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About this paper
Chong He, Yuechen Luo, Fang Li, Shaoqing Xu, Fuxi Wen
DriveVer introduces a lightweight, test-time verifier for autonomous driving trajectories, enhancing the safety and performance of various end-to-end planners with minimal latency. It achieves this by evaluating trajectory quality and performing geometric refinement during inference.
Key insights
- Test-time scaling can be effectively applied to autonomous driving despite stringent real-time latency requirements, offering a complementary approach to traditional training-time scaling for performance enhancement.
- Direct geometric refinement using a normalized directional vector and cosine similarity loss improves model robustness and generalization by focusing on the overall optimization trend, reducing sensitivity to absolute magnitude errors.
- A dedicated, balanced dataset is crucial for training a verifier to learn fine-grained evaluation boundaries and reliable refinement directions, addressing the scarcity of dense supervision in conventional autonomous driving data.
Summary by alphaXiv.
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