Capraru, R., Wu, J.-Y., Wang, J.-G., Ritchie, M., Lupu, E. C., & Soong, B. H. (2025). Overcoming Catastrophic Forgetting in Radar and Lidar Object Detection in Rain via Layer Freezing and Data Augmentation. 2025 IEEE Radar Conference (RadarConf25), 1–6. https://doi.org/10.1109/radarconf2559087.2025.11204860
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.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the Ministry of Education - Academic Research Fund Tier 1
Grant Reference no. : RG 66/22