Distributed Kalman Filter Fuses Connected Vehicles and Infrastructure for Traffic Estimation
Researchers combine 10% connected vehicles with sparse sensors to outperform traditional traffic monitoring.
Traffic state estimation traditionally relies on dense infrastructure sensor networks, which are expensive to install and maintain. A new paper from researchers at KTH Royal Institute of Technology introduces a distributed framework that treats both roadside infrastructure sensors and connected vehicles (CVs) as cooperative sensing nodes. Using Vehicle-to-Everything (V2X) communication, nearby nodes exchange local estimates and update them via a distributed Kalman filter designed for a second-order macroscopic traffic flow model. A consensus step fuses heterogeneous information across the network, while projection steps enforce physically consistent states like non-negative density and bounded speed.
The method was evaluated on real-world highway data (HighD and NGSIM) and microscopic SUMO simulations capturing transient congestion. Results show accurate reconstruction of traffic states and detection of nonlinear shockwave dynamics even with sparse infrastructure sensing and intermittent vehicular connectivity. A statistical analysis examined how CV penetration rate, V2X communication range, and infrastructure density affect accuracy. The key finding: at just 10% CV penetration and communication ranges of 300–400 meters, combined infrastructure-vehicle sensing consistently outperforms either modality alone, offering a practical path to cost-effective, city-wide traffic monitoring.
- Uses distributed Kalman filter with consensus and projection steps for physically consistent traffic states across V2X-connected nodes.
- Validated on HighD, NGSIM, and SUMO datasets, accurately reconstructing highway states and detecting shockwaves.
- With 10% CV penetration and 300–400m V2X range, combined infrastructure-vehicle outperforms single-modality approaches.
Why It Matters
Enables accurate traffic monitoring with fewer sensors, leveraging existing connected vehicles to reduce infrastructure costs.