Fleet size affects accuracy of crowdsourced digital traffic maps
Assessing the Impact of Fleet Size on Crowdsourced Mapping Using a Dissimilarity Measure
Robotics
Summary
Keeping digital maps up to date is important for driver safety systems, but traditional map surveys are expensive. The authors studied how using different numbers of cars to gather map data affects map accuracy. They created a way to measure map errors that considers how well traffic signs are detected and located. By simulating data from 5 to 50 vehicles, they showed that more cars generally improve map quality, and their error measure helps evaluate this improvement.
What this means in practice
- •For automotive mapping teams: Determine the number of vehicles needed to maintain accurate crowdsourced traffic sign maps in ADAS systems.
- •For smart city planners: Use the proposed dissimilarity measure to assess and improve the quality of traffic sign data collected from vehicle fleets.
Authors
Marie-Ngoïe Badibanga Kalenda, Philippe Bonnifait, Marie-Anne Mittet
Abstract
Accurate digital maps are essential for Advanced Driver Assistance Systems (ADAS) or Autonomous Driving (AD), providing critical information such as road geometry, traffic signs and speed limits required by safety functions including Intelligent Speed Assistance (ISA). Maintaining these map layers using traditional surveying methods is costly and difficult to scale. Crowdsourced approaches based on fleets provide a promising alternative for continuously validating and updating map information. However, the relationship between the number of contributing vehicles and the quality of the resulting map remains poorly understood. To address this gap, this paper presents a simulation-based framework for evaluating crowdsourced traffic sign maintenance using a dissimilarity measure called GOSPAM (Generalized Optimal SubPattern Assignment for Maps), which combines localization errors with detection performance by accounting for False Positives (FP) and False Negatives (FN). The proposed system models multivehicle observations with representative sensor noise, detection errors, and semantic recognition uncertainties. Observations from multiple vehicles are aggregated using spatial clustering and semantic filtering to estimate traffic sign locations. Using simulated trajectories generated from data carried out by an experimental vehicle in an area containing ground-truth traffic signs, we assess the influence of fleet size on the performance of crowdsourced mapping. The number of vehicles ranges from 5 to 50, and performance is analyzed using standard evaluation metrics which are compared to the GOSPAM . The results show that GOSPAM can be used to effectively assess the quality of crowdsourced mapping, such as the contributions made by the first vehicles or the improvements made by numerous vehicles.