Large Data Transfer Optimization for Improved Robustness in Real-Time V2X-Communication

Abstract

Vehicle-to-everything (V2X) roadmaps envision future applications that require the reliable exchange of large sensor data over a wireless network in real time. Applications include sensor fusion for cooperative perception or remote vehicle control that are subject to stringent real-time and safety constraints. Real-time requirements result from end-to-end latency constraints, while reliability refers to the quest for loss-free sensor data transfer to reach maximum application quality. In wireless networks, both requirements are in conflict, because of the need for error correction. Notably, the established video coding standards are not suitable for this task, as demonstrated in experiments. This article shows that middleware-based backward error correction (BEC) in combination with application controlled selective data transmission is far more effective for this purpose. The mechanisms proposed in this article use application and context knowledge to dynamically adapt the data object volume at high error rates at sustained application resilience. We evaluate popular camera datasets and perception pipelines from the automotive domain and apply two complementary strategies. The results and comparisons show that this approach has great benefits, far beyond the state of the art. It also shows that there is no single strategy that outperforms the other in all use cases.