Towards Sensor Data Abstraction of Autonomous Vehicle Perception Systems
This work tackles a domain-specific issue for autonomous driving systems, but it is incremental as it focuses on reviewing and identifying paths rather than presenting new methods or results.
The paper addresses the problem of sensor bias in autonomous vehicle perception systems, which hinders transferability to new sensor setups, and proposes sensor data abstraction as a solution by reviewing modalities and identifying paths for abstraction.
Full-stack autonomous driving perception modules usually consist of data-driven models based on multiple sensor modalities. However, these models might be biased to the sensor setup used for data acquisition. This bias can seriously impair the perception models' transferability to new sensor setups, which continuously occur due to the market's competitive nature. We envision sensor data abstraction as an interface between sensor data and machine learning applications for highly automated vehicles (HAD). For this purpose, we review the primary sensor modalities, camera, lidar, and radar, published in autonomous-driving related datasets, examine single sensor abstraction and abstraction of sensor setups, and identify critical paths towards an abstraction of sensor data from multiple perception configurations.