HydraSpace: Computational Data Storage for Autonomous Vehicles
Ruijun Wang, Liangkai Liu, Weisong Shi · 2020
To ensure the safety and reliability of an autonomous driving system, multiple sensors have been installed in various positions around the vehicle to eliminate any blind point which could bring potential risks. Although the sensor data is quite useful for localization and perception, the high volume of these data becomes a burden for on-board computing systems. More importantly, the situation will worsen with the demand for increased precision and reduced response time of self-driving applications. Therefore, how to manage this massive amount of sensed data has become a big challenge. The existing vehicle data logging system cannot handle sensor data because both the data type and the amount far exceed its processing capability. In this paper, we propose a computational storage system called HydraSpace with multi-layered storage architecture and practical compression algorithms to manage the sensor pipe data, and we discuss five open questions related to the challenge of storage design for autonomous vehicles. According to the experimental results, the total reduction of storage space is achieved by 88.6% while maintaining the comparable performance of the self-driving applications.