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arXiv Computer Vision By Zhongyu Xia, Zhiwei Lin, Yongtao Wang, Ming-Hsuan Yang

HENet++: Hybrid Encoding and Multi-task Learning for 3D Perception and End-to-end Autonomous Driving

Read the original on arXiv Computer Vision →

HENet++ is a unified framework that combines a hybrid image encoding strategy—using a large encoder for short‑term frames and a lightweight encoder for long‑term temporal context—to balance accuracy and efficiency in 3D perception for autonomous driving. It jointly extracts dense background and sparse foreground features, enabling task‑specific representations that reduce cumulative errors and enrich downstream prediction and planning modules. The approach supports diverse 3D feature extraction pipelines, multi‑modal inputs (camera and radar), and achieves state‑of‑the‑art results on nuScenes and NAVSIM benchmarks, including the lowest collision rate on nuScenes planning.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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