Capraru, R; Wu, JY; Wang, JG; Ritchie, M; Lupu, EC; Soong, BH; (2025) Overcoming Catastrophic Forgetting in Radar and Lidar Object Detection in Rain via Layer Freezing and Data Augmentation. In: Proceedings of the IEEE Radar Conference. (pp. pp. 1641-1646). IEEE

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Abstract

Advanced Driver-Assistance Systems (ADAS) use sensors like radar, LiDAR, and cameras for reliable vehicle perception in different weather conditions. While LiDAR and cameras offer high-resolution perception in clear weather, radar excels in adverse conditions such as low light, fog, or rain. Adapting systems trained on clear-weather data to cope with adverse weather often causes catastrophic forgetting, significantly reducing their initial performance after re-training. Unsupervised domain adaptation (UDA) techniques aim to address this but are complex. In this paper, we examine catastrophic forgetting effects on radar and LiDAR, proposing methods to reduce it: model freezing, pre-training with mixed data, and adding simulated data. Our experiments on the wellestablished RADIATE dataset show these methods improve clear-weather retention and rain detection, with radar showing a 6.59 % reduction in forgetting and a 17.19 % rain detection gain, and LiDAR a 13.62 % reduction in forgetting and 24 % improvement with simulations.

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