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Updated: Jan 13, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

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HiPro-AD: Sparse Trajectory Transformer for End-to-End Autonomous Driving with Hybrid Spatiotemporal Attention.

Bing Chen1, Gaopeng Wang2,3, Jiandong Yang4

  • 1Shandong Electric Power Engineering Consulting Institute Corp., Ltd., Jinan 250100, China.

Sensors (Basel, Switzerland)
|January 10, 2026
PubMed
Summary

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Orthogonal Trajectories01:26

Orthogonal Trajectories

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Orthogonal trajectories describe the geometric relationship between two families of curves that intersect each other at right angles. One illustrative case involves a family of parabolas that open sideways along the x-axis. These curves share a common shape but differ by a scaling parameter, resulting in a set of curves that all pass through the origin and widen at different rates.Determining Orthogonal TrajectoriesTo identify the orthogonal trajectories for these parabolas, the first step...
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This study introduces HiPro-AD, a novel sparse end-to-end (E2E) autonomous driving framework. It efficiently plans vehicle trajectories using camera data, outperforming dense methods in complex scenarios.

Area of Science:

  • Computer Vision
  • Robotics
  • Artificial Intelligence

Background:

  • Traditional autonomous driving pipelines suffer from error propagation.
  • Dense Bird's-Eye-View (BEV) methods in end-to-end (E2E) driving are computationally intensive.
  • Existing E2E approaches require complex post-processing for trajectory generation.

Purpose of the Study:

  • To propose HiPro-AD, a sparse, proposal-centric E2E autonomous driving framework.
  • To overcome the computational overhead and complexity of dense BEV paradigms.
  • To enhance efficiency and robustness in E2E autonomous driving.

Main Methods:

  • Developed HiPro-AD, integrating an IM-ResNet-34 encoder and a novel STFormer.
  • Employed a proposal-anchored mechanism for dynamic fusion of spatial and temporal features.
Keywords:
autonomous drivingbird’s eye viewtrajectory planning

Related Experiment Videos

Last Updated: Jan 13, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

1.0K
  • Utilized a Pairwise Ranking Scorer for optimal trajectory selection.

    • Main Results:

    • HiPro-AD achieved a 92.6 PDMS on the NAVSIM benchmark using only camera input.
    • Demonstrated real-time capability on Bench2Drive with a 37.31% success rate and 65.48 driving score (67 ms latency).
    • Outperformed prior dense BEV and multimodal methods in autonomous driving benchmarks.

    Conclusions:

    • The sparse, proposal-centric paradigm offers significant efficiency and robustness advantages.
    • HiPro-AD presents a viable alternative to dense BEV approaches for E2E autonomous driving.
    • Validated the framework's effectiveness in complex driving scenarios.