面向车路协同的路侧感知仿真系统
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广东省重点领域研发计划-新能源汽车专项(2019B090912002); 广州市科技计划-产业技术重大攻关计划-现代产业技术专题(201802010006); 广州市科技计划-对外科技合作计划-对外研发合作专题(201807010049)


Road-Side Sensing Simulation Toward Cooperative Vehicle Infrastructure System
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    摘要:

    路侧感知是车路协同应用开发的重要组成部分, 通过在路侧部署传感器, 将采集到的路面信息经V2X通信给到车辆, 使车辆拥有超视距的感知能力. 在实际应用中, 为达到最优的路侧感知效果, 不同的场景往往需要不同的RSU配置, RSU的选型及安装是一个耗时耗力的过程. 交通参与者的识别是路侧感知的核心, 基于机器学习的识别算法需要大量的标签数据, 而人工打标签被验证是一个效率极其低下的方式. 通过构建路侧感知仿真系统可以很好地解决RSU配置及样本数据生成的问题, 实验一通过在仿真系统中调整激光雷达的高度和角度, 得到极端情况下的车辆遮挡情况, 从而为激光雷达的实际安装高度提供参考, 实验二在仿真环境中输出带标签的激光雷达点云数据, 通过与实际采集的点云数据进行融合对比, 验证仿真系统输出的激光雷达点云数据可以作为模型训练的数据补充.

    Abstract:

    Road-side sensing is indispensable for a cooperative vehicle infrastructure system, through which vehicles could have sensing ability beyond the visual range by receiving road information via V2X communication. For the optimal sensing results in reality, RSU configuration needs to vary according to scenarios, which is both time consuming and labor intensive. Meanwhile, recognition of traffic participants based on machine learning is crucial to road-side sensing, requiring a huge amount of labeled data, and it is proven to be an inefficient way to label manually. However, these two problems can be solved by building a simulation system of road-side sensing. Experiment I shows the vehicle occlusion on extreme occasions by adjusting the height and orientation of lidar in the simulation system, which provides a recommended height for installment in reality. Experiment II proves the virtual data derived from the simulation system can be complementary to real data by mutual verification.

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郭云鹏,邹凯,陈升东,袁峰.面向车路协同的路侧感知仿真系统.计算机系统应用,2021,30(5):92-98

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  • 收稿日期:2020-09-11
  • 最后修改日期:2020-10-09
  • 在线发布日期: 2021-05-06
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