212,874 research outputs found

    Data-Driven User Behavior Evaluation

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    Automotive Original Equipment Manufacturers (OEMs) compete worldwide to stand out with new trends and technologies. Automated Driver Assistance Systems (ADAS) are an example of advanced solutions where a lot of effort is put into the development and utilization of vehicle data. ADAS systems range from different types of information/warning systems to adaptive functions designed to assist the driver in the driving tasks and ensure more efficient and comfortable driving. These types of systems have become standard at many OEMs, including Tesla, Cadillac, BMW, Mercedes, Volvo Cars, and others. Volvo Cars is well-known for the development of such ADAS functions as ACC (Adaptive Cruise Control) and PA (Pilot Assist). These functions offer lateral and/or longitudinal support, but leave the driver in full control and with responsibility for the driving task.The ADAS systems are not fully automated. These systems have a number of limitations related to the context where they can operate. Previous studies have demonstrated that the drivers’ understanding and adoption of these systems is not definite and may vary from full technology acceptance to complete ignorance. Therefore, in-depth understanding and interpretation of driver behavior and needs regarding the use of ADAS can significantly help developers to reflect on and improve the systems to meet the users’ expectations. Recently, the availability of data coming from the in-vehicle sensors network has increased significantly. The amount of received data potentially enables the in-depth quantitative driver behavior evaluation in a time-efficient and reliable way. Moreover, the ability of vehicle sensors and actuator data to synchronize the driver and system performance and assess the driving conditions in the moment of driver-system interaction can contribute to the comprehensive context-aware ADAS evaluation.\ua0 Developing methods for objective assessment of driver behavior is a task with a high level of complexity. This process requires (i) investigation of the driver behavior assessment area where vehicle data can be useful; (ii) identification of the influencing factors for evaluating ADAS functions; (iii) definition of the relevant data for the data-driven driver behavior evaluation; (iv) investigation of the ways to improve the feasibility of vehicle data. The research presented in this thesis focuses on the understanding of vehicle data applicability in user-related studies. The core of this research is the methodology for objective ADAS evaluation and a mixed-method approach that helps to integrate the quantitative methodologies into existing, mainly qualitative, evaluation practices.The conducted research revealed that vehicle data offers the possibility to determine individual user behavior, and to describe, categorize, and compare this to the average within a group. All of the above mentioned makes the applicability of vehicle data for user-related studies meaningful

    Human Motion Trajectory Prediction: A Survey

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    With growing numbers of intelligent autonomous systems in human environments, the ability of such systems to perceive, understand and anticipate human behavior becomes increasingly important. Specifically, predicting future positions of dynamic agents and planning considering such predictions are key tasks for self-driving vehicles, service robots and advanced surveillance systems. This paper provides a survey of human motion trajectory prediction. We review, analyze and structure a large selection of work from different communities and propose a taxonomy that categorizes existing methods based on the motion modeling approach and level of contextual information used. We provide an overview of the existing datasets and performance metrics. We discuss limitations of the state of the art and outline directions for further research.Comment: Submitted to the International Journal of Robotics Research (IJRR), 37 page

    Experiments of posture estimation on vehicles using wearable acceleration sensors

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    In this paper, we study methods to estimate drivers' posture in vehicles using acceleration data of wearable sensor and conduct a field test. Recently, sensor technologies have been progressed. Solutions of safety management to analyze vital data acquired from wearable sensor and judge work status are proposed. To prevent huge accidents, demands for safety management of bus and taxi are high. However, acceleration of vehicles is added to wearable sensor in vehicles, and there is no guarantee to estimate drivers' posture accurately. Therefore, in this paper, we study methods to estimate driving posture using acceleration data acquired from T-shirt type wearable sensor hitoe, conduct field tests and implement a sample application.Comment: 4 pages, 4 figures, The 3rd IEEE International Conference on Big Data Security on Cloud (BigDataSecurity 2017), pp.14-17, Beijing, May 201

    Map-Guided Curriculum Domain Adaptation and Uncertainty-Aware Evaluation for Semantic Nighttime Image Segmentation

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    We address the problem of semantic nighttime image segmentation and improve the state-of-the-art, by adapting daytime models to nighttime without using nighttime annotations. Moreover, we design a new evaluation framework to address the substantial uncertainty of semantics in nighttime images. Our central contributions are: 1) a curriculum framework to gradually adapt semantic segmentation models from day to night through progressively darker times of day, exploiting cross-time-of-day correspondences between daytime images from a reference map and dark images to guide the label inference in the dark domains; 2) a novel uncertainty-aware annotation and evaluation framework and metric for semantic segmentation, including image regions beyond human recognition capability in the evaluation in a principled fashion; 3) the Dark Zurich dataset, comprising 2416 unlabeled nighttime and 2920 unlabeled twilight images with correspondences to their daytime counterparts plus a set of 201 nighttime images with fine pixel-level annotations created with our protocol, which serves as a first benchmark for our novel evaluation. Experiments show that our map-guided curriculum adaptation significantly outperforms state-of-the-art methods on nighttime sets both for standard metrics and our uncertainty-aware metric. Furthermore, our uncertainty-aware evaluation reveals that selective invalidation of predictions can improve results on data with ambiguous content such as our benchmark and profit safety-oriented applications involving invalid inputs.Comment: IEEE T-PAMI 202

    LESSONS IN EVALUATING COMMUNICATIONS CAMPAIGNS

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    Builds on the findings of the first and second papers. It examines specifically how campaigns with different purposes (individual behavior change and policy change) have been evaluated, and how evaluators have tackled some of the associated evaluation challenges that the first three papers raised as important to address. It features fi ve brief case studies in which the main unit of analysis is not the campaign, but the campaign's evaluation. The case studies provide a brief snapshot of the real experiences of campaign evaluations. The paper also features cross-case lessons that highlight important findings and themes
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