【摘要】：當車輛在各種不同的路面上行駛時,獲知路面類型信息將有助于提高乘車人的安全性和舒適性,不同的路面類型將對車輛的加速、制動及操控等駕駛策略產(chǎn)生影響。基于機器學習的基本原理,提出一種使用加速度傳感器和相機特征數(shù)據(jù)融合對路面類型進行分類的方法,并與單獨使用其中一種傳感器進行了比較。使用垂直加速度和車速數(shù)據(jù)并利用車輛動態(tài)模型還原路面輪廓,進而完成特征提取和路面類型分類;對相機采集的路面圖像數(shù)據(jù)進行特征提取和分類;將兩類傳感器的數(shù)據(jù)特征進行融合,完成路面類型識別任務。實驗結果表明:使用兩種傳感器數(shù)據(jù)特征融合的方法,不但識別精度有所提高,而且其可靠性和適應性也都優(yōu)于單獨使用加速度數(shù)據(jù)或路面圖像數(shù)據(jù)。
[Abstract]:When vehicles travel on different roads, knowing the information of road types will help to improve the safety and comfort of passengers, and different road types will affect the driving strategies of vehicles such as acceleration, braking and handling. Based on the basic principle of machine learning, a method for classification of road surface types using accelerometer and camera feature data fusion is proposed and compared with one of the sensors. Using the vertical acceleration and speed data and using the vehicle dynamic model to restore the road contour, then complete the feature extraction and road type classification, and carry on the feature extraction and classification to the road surface image data collected by the camera. The data features of the two types of sensors are fused to complete the road type identification task. The experimental results show that not only the recognition accuracy is improved, but also the reliability and adaptability are better than the acceleration data or road image data by using the two kinds of sensor data feature fusion method.
【作者單位】： 長春理工大學光電工程學院光電工程國家級實驗教學示范中心;
【基金】：吉林省自然科學基金項目(20150101047JC)
【分類號】：TP391.41;U463.6
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本文編號：2447380