V2I Work Zone Geometry Reconstruction with Pose-Conditioned UWB Range Denoising

Researchers propose a machine learning system to improve ultra-wideband (UWB) range measurement accuracy for connected autonomous vehicles navigating work zones, using pose-conditioned denoising to filter out signal errors from obstacles and interference. The method reduces measurement error by 66.9% compared to raw data and demonstrates robust performance in real-world field tests, advancing V2I infrastructure capabilities for autonomous vehicle safety.

This research addresses a critical infrastructure gap for autonomous vehicle deployment in dynamic environments. Work zone navigation presents unique challenges beyond standard road scenarios: temporary layouts, frequent equipment repositioning, and dense obstacles create harsh signal conditions where traditional positioning systems fail. UWB technology offers cost-effective roadside deployment, but the technology's vulnerability to multipath errors and non-line-of-sight obstruction has limited practical adoption for safety-critical applications.

The proposed solution combines modern deep learning techniques—temporal prediction, permutation-equivariant architectures, and pose conditioning—to overcome domain-specific challenges that generic sensor fusion approaches cannot address. By leveraging vehicle motion as a geometric prior, the system achieves substantial accuracy improvements that could enable real-time work zone mapping without manual layout inputs. This represents meaningful progress toward autonomous vehicle infrastructure readiness.

For the autonomous vehicle ecosystem, improved V2I positioning directly impacts deployment feasibility in construction zones, roadwork scenarios, and temporary traffic management—high-value use cases where human error contributes to work zone accidents. Infrastructure providers and CAV developers monitoring positioning technology roadmaps should track this advancement, as it reduces capital requirements for work zone safety systems compared to alternative approaches like LiDAR-based mapping.

The work's reliance on real-world field data and controlled benchmarks suggests maturity beyond proof-of-concept, though broader deployment would require standardization across different UWB hardware and anchor configurations. Future development should focus on cross-manufacturer compatibility and integration with existing traffic management systems to enable practical adoption.

• →Pose-conditioned denoising reduces UWB range measurement error by 66.9% relative to raw sensor data in work zone scenarios.
• →The system handles practical deployment challenges including missing anchors, NLOS errors, and antenna reordering through permutation-equivariant architecture design.
• →Real-world field validation demonstrates viability for autonomous vehicle work zone navigation without manual layout inputs.
• →UWB-based V2I infrastructure offers cost-effective alternative to LiDAR mapping for dynamic work zone safety applications.
• →Two-stage training strategy with NLOS-weighted supervision improves robustness in signal-degraded environments typical of construction zones.