基于激光传感器的无人驾驶车辆动态障碍物检测、跟踪与识别方法

Hualei Zhang, M. Ikbal
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引用次数: 3

摘要

针对这些不足,本文提出了一种基于多特征融合的动态障碍物检测与跟踪方法和一种基于时空特征向量的动态障碍物识别方法。设计/方法/方法现有的基于几何特征的动态障碍物检测和跟踪方法存在较高的误检率。基于动态障碍物几何特征和运动状态的识别方法受距离和扫描角度的影响较大,不能满足真实交通场景的应用要求。研究发现:首先,基于动态障碍物的几何特征,考虑障碍物,利用回波脉冲宽度特征提高障碍物检测和跟踪的精度;其次,基于障碍物的时间维度和空间维度信息构建时空特征向量,然后利用支持向量机方法实现对动态障碍物的识别,提高障碍物目标识别的精度。最后,通过实车试验验证了所提方法的准确性和有效性。提出了一种基于多特征融合的动态障碍物检测与跟踪方法和一种基于时空特征向量的动态障碍物识别方法。通过实车试验验证了该方法的准确性和有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Unmanned vehicle dynamic obstacle detection, tracking and recognition method based on laser sensor
PurposeIn response to these shortcomings, this paper proposes a dynamic obstacle detection and tracking method based on multi-feature fusion and a dynamic obstacle recognition method based on spatio-temporal feature vectors.Design/methodology/approachThe existing dynamic obstacle detection and tracking methods based on geometric features have a high false detection rate. The recognition methods based on the geometric features and motion status of dynamic obstacles are greatly affected by distance and scanning angle, and cannot meet the requirements of real traffic scene applications.FindingsFirst, based on the geometric features of dynamic obstacles, the obstacles are considered The echo pulse width feature is used to improve the accuracy of obstacle detection and tracking; second, the space-time feature vector is constructed based on the time dimension and space dimension information of the obstacle, and then the support vector machine method is used to realize the recognition of dynamic obstacles to improve the obstacle The accuracy of object recognition. Finally, the accuracy and effectiveness of the proposed method are verified by real vehicle tests.Originality/valueThe paper proposes a dynamic obstacle detection and tracking method based on multi-feature fusion and a dynamic obstacle recognition method based on spatio-temporal feature vectors. The accuracy and effectiveness of the proposed method are verified by real vehicle tests.
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