本文選題：多目標雷達 + 車道劃分��； 參考：《計算機應(yīng)用》2017年10期

【摘要】：利用多目標交通測速雷達進行交通執(zhí)法時,只有正確地判斷出車輛所在的車道,抓拍照片才能作為交通執(zhí)法的依據(jù)。傳統(tǒng)的分車道方法主要通過人工測量的固定閾值以及坐標系旋轉(zhuǎn)的方法來達到車道劃分的目的,但這種方法誤差較大并且不易于操作�；诮y(tǒng)計和密度特征的核聚類算法(K-CSDF)分兩步進行:首先對雷達獲取的車輛數(shù)據(jù)進行特征提取,包括基于統(tǒng)計特征的閾值處理和基于密度特征的動態(tài)半徑提取;然后引入基于核的相似性的動態(tài)聚類算法對篩選出的有效點進行聚類。通過和高斯混合模型(GMM)算法以及自組織映射神經(jīng)網(wǎng)絡(luò)(SOM)算法進行仿真對比表明:當只取100個有效點進行聚類時,K-CSDF和SOM算法能達到90%以上的分車道正確率,而GMM算法不能給出車道中心線;在算法用時上,當取1000個有效點時,K-CSDF和GMM算法用時均小于1 s,可以保證實時性,而SOM算法則需要2.5 s左右;在算法魯棒性上,K-CSDF對不均勻樣本的適應(yīng)性優(yōu)于這兩種算法。當取不同數(shù)量的有效點進行聚類時,K-CSDF可以達到95%以上的平均分車道正確率。
[Abstract]:When the multi-target traffic velocity radar is used to enforce traffic law, only by correctly judging the lane where the vehicle is located and taking pictures can it be used as the basis of traffic law enforcement. The traditional lane separation method mainly achieves the purpose of lane division by manual measurement of fixed threshold and rotation of coordinate system, but this method has a large error and is difficult to operate. The kernel clustering algorithm based on statistical and density features is divided into two steps: firstly, the vehicle data acquired by radar are extracted, including threshold processing based on statistical features and dynamic radius extraction based on density features; Then the kernel similarity based dynamic clustering algorithm is introduced to cluster the selected valid points. The simulation results with Gao Si hybrid model (GMM) algorithm and self-organizing mapping neural network (Som) algorithm show that the K-CSDF and SOM algorithms can achieve more than 90% accuracy of lane separation when only 100 effective points are taken for clustering. However, the GMM algorithm can not give the center line of the driveway, and when 1000 effective points are taken, the time of both K-CSDF and GMM algorithm is less than 1 s, which can guarantee the real time, while the SOM algorithm needs about 2.5 seconds. In terms of robustness, K-CSDF is better than these two algorithms in its adaptability to heterogeneous samples. When different number of effective points are used for clustering, K-CSDF can achieve an average accuracy of more than 95%.
【作者單位】： 北京信息科技大學通信工程系;北京川速微波科技有限公司;
【基金】：國家自然科學基金資助項目(61671069) 北京高等學校高水平人才交叉培養(yǎng)項目~~
【分類號】：TN958;U495
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本文編號：1896526