Traditionally, road asset management has required road surveyors to drive thousands of kilometres of road and record videos and visually assess roadside features. For both road asset maintenance and road safety risk analysis, these processes are time-consuming and, costly, with the outputs being subject to a large degree of human error.
Main Roads Western Australia engaged Anditi in 2020 to design and develop a more automated, accurate and cost-effective solution for road assessment, to work towards their target of assessing the safety rating of 80% of most travelled roads. The project involved analysing 2000kms of urban and rural roads using Mobile LiDAR and 360 Degree Imagery to produce the following products:
Road survey date & location
Median type and width e.g. physical raised median, painted median etc
Roadside severity
Centreline rumble strips
Intersection type
Street lighting
Property access points
AusRap/iRAP Coding
Part of the pilot study was to explore if TomTom's ‘off-the-shelf data’ could be used for the automated detection of road assets and safety attributes. This data typically covers roads that carry more than 80% of WA traffic and is captured every two to three years using a consistent capture platform. The data is also approximately one-tenth of the cost of commissioned mobile LiDAR and imagery making it a cost-effective data source for regularly updating the status of road assets and safety features.
Off-the-shelf LiDAR and 360-degree imagery from TomTom’s MoMa capture program were used to identify road assets and key safety attributes using our Anditi's in-house patented algorithms across 2000 km of Western Australian roads with different techniques used to extract features from imagery alone, LiDAR or a combination of both.
Satellite imagery was also used to identify and extract features such as roundabouts and access points.
Features detected using imagery included line markings, centrelines, trees, poles, street lights, safety barriers, protected safety barrier ends, centre and shoulder rumble strips, flexible posts, semi-rigid structures, and access points (commercial and residential).
Some challenges were experienced with the level of scatter and misalignment observed in the mobile LiDAR point cloud. These impacted the ability to automatically extract road assets and safety features consistently. As part of the study, research was undertaken to identify if the scatter and misalignment observed could be corrected. It was found that scatter and misalignment could be improved through additional processing and correction of the data.
To accommodate the scatter and misalignment issues in the mobile LiDAR and enable the pilot study to be completed with the data that was available, alternate techniques for identification were developed.
The processes adopted used a combination of automated and semi-automated extraction techniques. These were complemented with a manual checking process using specialised raster images that were used for quality control of identified key Items and Attributes.
Key road assets and safety attributes targeted as part of the pilot study were successfully identified in the mobile LiDAR and 360-degree imagery. These attributes were then successfully extracted into an AusRAP/ iRAP compatible format for future use in the safety Star Rating of roads.
The pilot study significantly increased the understanding of the challenges of working with ‘off the shelf’ MoMa data and what is required to enable the data to be used for road asset and safety attribute detection during the course of the pilot study which has demonstrated that items and attributes relative to road safety can be satisfactorily detected using mobile LiDAR and 360-degree imagery.
Outcomes of the study show that with further development and automation combined with good quality data, the full range of physical attributes required for iRAP Star Rating could be automatically extracted from remote sensing data including mobile LiDAR, 360-degree imagery and satellite imagery.
Outcomes also indicate, that with further development, the process utilised for this pilot study can be undertaken in a scalable, efficient and cost-effective manner.
With Anditi's method, Main Roads was able to:
-Progress towards their target of assessing 80% of roads most travelled
-Eliminate the WHS risk by using remote sensed data
-Visually inspect and assess their road data via a 3D Web Based Portal
-Host, manage and access their spatial data assets for evaluating maintenance requirements and compliance checks on an ongoing basis.
-Cost Reduction: 50% more cost-effective than traditional methods
-Adopt a new, consistent, scalable AusRap coding approach
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