Unsupervised Domain Adaptation for Self-Driving from Past Traversal
  Features
Travis Zhang Katie Luo Cheng Perng Phoo Yurong You Wei-Lun Chao Bharath Hariharan Mark Campbell Kilian Q. Weinberger
The rapid development of 3D object detection systems for self-driving cars
has significantly improved accuracy. However, these systems struggle to
generalize across diverse driving environments, which can lead to
safety-critical failures in detecting traffic participants. To address this, we
propose a method that utilizes unlabeled repeated traversals of multiple
locations to adapt object detectors to new driving environments. By
incorporating statistics computed from repeated LiDAR scans, we guide the
adaptation process effectively. Our approach enhances LiDAR-based detection
models using spatial quantized historical features and introduces a lightweight
regression head to leverage the statistics for feature regularization.
Additionally, we leverage the statistics for a novel self-training process to
stabilize the training. The framework is detector model-agnostic and
experiments on real-world datasets demonstrate significant improvements,
achieving up to a 20-point performance gain, especially in detecting
pedestrians and distant objects. Code is available at
https://github.com/zhangtravis/Hist-DA.