Control is a critical module of autonomous driving systems, which ensures safety and enhances the human-machine interface. Due to the diverse control demands dictated by different driving scenarios, autonomous vehicles require a data-intensive, adaptive, and intelligent controller. To speed up the control process and improve the performance in different scenarios, we introduce a novelty federated learning framework FedMG, which efficiently coordinates diverse vehicles to train a collaboratively models while preserving data privacy to tune the control process. Through detailed analysis of driving scenarios, vehicles are clustered to different groups based on driving scenarios to seek a balance between data quality and communication efficiency. It enables the consolidation of several global models, each optimized for peak performance, thereby enhancing the overall system’s effectiveness. Extensive experiments with different numbers of vehicles and a variety of driving scenarios demonstrate the effectiveness of FedMG. The framework significantly reduces cumulative driving errors, achieving reductions ranging from 5.42% to 76.43%, while improving user comfort, with improvements ranging from 2.23% to 34.61% over baselines.

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.

The rapid development of autonomous driving poses new research challenges for on-vehicle computing system. The execution time of autonomous driving tasks heavily depends on the driving environment. As the scene becomes complex, task execution time increases significantly, leading to end-to-end deadline misses and potential accidents. Hence, a framework that can effectively schedule tasks according to the driving environment in order to guarantee end-to-end deadlines is critical for autonomous driving. In this article, we propose AutoRS, an environment-dependent real-time scheduling framework for end-to-end autonomous driving. AutoRS consists of two nested control loops. The inner control loop schedules tasks based on the driving environment to help them meet end-to-end deadlines while prioritizing the responsiveness and throughput of control commands. The outer control loop tunes task rates based on schedulability to efficiently utilize system resources with an RL-based design. We conduct extensive experiments on both simulation and hardware testbeds using representative autonomous driving applications. The results demonstrate that AutoRS effectively improves the driving performance by$7.95\%-56.9\%$in different driving environments. AutoRS can significantly enhance the safety and reliability of autonomous driving systems by providing timely control commands in complex and dynamic driving environments while guaranteeing task deadlines.