Improving Real-Time Performance of Autonomous Driving Systems with Edge Computing
DOI:
https://doi.org/10.53469/ijomsr.2025.08(05).06Keywords:
Edge computing, Autonomous driving system, Real-time performance optimization, Modular deployment, Container orchestration, Apollo platformAbstract
High-latency bottlenecks in traditional cloud-based architectures pose significant challenges for real-time autonomous driving applications. To address this issue, this study introduces an edge computing-based optimization framework tailored for autonomous driving systems. A hierarchical collaborative computing architecture is designed to distribute perception and decision-making tasks across edge nodes, thereby improving response efficiency. The framework incorporates Docker container technology to enable lightweight modularization of system components, while the Kubernetes orchestration platform ensures dynamic resource scheduling and flexible deployment. Empirical evaluation on the Baidu Apollo autonomous driving open platform demonstrates that the proposed solution reduces end-to-end system latency by 23.6%, with only a marginal decrease in target detection performance (mAP drop ≤ 2.8%). The results also confirm the framework’s scalability and adaptability, offering practical guidance for engineering deployment in autonomous driving scenarios.
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