A first look at the integration of machine learning models in complex autonomous driving systems: a case study on Apollo
Zi Jun Peng, Jinqiu Yang, Tse-Hsun Chen, Lei Ma
Abstract
Autonomous Driving System (ADS) is one of the most promising and valuable large-scale machine learning (ML) powered systems. Hence, ADS has attracted much attention from academia and practitioners in recent years. Despite extensive study on ML models, it still lacks a comprehensive empirical study towards understanding the ML model roles, peculiar architecture, and complexity of ADS (i.e., various ML models and their relationship with non-trivial code logic). In this paper, we conduct an in-depth case study on Apollo, which is one of the state-of-the-art ADS, widely adopted by major automakers worldwide. We took the first step to reveal the integration of the underlying ML models and code logic in Apollo. In particular, we study the Apollo source code and present the underlying ML model system architecture. We present our findings on how the ML models interact with each other, and how the ML models are integrated with code logic to form a complex system. Finally, we inspect Apollo in a dynamic view and notice the heavy use of model-relevant components and the lack of adequate tests in general. Our study reveals potential maintenance challenges of complex ML-powered systems and identifies future directions to improve the quality assurance of ADS and general ML systems.