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首页> 外文期刊>Promet-traffic & transportation >NEURAL NETWORK BASED VEHICULAR LOCATION PREDICTION MODEL FOR COOPERATIVE ACTIVE SAFETY SYSTEMS
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NEURAL NETWORK BASED VEHICULAR LOCATION PREDICTION MODEL FOR COOPERATIVE ACTIVE SAFETY SYSTEMS

机译：基于神经网络的合作主动安全系统车辆位置预测模型

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摘要

Safety systems detect unsafe conditions and provide warnings for travellers to take action and avoid crashes. Estimation of the geographical location of a moving vehicle as to where it will be positioned next with high precision and short computation time is crucial for identifying dangers. To this end, navigational and dynamic data of a vehicle are processed in connection with the data received from neighbouring vehicles and infrastructure in the same vicinity. In this study, a vehicular location prediction model was developed using an artificial neural network for cooperative active safety systems. The model is intended to have a constant, shorter computation time as well as higher accuracy features. The performance of the proposed model was measured with a real-time testbed developed in this study. The results are compared with the performance of similar studies and the proposed model is shown to deliver a better performance than other models.
机译：安全系统检测不安全状况，并向旅行者提供警告，以采取行动并避免撞车。对于移动车辆的下一个位置的高精度，短计算时间的估计对于识别危险至关重要。为此，结合从相邻车辆和相同附近的基础设施接收的数据来处理车辆的导航和动态数据。在这项研究中，使用人工神经网络为协作式主动安全系统开发了车辆位置预测模型。该模型旨在具有恒定的，较短的计算时间以及较高的精度特征。建议的模型的性能是通过在本研究中开发的实时测试平台进行测量的。将结果与类似研究的性能进行比较，并且所提出的模型显示出比其他模型更好的性能。

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