Automatic driving perception method and system based on dynamic neural operator and physical evolution

Abstract

The application discloses an automatic driving perception method and system based on dynamic neural operators and physical evolution, and relates to the technical field of automatic driving. Specifically, the method comprises the following steps: extracting a multi-scale initial feature map of an environment image; mapping the initial feature map to a frequency domain to obtain a frequency domain feature, modulating the frequency domain feature by using a physical attenuation mask and a dynamic modulation parameter to simulate the evolution process of the feature in a continuous physical field, and restoring the feature to a spatial domain to obtain a local physical evolution feature; using a linear complexity attention mechanism to capture cross-region dependencies to generate global semantic features; based on the scene complexity difference, performing spatial-level weighted fusion on the local physical evolution feature and the global semantic feature to obtain fused perception features; and outputting a perception result by using the fused perception features. The application aims to introduce physical equation constraints to improve the perception robustness, and to realize global collaboration at a low computational cost, which is suitable for real-time environment perception of a vehicle-mounted edge computing platform.

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