With the swift advancement of artificial intelligence technology, autonomous driving has increasingly emerged as a pivotal technology in the future of transportation. Real-time data exchange and processing across modules in autonomous driving systems necessitate efficient and reliable communication middleware. However, existing communication methods suffer from delay, congestion and packet loss when dealing with high-frequency and large-data-volume transmission tasks, significantly impairing system performance and security. To reduce communication latency and CPU overhead, a multi-mode adaptive high-performance and high-reliability communication middleware CAPilot is proposed. Firstly, a novel shared-memory communication architecture is proposed, comprising a Data Pool, an Event Notification Index Pool, and a Cycle Index Pool. The Data Pool employs a lock-free mechanism to avert deadlock and starvation issues, while addressing frame-skipping using a real-time maintenance and discriminative approach. Event-triggered and period-triggered data acquisition strategies proficiently circumvent data security concerns and performance limitations inherent in conventional shared memory connectivity. Then, to mitigate the overhead associated with dynamic broadcasts within the constrained embedded resources of the network, an adaptive communication scheme is proposed. This scheme incorporates a profile-based static communication encoding that automatically determines the optimal communication method based on the environments of the communicating entities. Finally, the intra-process pointer passing method is optimised by introducing a dual adaptive buffered ring queue, which facilitates bulk data retrieval without using locks. Experimental results show that CAPilot outperforms existing communication middlewares such as ROS2, CyberRT and DDS in terms of communication latency, message throughput, message frame loss rate and resource utilization. These advancements suggest that CAPilot is well-suited for extensive deployment in diverse autonomous driving applications.
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Kuan Wang
Pinzhong Qin
Changquan Xue
ACM Transactions on Internet Technology
Chongqing University
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Wang et al. (Mon,) studied this question.
www.synapsesocial.com/papers/69df2ba0e4eeef8a2a6b095c — DOI: https://doi.org/10.1145/3799695
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