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Xinwei Gao

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AAMAS Conference 2025 Conference Paper

CRLLK: Constrained Reinforcement Learning for Lane Keeping in Autonomous Driving

  • Xinwei Gao
  • Arambam James Singh
  • Gangadhar Royyuru
  • Michael Yuhas
  • Arvind Easwaran

Lane keeping in autonomous driving systems requires scenariospecific weight tuning for different objectives. We formulate lanekeeping as a constrained reinforcement learning problem, where weight coefficients are automatically learned along with the policy, eliminating the need for scenario-specific tuning. Empirically, our approach outperforms traditional RL in efficiency and reliability. Additionally, real-world demonstrations validate its practical value for real-world autonomous driving.

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ICLR Conference 2023 Conference Paper

Selective Frequency Network for Image Restoration

  • Yuning Cui 0001
  • Yi Tao
  • Zhenshan Bing
  • Wenqi Ren
  • Xinwei Gao
  • Xiaochun Cao
  • Kai Huang 0001
  • Alois C. Knoll

Image restoration aims to reconstruct the latent sharp image from its corrupted counterpart. Besides dealing with this long-standing task in the spatial domain, a few approaches seek solutions in the frequency domain in consideration of the large discrepancy between spectra of sharp/degraded image pairs. However, these works commonly utilize transformation tools, e.g., wavelet transform, to split features into several frequency parts, which is not flexible enough to select the most informative frequency component to recover. In this paper, we exploit a multi-branch and content-aware module to decompose features into separate frequency subbands dynamically and locally, and then accentuate the useful ones via channel-wise attention weights. In addition, to handle large-scale degradation blurs, we propose an extremely simple decoupling and modulation module to enlarge the receptive field via global and window-based average pooling. Integrating two developed modules into a U-Net backbone, the proposed Selective Frequency Network (SFNet) performs favorably against state-of-the-art algorithms on five image restoration tasks, including single-image defocus deblurring, image dehazing, image motion deblurring, image desnowing, and image deraining.

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v2026.09.13