69,300 research outputs found

    End-to-end Learning of Driving Models from Large-scale Video Datasets

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    Robust perception-action models should be learned from training data with diverse visual appearances and realistic behaviors, yet current approaches to deep visuomotor policy learning have been generally limited to in-situ models learned from a single vehicle or a simulation environment. We advocate learning a generic vehicle motion model from large scale crowd-sourced video data, and develop an end-to-end trainable architecture for learning to predict a distribution over future vehicle egomotion from instantaneous monocular camera observations and previous vehicle state. Our model incorporates a novel FCN-LSTM architecture, which can be learned from large-scale crowd-sourced vehicle action data, and leverages available scene segmentation side tasks to improve performance under a privileged learning paradigm.Comment: camera ready for CVPR201

    Sharing Human-Generated Observations by Integrating HMI and the Semantic Sensor Web

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    Current “Internet of Things” concepts point to a future where connected objects gather meaningful information about their environment and share it with other objects and people. In particular, objects embedding Human Machine Interaction (HMI), such as mobile devices and, increasingly, connected vehicles, home appliances, urban interactive infrastructures, etc., may not only be conceived as sources of sensor information, but, through interaction with their users, they can also produce highly valuable context-aware human-generated observations. We believe that the great promise offered by combining and sharing all of the different sources of information available can be realized through the integration of HMI and Semantic Sensor Web technologies. This paper presents a technological framework that harmonizes two of the most influential HMI and Sensor Web initiatives: the W3C’s Multimodal Architecture and Interfaces (MMI) and the Open Geospatial Consortium (OGC) Sensor Web Enablement (SWE) with its semantic extension, respectively. Although the proposed framework is general enough to be applied in a variety of connected objects integrating HMI, a particular development is presented for a connected car scenario where drivers’ observations about the traffic or their environment are shared across the Semantic Sensor Web. For implementation and evaluation purposes an on-board OSGi (Open Services Gateway Initiative) architecture was built, integrating several available HMI, Sensor Web and Semantic Web technologies. A technical performance test and a conceptual validation of the scenario with potential users are reported, with results suggesting the approach is soun

    A novel Big Data analytics and intelligent technique to predict driver's intent

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    Modern age offers a great potential for automatically predicting the driver's intent through the increasing miniaturization of computing technologies, rapid advancements in communication technologies and continuous connectivity of heterogeneous smart objects. Inside the cabin and engine of modern cars, dedicated computer systems need to possess the ability to exploit the wealth of information generated by heterogeneous data sources with different contextual and conceptual representations. Processing and utilizing this diverse and voluminous data, involves many challenges concerning the design of the computational technique used to perform this task. In this paper, we investigate the various data sources available in the car and the surrounding environment, which can be utilized as inputs in order to predict driver's intent and behavior. As part of investigating these potential data sources, we conducted experiments on e-calendars for a large number of employees, and have reviewed a number of available geo referencing systems. Through the results of a statistical analysis and by computing location recognition accuracy results, we explored in detail the potential utilization of calendar location data to detect the driver's intentions. In order to exploit the numerous diverse data inputs available in modern vehicles, we investigate the suitability of different Computational Intelligence (CI) techniques, and propose a novel fuzzy computational modelling methodology. Finally, we outline the impact of applying advanced CI and Big Data analytics techniques in modern vehicles on the driver and society in general, and discuss ethical and legal issues arising from the deployment of intelligent self-learning cars

    Imitating Driver Behavior with Generative Adversarial Networks

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    The ability to accurately predict and simulate human driving behavior is critical for the development of intelligent transportation systems. Traditional modeling methods have employed simple parametric models and behavioral cloning. This paper adopts a method for overcoming the problem of cascading errors inherent in prior approaches, resulting in realistic behavior that is robust to trajectory perturbations. We extend Generative Adversarial Imitation Learning to the training of recurrent policies, and we demonstrate that our model outperforms rule-based controllers and maximum likelihood models in realistic highway simulations. Our model both reproduces emergent behavior of human drivers, such as lane change rate, while maintaining realistic control over long time horizons.Comment: 8 pages, 6 figure

    EEG-based mental workload neurometric to evaluate the impact of different traffic and road conditions in real driving settings

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    Car driving is considered a very complex activity, consisting of different concomitant tasks and subtasks, thus it is crucial to understand the impact of different factors, such as road complexity, traffic, dashboard devices, and external events on the driver’s behavior and performance. For this reason, in particular situations the cognitive demand experienced by the driver could be very high, inducing an excessive experienced mental workload and consequently an increasing of error commission probability. In this regard, it has been demonstrated that human error is the main cause of the 57% of road accidents and a contributing factor in most of them. In this study, 20 young subjects have been involved in a real driving experiment, performed under different traffic conditions (rush hour and not) and along different road types (main and secondary streets). Moreover, during the driving tasks different specific events, in particular a pedestrian crossing the road and a car entering the traffic flow just ahead of the experimental subject, have been acted. A Workload Index based on the Electroencephalographic (EEG), i.e., brain activity, of the drivers has been employed to investigate the impact of the different factors on the driver’s workload. Eye-Tracking (ET) technology and subjective measures have also been employed in order to have a comprehensive overview of the driver’s perceived workload and to investigate the different insights obtainable from the employed methodologies. The employment of such EEG-based Workload index confirmed the significant impact of both traffic and road types on the drivers’ behavior (increasing their workload), with the advantage of being under real settings. Also, it allowed to highlight the increased workload related to external events while driving, in particular with a significant effect during those situations when the traffic was low. Finally, the comparison between methodologies revealed the higher sensitivity of neurophysiological measures with respect to ET and subjective ones. In conclusion, such an EEG-based Workload index would allow to assess objectively the mental workload experienced by the driver, standing out as a powerful tool for research aimed to investigate drivers’ behavior and providing additional and complementary insights with respect to traditional methodologies employed within road safety research
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