Zhongzheng Ren Zhang

Unsupervised Anomaly Detection Improves Imitation Learning for Autonomous Racing

Imitation Learning (IL) has shown significant promise in autonomous driving, but its performance heavily depends on the quality of training data. Noisy or corrupted sensor inputs can degrade learned policies, leading to unsafe behavior. This paper presents an unsupervised anomaly detection approach