1 minute read
SmartGarbos – AI-Powered 5G Garbage Trucks for Roadside Asset Management
Category
Excellence in Research and Development Award
Advertisement
Submitting Organisation
Swinburne University of Technology
Collaborating Partners
Brimbank City Council
Optus
Amazon Web Services
Timely detection of roadside assets that require maintenance is crucial for improving citizen satisfaction. Currently, such maintenance issues are typically identified manually by residents or council workers, which is time consuming, expensive, and slow to respond. Swinburne University of Technology and Brimbank City Council, in collaboration with Optus and Amazon Web Services, have developed and deployed this solution funded by the Department of Industry, Science, Energy and Resources of Federal Government under the Australian 5G innovation Initiative Scheme.
First of its kind in Australia, this project delivered a solution that equipped waste collection trucks with Internet of Things (IoT) sensors (high resolution 3D depth sensing cameras, GPS, edge computers) and 5G technology to remotely capture roadside asset data in local government area (LGA). Using AI, the data is analysed in real-time to identify and report issues such as damaged road signs and illegal rubbish around LGAs. The solution has been deployed in 11 waste collection trucks in Brimbank and is operational since July 2022.
The project uncovers the following key features and findings.
• This solution leverages the existing waste collection trucks that operate every day within LGA (e.g., in Brimbank LGA waste collection trucks cover ~1,000 kms). A novel way of capturing transport related data within minimal cost.
• First of its kind real-world deployment integrating IoT, 5G, artificial intelligence and edge and cloud computing.
• Novel AI models that produce on an average 85% accuracy in detecting issues with roadside asset.
This solution will significantly reduce the costs and time associated with maintaining roadside assets while improving citizen satisfaction and safety. Further, this solution offers a new scalable and cost-effective method for collecting data related to transport infrastructure in LGAs.
