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中文摘要：
为了检测车辆目标,提出了一种基于主被动传感器融合的车辆检测方法.将车辆检测分为假设和验证假设两个步骤,在假设阶段,通过被动传感器——毫米波雷达进行目标的检测与跟踪,在对雷达数据进行最邻近法聚类后,在多假设跟踪模型下,将观测目标集与通过卡尔曼滤波器预测的目标集进行数据关联,得到雷达目标.在验证假设阶段,首先通过新出现的雷达目标找出车辆可能存在的区域,然后通过训练好的分类器对这些区域进行验证得到最终的车辆目标.在实验室的无人自主车平台上,本系统在城市道路和乡村道路环境下进行了大量实验,结果表明本文的方法可以有效地检测并跟踪到车辆目标,得到目标的距离和速度信息,从而帮助自主平台实现更多功能.
英文摘要：
To detect vehicles on road ,a vehicle detection method based on active and passive vision was proposed .The system contained two stages ,hypothesis generation (HG) and hypothesis verifica‐tion (HV) .In the HG stage ,the active sensor ,microwave radar was used to detect and track the tar‐gets .First the NN clustering method was used to deal with the radar data ,and then under the frame‐work of multiple hypothesis tracking ,the observations and predictions calculated by Kalman were as‐sociated to obtain the radar targets .In the HV stage ,the newly appeared radar targets were used to find regions of interests (ROIs) in image ,and the vehicles were detected by verifying the ROIs through a classifier trained offline .Lots of experiments were conducted on the platform of intelligent vehicle in the lab .The results suggest that for both city and country roads ,this method can effective‐ly detect and track target vehicles and can get the targets′range and speed ,w hich can help the plat‐form achieve more functions .
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