Analysis of Data Volumes Generated by Automated Vehicles for Monitoring, Reporting and Remote Driving

Vehicles currently on the road are characterized by a growing level of automation. To improve safety standards and solve disputes on accident responsibilities, policy-makers have established sets of guidelines related to the monitoring of the automated vehicle (AV) behaviour, which requires logging and reporting of a set of data acquisitions, including environment perception and decision-making. Given the rising numbers of AVs, this paper provides a methodology to estimate the amount of data generated by logging and recording operations considering the following factors: AV adoption levels, sensing system architecture, decision-making pipelines, user familiarity with AVs, and point of data extraction along the vehicle sensing pipeline. The method is tested on a European scale, but can be replicated on broader scenarios. The results show the impossibility of uploading raw sensor output, due to the required bit-rate and storage memory, which are not sustainable by current infrastructure and technology. Upload of processed and annotated data in place of raw sensors output would downsize the volumes involved by several orders of magnitude, allowing for real time upload to the cloud. However, the lack of an annotation standard leads to greater uncertainty in the total volume of generated data, making cloud storage design more difficult for authorities. This demonstrates the stringent need for a data annotation standard to efficiently perform monitoring of AVs.