Lidar-based Tracking of Traffic Participants with Sensor Nodes in Existing Urban Infrastructure

2025-09-24

AI Summary

To address the high cost, computational overhead, and poor robustness of conventional solutions for urban roadside perception, this paper proposes a CPU-only real-time traffic participant tracking framework. The framework leverages a single-line LiDAR sensor and an edge computing unit, integrating a lightweight extended Kalman filter, 1D grid-map-based Bayesian state update, footprint lookup table-driven fine-grained classification, and a trajectory-age- and bounding-box-consistency-based existence criterion to jointly estimate position, velocity, class, and existence. Evaluated in urban-like dynamic scenarios, the end-to-end system achieves 99.88% message processing latency ≤100 ms, high detection accuracy, and stable performance under simulated wind-induced vibration. This work presents the first empirical validation of a GPU-free, CPU-only architecture for large-scale roadside perception—demonstrating feasibility for real-time, robust, and low-cost deployment.

Technology Category

• Computer Vision: Motion & Tracking
• Intelligent Robots: State Estimation
• Planning, Routing, and Scheduling: Activity and Plan Recognition

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Security and Privacy: Large-scale security measurements
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

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 footprint, and (iv) existence estimation from track age and bounding-box consistency. Experiments in dynamic urban-like scenes with diverse traffic participants demonstrate real-time performance and high precision: The complete end-to-end pipeline finishes within SI{100}{millisecond} for SI{99.88}{%} of messages, with an excellent detection rate. Robustness is further confirmed under simulated wind and 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.

Problem

• Developing cost-effective lidar tracking for urban infrastructure integration
• Creating real-time traffic monitoring without expensive GPU computation
• Enabling scalable roadside tracking under challenging perceptual conditions

Innovation

• Lidar-only tracking framework with edge computing units
• Extended Kalman filter for real-time state estimation
• CPU-only Bayesian methods for dimension and class updates