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TrajData: On Vehicle Trajectory Collection With Commodity Plug-and-Play OBU Devices
IEEE Internet of Things Journal ( IF 10.6 ) Pub Date : 2020-06-11 , DOI: 10.1109/jiot.2020.3001566
Zhu Xiao , Fancheng Li , Ronghui Wu , Hongbo Jiang , Yupeng Hu , Ju Ren , Chenglin Cai , Arun Iyengar

For years, vehicle trajectory data have increasingly been important for a wide range of applications, from driver behavior investigation/classification, travel time/distance estimation, and routing in vehicular networks, to vehicle energy/emission evaluation. This article presents TrajData, the first systematic solution to reliable vehicle trajectory data collection, with only reliance on commercial-off-the-shelf (COTS) onboard unit (OBU) devices that utilize lightweight GPS modules and low-cost onboard diagnostics (OBD) readers. In the practical use of trajectory collection, GPS outages inevitably occur in urban environments thereby leading to large trajectory errors as well as missing vehicle location data. To resolve this, we propose a novel data-fusion-enabled deep learning approach with the purpose of achieving reliable vehicle trajectory collection in various urban road conditions. Specifically, we leverage motion information retrieved from OBD readers in TrajData to help reconstruct the trajectory data during GPS outages. By investigating the changes of direction angle from the OBD readings, we can identify different types of road sections. Furthermore, we integrate the neural arithmetic logic units (NALUs) into our trajectory reconstruction model to tame the challenges when GPS outages take place in various road sections. Experimental results from realistic data have demonstrated the effectiveness and reliability of the proposed method. In the road test, TrajData achieves an average position error below 15-m around a 60-s GPS outage, even in complex road sections, i.e., continuous turns and driving with accelerations/decelerations resulting in frequent changes of direction and speed.

中文翻译:

TrajData:使用商品即插即用OBU设备进行车辆轨迹收集

多年来,车辆轨迹数据对于从驾驶员行为调查/分类,行进时间/距离估计,车辆网络中的路线选择到车辆能量/排放评估等广泛的应用,都越来越重要。本文介绍TrajData,这是可靠的车辆轨迹数据收集的首个系统解决方案,仅依靠利用轻型GPS模块和低成本车载诊断程序(OBD)的商用现货(COTS)车载单元(OBU)设备读者。在轨迹收集的实际使用中,不可避免地在城市环境中发生GPS中断,从而导致较大的轨迹误差以及丢失的车辆位置数据。为了解决这个问题,我们提出了一种新颖的基于数据融合的深度学习方法,目的是在各种城市道路条件下实现可靠​​的车辆轨迹收集。具体来说,我们利用TrajData中从OBD读取器获取的运动信息来帮助在GPS中断期间重建轨迹数据。通过调查OBD读数中方向角的变化,我们可以识别不同类型的路段。此外,我们将神经算术逻辑单元(NALU)集成到我们的轨迹重建模型中,以应对GPS在不同路段发生故障时的挑战。实际数据的实验结果证明了该方法的有效性和可靠性。在路试中,TrajData在60秒的GPS中断期间实现了15米以下的平均位置误差,
更新日期:2020-06-11
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