Edge computing for urban traffic monitoring
January 2024 – December 2025
We propose energy-efficient, high-speed, real-time monitoring of complex traffic scenarios in urban areas. Reliable real-time information about urban traffic supports emergency responders and city planners and reduces carbon emissions, thereby increasing the urban citizens’ quality of life. Using decentralized neuromorphic sensing and processing, we obtain four benefits compared to state-of-the-art: (1) low latency, (2) minimal transmission bandwidth, (3) low power, and (4) enhanced privacy.
As a result of the project, we expect to provide a Live Demonstrator which, in real-time, identifies objects in traffic (e.g., cars, bicycles, and scooters) while continuously running on a negligible energy budget (e.g., a solar cell).
In the upcoming decades, urban areas will become “much larger, more complex, and interconnected, ” requiring optimized infrastructure with increasing automatization at increasingly larger scales. More broadly, real-time information about traffic and infrastructure utilization will become an increasingly valuable commodity for companies and officials to adapt and update the infrastructure as needed. Such data is generated by video cameras in cities today.
Embedding low-power event cameras with low-power neuromorphic local computation provides a uniquely scalable solution for the growing need to monitor urban traffic and resource flows in real time. Our project estimates a 100x reduction in power and a 20x reduction in installation cost.
The teams contribute expertise in neuromorphic hardware and algorithms for system development and expertise in traffic observation and urban traffic management. Only both teams and their complementary expertise can develop real-time low-power computing systems for the societal relevant task of urban traffic monitoring.
Associate Professor of Neurocomputing Systems, KTH EECS CST, PI of project Edge computing for urban traffic monitoring, Digital Futures Faculty+46 8 790 6231