Over 60,000 km in a year: remotely collecting large-volume high-quality data from a logistics truck

Artikel i vetenskaplig tidskrift, 2022

After the first successful large-scale demonstration of eleven self-driving vehicles at the DARPA Urban Challenge in 2007, research results from the competing teams found their way into advanced driver systems (ADAS) that support typical driving tasks like adaptive cruise control and semi-automated parking. However, as of today, SAE Level 4 vehicles are not commercially available yet, which would allow the driver to be inattentive for longer periods. Hence, SAE Level 3, which represents partial automation yet continuously monitored by a human operator, may provide a step towards a viable SAE Level 4 product especially for commercial freight logistics. However, large amounts of data from such freight operations is needed to study the unique challenges in such use cases. In this paper, we present the system and software architecture of an end-to-end data logging solution, which is capable of recording large volumes of high-quality data. The system is installed in a commercial truck that is in daily operation by a logistics company and hence, the recorded data is only accessible remotely (i.e., over-the-air). We report about the fail-safe system design, initial findings from over one year of operation, as well as our lessons learned. During its first year of operation, the truck was used for 210 days by the logistics company, out of which 193 days were logged resulting in more than 4.5 TB of data from five cameras, two GNSS–IMU sensors, and six on-board vehicle controller area networks (CAN) busses. We demonstrate the value of the proposed end-to-end approach for traffic and driver behavior research by analyzing the uploaded data in the cloud to spot critical events such as unexpected harsh braking maneuvers caused by lane merging operations.