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Cooperative vehicle positioning with multi-sensor data fusion and vehicular communications

机译:具有多传感器数据融合和车辆通信的协同车辆定位

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摘要

Vehicular positioning with multi-sensor fusion has achieved promising results in recent years. Having potential benefit from the emerging vehicular communications based on IEEE 802.11p dedicated short-range communication (DSRC), cooperative positioning opens new opportunities to support several vehicular applications. In addition, radar based active safety functions and GPS are being integrated into modern vehicles. With the availability of information from multiple sources, exchange of sensor information and multi-sensor fusion can be applied to obtain the precise positioning of vehicle without substantial additional cost. However, the main challenge in data fusion is the inherent data association problem due to dissimilar measurement update rate of DSRC and automotive radar. To overcome these challenges, this paper proposes a robust positioning approach considering track-to-track matching and fusion of position information obtained from multiple on-board sources such as GPS receiver, Vehicle-to-vehicle communication and automotive radar. Realistic 3D road traffic and wave propagation model is developed using a ray-tracing tool and the effectiveness of the proposed concept was evaluated. The system concept is validated by conducting extensive simulation considering realistic car following model. Results show that the proposed cooperative positioning method exhibits significant improvement in terms of positioning accuracy.
机译:近年来,具有多传感器融合的车辆定位已取得了可喜的成果。基于新兴的基于IEEE 802.11p专用短程通信(DSRC)的车辆通信可能带来的潜在好处,协作定位为支持多种车辆应用提供了新的机遇。此外,基于雷达的主动安全功能和GPS已集成到现代车辆中。利用来自多个来源的信息,可以应用传感器信息交换和多传感器融合来获得车辆的精确定位,而无需花费大量额外成本。然而,由于DSRC和汽车雷达的测量更新速率不同,数据融合中的主要挑战是固有的数据关联问题。为了克服这些挑战,本文提出了一种鲁棒的定位方法,该方法考虑了轨道间的匹配以及从多个车载源(例如GPS接收器,车对车通信和汽车雷达)获得的位置信息的融合。使用光线追踪工具开发了逼真的3D道路交通和波浪传播模型,并评估了所提出概念的有效性。该系统概念通过考虑现实的汽车跟随模型进行广泛的仿真来验证。结果表明,所提出的协同定位方法在定位精度方面有显着提高。

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