This paper proposes a fully automated approach to map and assess roadside clearance parameters using mobile Light Detection and Ranging (lidar) data on rural highways. Compared with traditional manual surveying methods, lidar data could provide a more efficient and cost-effective source to extract roadside information. This study proposes a novel voxel-based raycasting approach focused primarily on automating roadside mapping and assessment. First, the scanning vehicle trajectory is extracted. Pavement surface points are then detected, and a method is proposed to extract pavement edge trajectories. Once pavement edges are extracted, guardrails were identified using a conical frustum emitted from the edge trajectory points. Target points and flexion points are then generated and located on the roadside, and a voxel-based raycasting approach is used to search for roadside obstacles and query their locations. Finally, roadside slopes and embankment heights were mapped at specific intervals, and roadside design guidelines and requirements were automatically checked against the mapping results. Noncompliant locations with substandard conditions were automatically queried. The method was tested on four highway segments in Alberta, Canada. The accuracy of the edge detection reached up to 98.5%. Furthermore, the method proved to be accurate in object detection, being able to detect all obstructions on the roadside in each tested segment. The proposed method can help transportation authorities automatically map and inventory roadside clearance parameters. Moreover, the safety performance of existing road infrastructure can be studied using collected information and crash data to support decision making on road maintenance and upgrades.

本文提出了一种全自动方法，使用农村公路上的移动光检测和测距（激光雷达）数据来绘制和评估路边净空参数。与传统的人工测量方法相比，激光雷达数据可以提供更高效、更具成本效益的路边信息提取来源。这项研究提出了一种新的基于体素的光线投射方法，主要侧重于自动化路边测绘和评估。首先，提取扫描车辆轨迹。然后检测路面表面点，并提出一种提取路面边缘轨迹的方法。一旦提取了路面边缘，就使用从边缘轨迹点发出的圆锥截头体来识别护栏。然后生成目标点和弯曲点并将其定位在路边，并使用基于体素的光线投射方法来搜索路边障碍物并查询其位置。最后，以特定间隔绘制路边坡度和路堤高度，并根据绘制结果自动检查路边设计指南和要求。自动查询条件不合格的不合规位置。该方法在加拿大艾伯塔省的四个高速公路路段上进行了测试。边缘检测准确率高达98.5%。此外，该方法在物体检测方面被证明是准确的，能够检测到每个测试段中路边的所有障碍物。所提出的方法可以帮助交通当局自动绘制和清点路边清关参数。而且，