# WorkIng: A Real-World and Synthetic Highway Work-Zone Benchmark Dataset for Autonomous Navigation

**Authors:** Xujun Xie, Dominik Rößle, Adithya Mohan, Daniel Cremers, Torsten Schön  
**Lab:** Technische Hochschule Ingolstadt · **Published:** 30 Oct 2026  
**Links:** [arXiv](https://arxiv.org/abs/2610.41772) · [PDF](https://arxiv.org/pdf/2610.41772) · [Code](https://github.com/cvims/WorkIng) · [This page](https://omen-bit.github.io/ai-paper-daily/paper/2610.41772.html)

## Abstract

Highway work zones are critical edge cases for autonomous vehicles (AVs), combining atypical geometries and jurisdiction-specific regulations that existing benchmarks fail to capture: real-world datasets lack 360° multi-modal, full-scene 3D work zone annotation, and no single recorded site can span the layouts and regulatory variants needed for generalizable models. We present WorkIng, a dual-domain benchmark and training resource. WorkIng-Highway provides a 360° multi-modal work zone benchmark with synchronized 128-channel LiDAR, six RGB cameras, and RTK-GPS across 32 km, yielding 12,000+ annotated frames and 227,399 tracked 3D bounding boxes. WorkIng-CARLA complements this with procedural simulation spanning 24 regulation-aware scenarios across six canonical topologies and six international traffic standards, populated by 100+ custom 3D assets. Benchmarking representative 3D detection and end-to-end planning architectures shows that real-world data remains the primary performance driver, and that scenario-stratified synthetic augmentation improves architectures across 2D recognition, 3D detection, and trajectory planning. Resources: https://github.com/cvims/WorkIng

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