Cross-Domain Adaptation and Anti-Interference Performance of Autonomous Driving Perception Models under Extreme Conditions
Keywords:
Domain adaptation, Adversarial training, Perception system, Cross-domain recognition, Environmental adaptabilityAbstract
Severe weather and lighting changes often cause significant degradation in the recognition performance of autonomous driving perception systems. This study proposes a visual perception model that integrates adversarial training with a domain adaptation mechanism. By combining Generative Adversarial Networks (GANs) and a self-supervised pretraining strategy, the model aims to enhance feature consistency and recognition stability across different environmental domains. At the model structure level, a feature alignment loss and a style transfer module are introduced to improve the model's adaptability in extreme conditions such as rain, nighttime, and strong light. Evaluations are conducted on the Oxford RobotCar and DAWN multi-domain datasets. The results show that the proposed method achieves a 14.2% improvement in average recognition accuracy and maintains a low false detection rate during environmental transitions. These outcomes demonstrate excellent cross-domain adaptability and strong anti-interference performance.
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