Lidar‐Based Object Tracking of Traffic Participants with Sensor Nodes in Existing Urban Infrastructure

This paper presents a lidar‐only state estimation and tracking framework, along with a roadside sensing unit for integration with existing urban infrastructure. Urban deployments demand scalable, real‐time tracking solutions, yet traditional remote sensing remains costly and computationally intensive, especially under perceptually degraded conditions. Our sensor node couples a single lidar with an edge computing unit and runs a computationally efficient, GPU‐free observer that simultaneously estimates object state, class, dimensions, and existence probability. The pipeline performs (i) state updates via an extended Kalman filter, (ii) dimension estimation using a 1D grid map/Bayesian update, (iii) class updates via a lookup table driven by the most probable object footprint, and (iv) existence estimation from track age and bounding box consistency. Experiments in dynamic urban‐like scenes with an instrumented passenger vehicle and additional road users demonstrate real‐time performance. Quantitative tracking accuracy is reported for the vehicle, while detection and classification performance are evaluated across all 48 annotated tracks. The complete end‐to‐end pipeline finishes within 100 ms for 99.88% of messages, with an excellent detection rate. Robustness is further confirmed under simulated wind‐induced sensor vibration. These results indicate that reliable, real‐time roadside tracking is feasible on CPU‐only edge hardware, enabling scalable, privacy‐friendly deployments within existing city infrastructure. The framework integrates with existing poles, traffic lights, and buildings, reducing deployment costs and simplifying large‐scale urban rollouts and maintenance efforts.