Driving Scenario Perception-Aware Computing System Design in Autonomous Vehicles

Recently, autonomous driving ignited competitions among car makers and technical corporations. Low-level autonomous vehicles are already commercially available. However, high autonomous vehicles where the vehicle drives by itself without human monitoring is still at infancy. Such autonomous vehicles (AVs) fully rely on the computing system in the car to perceive the environment and make driving decisions. In AV computing systems, the latency is an essential metric for ensuring the efficiency and safety, because a timely decision with low latency will avoid accidents and save lives. Moreover, we perform a field study by running industrial Level-4 autonomous driving fleets in various locations, road conditions, and traffic patterns. We observe that the perception module consumes the longest latency, and it is highly sensitive to surrounding obstacles. To study the correlation between perception latency and surrounding obstacles, we propose a perception latency model. Moreover, we demonstrate the use of our latency model, by developing and evaluating a driving scenario perception-aware AV computing system that efficiently manages computation hardware resource. Our evaluation results show that the proposed AV system resource management improves performance significantly.