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HCPerf: Driving Performance-Directed Hierarchical Coordination for Autonomous Vehicles

by Jialiang Ma, Li Li, Zejiang Wang, Jun Wang, Chengzhong Xu
Publication Type
Conference Paper
Book Title
2023 IEEE 43rd International Conference on Distributed Computing Systems (ICDCS)
Publication Date
Page Numbers
487 to 498
Publisher Location
New Jersey, United States of America
Conference Name
2023 43rd IEEE International Conference on Distributed Computing Systems (ICDCS)
Conference Location
Hong Kong, Hong Kong
Conference Sponsor
Conference Date

The rapid development of autonomous driving poses new research challenges to the on-vehicle computing system. In particular, the execution time of autonomous driving tasks highly depends on the specific driving environment. For instance, the execution time of configurable sensor fusion increases significantly as the scene becomes complex, which leads to end-to-end deadline misses from sensing to control and may cause accidents. Thus, a framework that can effectively utilize the system resources to guarantee the end-to-end deadlines of autonomous driving tasks as well as effectively prioritize the responsiveness and throughput of the control commands is crucial for autonomous driving. In this paper, we propose HCPerf, a performance-directed hierarchical coordination framework that intelligently coordinates the autonomous driving tasks with high execution time variation and complex dependencies according to the driving performance in real-time. Specifically, HCPerf mainly consists of two coordinators. The internal coordinator intelligently schedules the tasks according to the driving performance of the vehicle in order to help them meet the end-to-end deadlines while well prioritizing the responsiveness and throughput of the control commands. At the same time, the external coordinator dynamically tunes the rates of tasks according to the schedulability in order to efficiently utilize the system resource. We conduct extensive experiments on both simulation and hardware testbeds with the representative autonomous driving application. The results show that HCPerf can effectively improve the driving performance by 7.69%-45.94% in different driving scenarios.