powerful assessment tool to evaluate the effectiveness of the alternative policies that countries have taken within the cluster. In brief, a cluster based analysis is a more productive approach upon which to formulate donor programs

Prospective and retrospective performance assessment of Advanced Driver Assistance Systems in imminent collision scenarios: the CMI-Vr approach

Structured abstract Introduction Prospective and retrospective performance assessment of Advanced Driver Assistance Systems (ADASs) is fundamental to pilot future enhancements for active safety devices. In critical road scenarios between two vehicles where ADAS activation enables collision mitigation only, currently available assessment methodologies rely on the reconstruction of the impact phase consequent to the specific intervention on braking and steering: the velocity change sustained by the vehicle in the collision ($$\Delta V$$ Δ V ) is retrieved, so that IR decrease for the vehicle occupants can be obtained by appropriate Injury Risk (IR) models. However, information regarding the ADAS performance is available only after the impact phase reconstruction and not just as when the criticality occurs in the pre-impact phase: the best braking and steering alternative cannot be immediately envisaged, since a direct correlation lacks between the braking/steering intervention and IR. Method This work highlights an ADAS performance assessment method based on the disaggregation of $$\Delta V$$ Δ V in the two pre-impact parameters closing velocity at collision ($$V_r$$ V r ) and impact eccentricity, represented by the Crash Momentum Index (CMI). Such a disaggregation leads to the determination of IR based solely on impact configuration between the vehicles, without directly considering the impact phase. The performance of diverse ADASs in terms of intervention logic are directly comparable based on the resulting impact configuration, associated with a single coordinate in the CMI-$$V_r$$ V r plane and a sole IR value as a consequence. Results The CMI-$$V_r$$ V r approach is employable for both purposes of prospective and retrospective performance assessment of ADAS devices. To illustrate the advantages of the methodology, a solution for prospective assessment based on the CMI-$$V_r$$ V r plane is initially proposed and applied to case studies: this provides direct suggestions regarding the most appropriate interventions on braking and steering for IR minimization, fundamental in the tuning or development phase of an ADAS. A method for retrospective assessment is ultimately contextualized in the EuroNCAP "Car-to-Car Rear moving" test for an Inter-Urban Autonomous Emergency Braking system, a device implemented on a significant portion of the circulating fleet. Conclusions Based on the evidenced highlights, it is demonstrated that the approach provides complementary information compared to well-established performance assessment methodologies in all stages of an ADAS life cycle, by suggesting a direct physical connection in the pre-impact phase between the possible ADAS interventions and the foreseeable injury outcomes

Mobility and Aging: Older Drivers’ Visual Searching, Lane Keeping and Coordination

This thesis examined older drivers’ mobility and behaviour through comprehensive measurements of driver-vehicle-environment interaction and investigated the associations between driving behaviour and cognitive functions. Data were collected and analysed for 50 older drivers using eye tracking, GNSS tracking, and GIS. Results showed that poor selective attention, spatial ability and executive function in older drivers adversely affect lane keeping, visual search and coordination. Visual-motor coordination measure is sensitive and effective for driving assessment in older drivers

Traffic conflict technique toolkit : making the journey to and from school safer for student

An evidence-based approach to designing and executing traffic conflict data collection with a focus on school zones in low- and middle-income countries The Traffic Conflict Technique (TCT) is a means of proactively collecting observational data to evaluate the safety of intersections or stretches of roadways with the intention of preventing crashes and injuries before they occur. Particularly in locations where data are scarce, TCTs can help determine if road safety interventions are effective in reducing traffic conflicts, and thus, reducing crashes and injuries. This toolkit is intended to serve as a comprehensive guide for applying TCTs and presents a variety of different methods to conduct this evaluation based on time, resources, and need.Suggested citation: Swanson, J. M., Roehler, D. R., & Sauber-Schatz, E. K. (2020) Traffic Conflict Technique Toolkit: Making the Journey to and from School Safer for Students. CDC Foundation and FIA Foundation. Available from: https://www.childhealthinitiative.org/media/791406/tct_toolkit_final_508.pdf.tct_toolkit_final_508.pdf20201066