CAJ | 학술논문

为了使无人驾驶车获得可行驶区域和障碍物信息,通过分析大量激光雷达扫描点数据,总结并得出路沿数据点独有的特征,提出一种基于路沿数据点特征和多层融合技术的路沿检测算法。应用 Dezert-Smarandache 理论(Dezert-Smarandache theory,DSmT)对无人驾驶车前方道路环境建立栅格地图,并利用证据理论中的冲突系数检测动态障碍物。最后,采用膨胀算法、侵蚀算法和改进的八邻域区域标记算法对动态障碍物进行聚类和信息提取。实车实验结果表明：本算法可稳定、准确地感知无人驾驶车周围环境信息。
위료사무인가사차획득가행사구역화장애물신식,통과분석대량격광뢰체소묘점수거,총결병득출로연수거점독유적특정,제출일충기우로연수거점특정화다층융합기술적로연검측산법。응용 Dezert-Smarandache 이론(Dezert-Smarandache theory,DSmT)대무인가사차전방도로배경건립책격지도,병이용증거이론중적충돌계수검측동태장애물。최후,채용팽창산법、침식산법화개진적팔린역구역표기산법대동태장애물진행취류화신식제취。실차실험결과표명：본산법가은정、준학지감지무인가사차주위배경신식。
To obtain the information of drivable area and obstacles for a driverless car, unique characteristics of the road edge data points were summarized and concluded by analyzing a large number of scanning lidar data, and a road edge detection algorithm was proposed based on the features of road edge data points and multi-layer fusion technology. DSmT was applied to establish a grid map for the road environment in front of the unmanned vehicle. The DSmT conflict coefficient was used to detect dynamic obstacles. Finally, the clustering and information extraction of dynamic obstacles was completed by the expansion algorithm, erosion algorithm, and the improved eight neighborhood labeling algorithm. Results show that the algorithm can stably and accurately perceive the environment information around driverless vehicle.