# Improving Autonomous Vehicle Mapping and Navigation in Work Zones Using Crowdsourcing Vehicle Trajectories

arXiv: https://arxiv.org/abs/2301.09194 · HTML: https://mlsanity.com/paper/2301.09194/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 29 references that are in the map.
- First posted on arXiv 22 Jan 2023.

## Builds on (most cited first, 5 of 5)

- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [Scalability in Perception for Autonomous Driving: Waymo Open Dataset](https://mlsanity.com/paper/1912.04838/) (2019)
- [An LSTM Network for Highway Trajectory Prediction](https://mlsanity.com/paper/1801.07962/) (2018)
- [SUPER: A Novel Lane Detection System](https://mlsanity.com/paper/2005.07277/) (2020)
- [Non-parametric Memory for Spatio-Temporal Segmentation of Construction Zones for Self-Driving](https://mlsanity.com/paper/2101.06865/) (2021)

## Built on by (most cited first, 2 of 2)

- [V2I Work Zone Geometry Reconstruction with Pose-Conditioned UWB Range Denoising](https://mlsanity.com/paper/2606.00119/) (2026)
- [Geometry-Aware Infrastructure-Anchored Denoiser for UWB Sensing and Work-Zone Reconstruction](https://mlsanity.com/paper/2607.05449/) (2026)

## Abstract (arXiv)

Prevalent solutions for Connected and Autonomous vehicle (CAV) mapping include high definition map (HD map) or real-time Simultaneous Localization and Mapping (SLAM). Both methods only rely on vehicle itself (onboard sensors or embedded maps) and can not adapt well to temporarily changed drivable areas such as work zones. Navigating CAVs in such areas heavily relies on how the vehicle defines drivable areas based on perception information. Difficulties in improving perception accuracy and ensuring the correct interpretation of perception results are challenging to the vehicle in these situations. This paper presents a prototype that introduces crowdsourcing trajectories information into the mapping process to enhance CAV's understanding on the drivable area and traffic rules. A Gaussian Mixture Model (GMM) is applied to construct the temporarily changed drivable area and occupancy grid map (OGM) based on crowdsourcing trajectories. The proposed method is compared with SLAM without any human driving information. Our method has adapted well with the downstream path planning and vehicle control module, and the CAV did not violate driving rule, which a pure SLAM method did not achieve.