Vehicle-to-Infrastructure Work Zone Geometry Reconstruction With Pose-Conditioned UWB Range Denoising

Details

• Creators:
  Liu, Jiaxi ; Li, Hangyu ; Cheng, Yang ; Gan, Rui ; You, Junwei ; Tang, Weizhe ; Zhang, Peng ; Parker, Steven T. ; Li, Xiaopeng ; Ran, Bin
• Corporate Creators:
  University of Wisconsin--Madison. Department of Civil and Environmental Engineering
• Subject/TRT Terms:
  • Work zones
  • Reconstruction
  • Simulation
  • Benchmarks
  • Mapping
  • Geometry
  • Statistical analysis
  • Mathematical prediction
  • Vehicle to infrastructure communications
  • work zone geometry reconstruction
  More +
• Resource Type:
  Research Paper
• Geographical Coverage:
  United States
• Corporate Publisher:
  University of Wisconsin--Madison. Dept. of Civil and Environmental Engineering
• Abstract:
  Reliable work zone mapping is important for connected and autonomous vehicles (CAVs) to navigate safely and smoothly through work zone areas. Cone-mounted ultra-wideband (UWB)roadside units (RSU) offer a cost-effective way for work zone layout inference, as roadside anchors and vehicle tags provide direct vehicle-to-infrastructure (V2I) range constraints for work zone geometry reconstruction. However, UWB range estimation is degraded by bursty outliers, non-line-of-sight (NLOS) errors, arbitrary anchor-ordering issues, and vehicle pose uncertainties in practical field deployments. To address these challenges, this study proposes a pose-conditioned, permutation-equivariant predictive denoiser for multi-anchor UWB ranging. The model employs shared anchor-wise temporal prediction to capture range dynamics, symmetric set aggregation to handle unordered and missing anchors, and pose-conditioned residual decoding to incorporate vehicle motion as a geometric prior. A two-stage training strategy first learns prediction from observed ranges, and then fine-tunes the denoiser with NLOS-weighted supervision. The method is evaluated on rare real-world V2I UWB field data collected with a CAV, as well as on controlled large-scale simulation benchmarks for ablative insights. Results show that the proposed method substantially improves range accuracy, cone localization, and work zone geometry reconstruction in challenging NLOS-dominated regimes, remains robust to anchor re-indexing and moderate anchor dropout, and reduces measurement-weighted field MSE by 66.9% relative to the raw input.
• Content Notes:
  Preprint submitted to Elsevier.
• Format:
  PDF
• Funding:
  69A3552348305
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/93285/dot_93285_DS1.pdf
• File Type:
  [PDF - 4.34 MB]
• Collection(s):
  US Transportation Collection
• Main Document Checksum:
  urn:sha-512:b5441fedaa4d827b0c5ea89400027f6ad84c6714d1935c4e75f2178628922cc9d546b44f66cbf912286edf650db81dd237ccb8561e4546e28c483d8dcb4c3464