Lane Accurate Detection Of Map Changes Based On Low Cost Smartphone Data

Self-driving vehicles rely on High Definition Street Maps (HD Maps) to ensure the safety and comfort of their driving capabilities. However, the road network infrastructure is subject to constant changes (e.g. through constructions works, accidents, ...). Such changes have to be quickly identified to avoid dangerous driving situations, for example through a reduction of driving speed or the safe handover of driving control back to the human. To address this issue we propose a road hazard detection algorithm that identifies and marks the extent of such changes based on crowdsourced GNSS data. To increase the detection speed of our proposed algorithm, we only rely on sensor information in the collection process, that is not only available through vehicles, but as well by cheap and ubiquitous devices carried on by the passengers such as smartphones. To deal with the limited accuracy of the collected data, we enhance existing algorithmic clustering approaches by leveraging additional meta-data such as the quality of the collected GNSS points and the vehicle's current lane position. Our concept is evaluated with real world measurements in a highway construction site scenario showing improved performance in comparison to the Kernel Density Estimation reference algorithm, used versatile in Related Work.