School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255000, China.
Collaborative Innovation Center of New Energy Automotive, Shandong University of Technology, Zibo 255000, China.
Sensors (Basel). 2023 May 19;23(10):4920. doi: 10.3390/s23104920.
Road obstacle detection is an important component of intelligent assisted driving technology. Existing obstacle detection methods ignore the important direction of generalized obstacle detection. This paper proposes an obstacle detection method based on the fusion of roadside units and vehicle mounted cameras and illustrates the feasibility of a combined monocular camera inertial measurement unit (IMU) and roadside unit (RSU) detection method. A generalized obstacle detection method based on vision IMU is combined with a roadside unit obstacle detection method based on a background difference method to achieve generalized obstacle classification while reducing the spatial complexity of the detection area. In the generalized obstacle recognition stage, a VIDAR (Vision-IMU based identification and ranging) -based generalized obstacle recognition method is proposed. The problem of the low accuracy of obstacle information acquisition in the driving environment where generalized obstacles exist is solved. For generalized obstacles that cannot be detected by the roadside unit, VIDAR obstacle detection is performed on the target generalized obstacles through the vehicle terminal camera, and the detection result information is transmitted to the roadside device terminal through the UDP (User Data Protocol) protocol to achieve obstacle recognition and pseudo-obstacle removal, thereby reducing the error recognition rate of generalized obstacles. In this paper, pseudo-obstacles, obstacles with a certain height less than the maximum passing height of the vehicle, and obstacles with a height greater than the maximum passing height of the vehicle are defined as generalized obstacles. Pseudo-obstacles refer to non-height objects that appear to be "patches" on the imaging interface obtained by visual sensors and obstacles with a height less than the maximum passing height of the vehicle. VIDAR is a vision-IMU-based detection and ranging method. IMU is used to obtain the distance and pose of the camera movement, and through the inverse perspective transformation, it can calculate the height of the object in the image. The VIDAR-based obstacle detection method, the roadside unit-based obstacle detection method, YOLOv5 (You Only Look Once version 5), and the method proposed in this paper were applied to outdoor comparison experiments. The results show that the accuracy of the method is improved by 2.3%, 17.4%, and 1.8%, respectively, compared with the other four methods. Compared with the roadside unit obstacle detection method, the speed of obstacle detection is improved by 1.1%. The experimental results show that the method can expand the detection range of road vehicles based on the vehicle obstacle detection method and can quickly and effectively eliminate false obstacle information on the road.
道路障碍物检测是智能辅助驾驶技术的重要组成部分。现有的障碍物检测方法忽略了广义障碍物检测的重要方向。本文提出了一种基于路边单元和车载摄像机融合的障碍物检测方法，并说明了基于单目摄像机惯性测量单元（IMU）和路边单元（RSU）的组合检测方法的可行性。提出了一种基于视觉 IMU 的广义障碍物检测方法与基于背景差分法的路边单元障碍物检测方法相结合，在实现广义障碍物分类的同时，降低了检测区域的空间复杂度。在广义障碍物识别阶段，提出了一种基于视觉-IMU 的 VIDAR（基于视觉-IMU 的识别和测距）的广义障碍物识别方法。解决了在存在广义障碍物的驾驶环境中障碍物信息获取精度低的问题。对于路边单元无法检测到的广义障碍物，通过车载终端摄像机对目标广义障碍物进行 VIDAR 障碍物检测，并通过 UDP（用户数据报协议）协议将检测结果信息传输到路边设备终端，实现障碍物识别和伪障碍物去除，从而降低广义障碍物的错误识别率。在本文中，将一定高度小于车辆最大通行高度的障碍物和高度大于车辆最大通行高度的障碍物定义为广义障碍物。伪障碍物是指视觉传感器获得的成像界面上出现的“补丁”状非高度物体和高度小于车辆最大通行高度的障碍物。VIDAR 是一种基于视觉-IMU 的检测和测距方法。IMU 用于获取相机运动的距离和姿态，通过逆透视变换，可以计算出图像中物体的高度。基于 VIDAR 的障碍物检测方法、基于路边单元的障碍物检测方法、YOLOv5（你只看一次版本 5）和本文提出的方法在户外对比实验中进行了应用。结果表明，与其他四种方法相比，该方法的准确率分别提高了 2.3%、17.4%和 1.8%。与基于路边单元的障碍物检测方法相比，障碍物检测速度提高了 1.1%。实验结果表明，该方法可以扩展基于车辆障碍物检测方法的道路车辆检测范围，并能快速有效地消除道路上的虚假障碍物信息。
