随着智能交通和高级驾驶辅助系统的火热发展,如何根据车辆轨迹数据生成高精地图成为业界的一大难题。轨迹相似性计算与相似轨迹提取是利用相似轨迹推理剩余车道位置的重要步骤,其正确率极大地影响车道位置的精度。传统最长公共子序列(lo...随着智能交通和高级驾驶辅助系统的火热发展,如何根据车辆轨迹数据生成高精地图成为业界的一大难题。轨迹相似性计算与相似轨迹提取是利用相似轨迹推理剩余车道位置的重要步骤,其正确率极大地影响车道位置的精度。传统最长公共子序列(longest common subsequence,LCSS)算法多用于计算重叠轨迹的相似性,针对此问题,根据采集的车道轨迹互相平行且保持固定距离的特点,提出一种适用于提取此类相似轨迹的改进LCSS方法。首先构建缓冲区,筛选可能相似的轨迹,然后利用基于平移和重采样的轨迹对齐策略使两条轨迹在时空中同步,最后基于LCSS计算两条轨迹的相似性,当相似度满足阈值条件时,判定该轨迹对相似。对比实验表明该方法能有效地提取相似轨迹。展开更多
Taking autonomous driving and driverless as the research object,we discuss and define intelligent high-precision map.Intelligent high-precision map is considered as a key link of future travel,a carrier of real-time p...Taking autonomous driving and driverless as the research object,we discuss and define intelligent high-precision map.Intelligent high-precision map is considered as a key link of future travel,a carrier of real-time perception of traffic resources in the entire space-time range,and the criterion for the operation and control of the whole process of the vehicle.As a new form of map,it has distinctive features in terms of cartography theory and application requirements compared with traditional navigation electronic maps.Thus,it is necessary to analyze and discuss its key features and problems to promote the development of research and application of intelligent high-precision map.Accordingly,we propose an information transmission model based on the cartography theory and combine the wheeled robot’s control flow in practical application.Next,we put forward the data logic structure of intelligent high-precision map,and analyze its application in autonomous driving.Then,we summarize the computing mode of“Crowdsourcing+Edge-Cloud Collaborative Computing”,and carry out key technical analysis on how to improve the quality of crowdsourced data.We also analyze the effective application scenarios of intelligent high-precision map in the future.Finally,we present some thoughts and suggestions for the future development of this field.展开更多
文摘随着智能交通和高级驾驶辅助系统的火热发展,如何根据车辆轨迹数据生成高精地图成为业界的一大难题。轨迹相似性计算与相似轨迹提取是利用相似轨迹推理剩余车道位置的重要步骤,其正确率极大地影响车道位置的精度。传统最长公共子序列(longest common subsequence,LCSS)算法多用于计算重叠轨迹的相似性,针对此问题,根据采集的车道轨迹互相平行且保持固定距离的特点,提出一种适用于提取此类相似轨迹的改进LCSS方法。首先构建缓冲区,筛选可能相似的轨迹,然后利用基于平移和重采样的轨迹对齐策略使两条轨迹在时空中同步,最后基于LCSS计算两条轨迹的相似性,当相似度满足阈值条件时,判定该轨迹对相似。对比实验表明该方法能有效地提取相似轨迹。
基金National Key Research and Development Program(No.2018YFB1305001)Major Consulting and Research Project of Chinese Academy of Engineering(No.2018-ZD-02-07)。
文摘Taking autonomous driving and driverless as the research object,we discuss and define intelligent high-precision map.Intelligent high-precision map is considered as a key link of future travel,a carrier of real-time perception of traffic resources in the entire space-time range,and the criterion for the operation and control of the whole process of the vehicle.As a new form of map,it has distinctive features in terms of cartography theory and application requirements compared with traditional navigation electronic maps.Thus,it is necessary to analyze and discuss its key features and problems to promote the development of research and application of intelligent high-precision map.Accordingly,we propose an information transmission model based on the cartography theory and combine the wheeled robot’s control flow in practical application.Next,we put forward the data logic structure of intelligent high-precision map,and analyze its application in autonomous driving.Then,we summarize the computing mode of“Crowdsourcing+Edge-Cloud Collaborative Computing”,and carry out key technical analysis on how to improve the quality of crowdsourced data.We also analyze the effective application scenarios of intelligent high-precision map in the future.Finally,we present some thoughts and suggestions for the future development of this field.
