Support maintenance and management decisions in your road network by automatically identifying and assessing assets such as pavement defects, signs, and lane markings condition.
Vaisala's Road Asset Management combines geospatial videos, driver-made annotations, and computer vision analysis into a highly functional systeml to support road maintenance and management.
Let technology provide a shortcut to assessing and monitoring your road network by automatically identifying assets such as pavement defects, signs, and lane markings condition. The efficiency in data collection enables deployment into the maintenance patrolling fleet.
This means that videos, and therefore road condition and surface data, are constantly being updated, enabling engineering teams to make decisions based on the current condition of the network. Data and videos can be shared between teams for a variety of purposes, saving time spent on unnecessary site visits.
Vaisala's Road Asset Management provides AI-powered solutions to the most difficult challenges road authorities and smart cities are facing related to infrastructure management, efficiency of operations, and improving and maintaining roadway safety.
Patrolling teams can record a variety of on-site issues with the integrated steering wheel button, while video collection happens in the background. This means that unnecessary stops can be avoided, bringing higher efficiency but also increasing safety, with the road inspector not having to step out of the vehicle. Same-day delivery of videos enables teams to respond to public surface condition reports from within the office without visiting a site.
Traditional road pavement condition surveys are mostly performed manually, subjecting the process to human errors and delays, and further draining limited resources. The Road Asset Management technology and methodology quickly and accurately assesses pavement conditions up to four times faster and at half the price of a manual road survey. It distinguishes between all defect types and provides information and analysis of pavement defects and patches across more than 20 different categories, reporting on every 10m segment. Road repair and resurfacing schemes can be planned in reduced time with up-to-date road surface condition and defect data available whenever it is needed. Linking data collection to existing road inspection schedules unlocks a variety of cost-effective pavement preservation strategies. These strategies are not possible with conventional project-based data collection happening every 2–5 years.
The privacy of data captured by Road Asset Management is ensured by autonomous anonymization of people and vehicles within the imagery. Privacy of the individual is a primary concern of Vaisala, and our computer vision technology has developed to comply with regulations such as EU GDPR by being taught to reliably identify and label people or vehicles.
It is crucial that anonymization provides clear images, leave other valuable data objects untouched, and does not interfere with other computer vision detectable objects; such as traffic signs, cracking or potholes, as the processed visual material is used for simultaneous monitoring of multiple infrastructural elements.
Road Asset Management computer vision technology goes beyond pavement defects. With traffic sign detection and management tools and lane marking condition assessment, it provides improved management of kerb-to-kerb assets. In the future we plan to expand the pavement detection capability from asphalt roads into concrete roads, gravel, footways, and cycle ways.
RoadAI is your trusted companion for planned and reactive road maintenance. You can generate a comprehensive assessment of your road network in hours with the power of computer vision, for a fraction of a cost.
Combines a user-friendly Artificial Intelligence (AI) tool with high-quality video data and reliable methodology to improve and streamline the pavement inventory and management processes with objective visual assessments.
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