基于四線激光雷達(dá)的道路信息提取與目標(biāo)檢測
發(fā)布時間：2018-08-18 11:21
【摘要】：為了保障無人駕駛車在行駛過程中的安全性與可靠性,利用四線激光雷達(dá)對道路信息進(jìn)行提取并對車輛前方的目標(biāo)進(jìn)行檢測。針對傳統(tǒng)的基于密度的DBSCAN算法對輸入?yún)?shù)敏感、僅適用于單一密度數(shù)據(jù)集等缺點,提出了一種利用k-最近鄰方法改進(jìn)的DBSCAN算法,使算法參數(shù)(Eps,Minpts)可以根據(jù)數(shù)據(jù)集特點進(jìn)行自適應(yīng)的選取。并且根據(jù)激光雷達(dá)掃描到路沿上的數(shù)據(jù)集特點,提出基于共線點的二次提取算法,將路沿數(shù)據(jù)集準(zhǔn)確的提取出來,并將車輛前方的道路劃分為可行駛區(qū)域與不可行駛區(qū)域;在可行駛區(qū)域內(nèi),利用改進(jìn)后的聚類算法檢測道路中的障礙物。實車實驗表明,本文所提出的算法穩(wěn)定性強,在道路信息的提取與目標(biāo)檢測方面具有很好的實時性與準(zhǔn)確性。
[Abstract]:In order to ensure the safety and reliability of the driverless vehicle in the course of driving, the road information is extracted by four-line lidar and the target in front of the vehicle is detected. Aiming at the disadvantages of traditional density-based DBSCAN algorithm which is sensitive to input parameters and only suitable for single density data set, an improved DBSCAN algorithm based on k- nearest neighbor method is proposed. The algorithm parameters (Eps-Minpts) can be selected adaptively according to the characteristics of the data set. According to the characteristics of the data set scanned to the road edge by lidar, a second extraction algorithm based on collinear points is proposed to extract the road edge data set accurately, and the road in front of the vehicle can be divided into driving area and non-driving area. The improved clustering algorithm is used to detect the obstacles in the driving region. Real vehicle experiments show that the proposed algorithm is stable and has good real-time and accuracy in road information extraction and target detection.
【作者單位】： 北京工業(yè)大學(xué)城市交通學(xué)院;
【基金】：北京市教委基金項目資助課題(JJ002790200802)
【分類號】：TN958.98;U463.6
[Abstract]:In order to ensure the safety and reliability of the driverless vehicle in the course of driving, the road information is extracted by four-line lidar and the target in front of the vehicle is detected. Aiming at the disadvantages of traditional density-based DBSCAN algorithm which is sensitive to input parameters and only suitable for single density data set, an improved DBSCAN algorithm based on k- nearest neighbor method is proposed. The algorithm parameters (Eps-Minpts) can be selected adaptively according to the characteristics of the data set. According to the characteristics of the data set scanned to the road edge by lidar, a second extraction algorithm based on collinear points is proposed to extract the road edge data set accurately, and the road in front of the vehicle can be divided into driving area and non-driving area. The improved clustering algorithm is used to detect the obstacles in the driving region. Real vehicle experiments show that the proposed algorithm is stable and has good real-time and accuracy in road information extraction and target detection.
【作者單位】： 北京工業(yè)大學(xué)城市交通學(xué)院;
【基金】：北京市教委基金項目資助課題(JJ002790200802)
【分類號】：TN958.98;U463.6
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