20,763 research outputs found

    Towards the development of a User Interface to model scenarios on driving Simulators

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    International audienceScenario Modeling on driving simulator requires careful consideration and controlled environment (depending on the research objectives) to achieve the desired goal of the experiment. It is one of the critical steps while designing and implementing an experiment on a driving simulator. It specifies where and what happens in the simulator by specifying, where to place the virtual objects and what those objects will be doing during the experimental trials. But complex and technical nature of driving simulator makes it difficult for the end-users (behavioral researchers/trainers) to design and execute and experimental protocol

    Development of Driving Simulation Scenarios Based on Building Information Modeling (BIM) for Road Safety Analysis

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    [EN] The analysis of road safety is critical in road design. Complying to guidelines is not enough to ensure the highest safety levels, so many of them encourage designers to virtually recreate and test their roads, benefitting from the evolution of driving simulators in recent years. However, an accurate recreation of the road and its environment represents a real bottleneck in the process. A very important limitation lies in the diversity of input data, from different sources and requiring specific adaptations for every single simulator. This paper aims at showing a framework for recreating faster virtual scenarios by using an Industry Foundation Classes (IFC)-based file. This methodology was compared to two other conventional methods for developing driving scenarios. The main outcome of this study has demonstrated that with a data exchange file in IFC format, virtual scenarios can be faster designed to carry out safety audits with driving simulators. As a result, the editing, programming, and processing times were substantially reduced using the proposed IFC exchange file format through a BIM (Building Information Modeling) model. This methodology facilitates cost-savings, execution, and optimization resources in road safety analysis.This research was funded by the Spanish Center for Industrial Technological Development (CDTI), as well as the European Regional Development Fund, grant number EXP-00091379/ITC-20161077.Dols Ruiz, JF.; Molina, J.; Camacho-Torregrosa, FJ.; Llopis-Castelló, D.; García García, A. (2021). Development of Driving Simulation Scenarios Based on Building Information Modeling (BIM) for Road Safety Analysis. Sustainability. 13(4):1-21. https://doi.org/10.3390/su13042039S12113

    Modeliranje ljudske vožnje primjenom po dijelovima linearnog modela

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    This paper presents development of the modeling strategy of the human driving behavior based on the expression as Piecewise Linear (PWL) model focusing on the driver\u27s stopping maneuver. The driving data are collected by using the three-dimensional driving simulator based on CAVE Automatic Virtual Environment (CAVE), which provides stereoscopic immersive virtual environment. In our modeling, the control scenario of the human driver, that is, the mapping from the driver\u27s sensory information to the operation of the driver such as acceleration, braking and steering, is expressed by Piecewise Linear (PWL) model. Since the PWL model includes both continuous behaviors given by polynomials and discrete logical conditions, it can be regarded as a class of Hybrid Dynamical System (HDS). The identification problem for the PWL model is formulated as the Mixed Integer Linear Programming (MILP) by transforming the switching conditions into binary variables. From the obtained results, it is found that the driver appropriately switches the \u27control law\u27 according to the sensory information. These results enable us to capture not only the physical meaning of the driving skill, but also the decision-making aspect (switching conditions) in the driver\u27s stopping maneuver.Ovaj članak prikazuje razvoj strategije modeliranja ljudskog ponašanja pri vožnji, koja je utemeljena na po dijelovima linearnom (PWL) modelu fokusiranom na vozačev manevar zaustavljanja. Podaci o vožnji prikupljeni su korištenjem trodimenzionalnog simulatora vožnje zasnovanog na CAVE Automatic Virtual Environment (CAVE) koji osigurava potpuno stereoskopsko virtualno okruženje. Pri modeliranju je upravljački scenarij za vozača, odnosno preslikavanje vozačevih senzorskih informacija u operacije poput ubrzanja, kočenja i upravljanja vozilom, opisan PWL modelom. Kako PWL model uključuje istodobno kontinuirano ponašanje izraženo preko polinoma kao i diskretne logičke uvjete, takav se model može promatrati kao klasa hibridnih dinamičkih sustava (HDS). Transformiranjem uvjeta prekapčanja u binarne varijable, problem identifikacije PWL modela formuliran je kao mješoviti cjelobrojni linearni program (MILP). Iz dobivenih je rezultata vidljivo da vozač prekapča »zakon upravljanja« u skladu sa senzorskim informacijama. Rezultati omogućuju razumijevanje ne samo fizikalnog značenja sposobnosti vožnje već i sam aspekt donošenja odluka (uvjeta prekapčanja) prilikom vozačevog manevra zaustavljanja

    Virtual to Real Reinforcement Learning for Autonomous Driving

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    Reinforcement learning is considered as a promising direction for driving policy learning. However, training autonomous driving vehicle with reinforcement learning in real environment involves non-affordable trial-and-error. It is more desirable to first train in a virtual environment and then transfer to the real environment. In this paper, we propose a novel realistic translation network to make model trained in virtual environment be workable in real world. The proposed network can convert non-realistic virtual image input into a realistic one with similar scene structure. Given realistic frames as input, driving policy trained by reinforcement learning can nicely adapt to real world driving. Experiments show that our proposed virtual to real (VR) reinforcement learning (RL) works pretty well. To our knowledge, this is the first successful case of driving policy trained by reinforcement learning that can adapt to real world driving data

    Are You Used To It Yet? Braking Performance and Adaptation in a Fixed-base Driving Simulator.

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    During braking-to-stop manoeuvres in a fixed-base driving simulator, the paucity of visual and vestibular cues can lead to driver misperception and produce different patterns of braking response between real and simulated driving. For these reasons, drivers must adapt their behaviour in a simulator to affect a comfortable and efficient braking manoeuvre. Such behavioural adaptation is likely to have negative consequences by increasing a driver’s attentional demand. In this study, 48 participants underwent a series of braking-to-stop manoeuvres in an instrumented vehicle on a test-track. Each participant was instructed to drive at 40mph. A set of traffic lights, on occasions, changed to red as the vehicle was 58m from the lights. Deceleration profiles provided the baseline data. The same scenario was modelled in a fixed-base driving simulator. Two groups, each of 24 participants, one familiar with the simulator from previous investigations and one with no prior simulator experience, underwent the simulated traffic light scenario ten times. This paper suggests a method of objectively assessing driver braking performance between the real and simulated environments. Results appear to suggest that in as little as five or six practice stops drivers can adapt their simulator driving style to closely match that observed in a real vehicle on a test track. However, any process of adaptation from prior exposure to the simulator is short-lived
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