Institution profile

Yanshan University

Academic institutionasia · cn
Official website
Research library20linked papers
Opportunities0open roles
Selected work

Representative Papers

Not All History Helps: Velocity-Aware Selective Memory for Long-Horizon End-to-End Autonomous Driving

Aug 16, 2026

This study addresses the challenges of unreliable historical states and motion evolution in long-horizon planning for end-to-end autonomous driving by proposing StableDrive. The method leverages Mamba operators to construct a selective momentum memory that enhances the robustness of historical representations, while introducing a motion-stage training scaffold to guide the model in perceiving dynamic evolution, thereby enabling efficient single-model deployment without ensembling. Experiments demonstrate that StableDrive achieves state-of-the-art performance on benchmarks such as nuScenes, reducing collision rates by 23.3% and attaining the highest EPDMS score on NAVSIM v2. These results indicate significant improvements in both safety and temporal consistency for long-horizon planning, validating the effectiveness of integrating structured memory mechanisms with stage-aware training in complex driving scenarios.

0 citationsRead paper

PhyLatent: Learning Dynamics-Relevant Representations for JEPA World Models

Aug 06, 2026

This work systematically identifies three distinct modes of representation collapse in JEPA-based world models—physical invariance, identifiability, and counterfactual dynamics—even when global latent collapse is avoided. To address these failure modes, the paper introduces PhyLatent, a novel training objective that jointly optimizes dynamics-relevant representations through physical state anchoring, future representation alignment, static visual invariance constraints, counterfactual branch disentanglement, and latent denoising. Moving beyond reliance on global non-collapse assumptions alone, PhyLatent significantly reduces the three collapse rates to 7.53%, 0.95%, and 4.62% on OGBench-Cube, yielding a model-predictive control (MPC) success rate of 78.1%. It further achieves a 98.0% success rate on the TwoRooms task and maintains state-of-the-art performance on Reacher and PushT benchmarks.

0 citationsRead paper
Recent publications

Latest Papers

Not All History Helps: Velocity-Aware Selective Memory for Long-Horizon End-to-End Autonomous Driving

Aug 16, 2026

This study addresses the challenges of unreliable historical states and motion evolution in long-horizon planning for end-to-end autonomous driving by proposing StableDrive. The method leverages Mamba operators to construct a selective momentum memory that enhances the robustness of historical representations, while introducing a motion-stage training scaffold to guide the model in perceiving dynamic evolution, thereby enabling efficient single-model deployment without ensembling. Experiments demonstrate that StableDrive achieves state-of-the-art performance on benchmarks such as nuScenes, reducing collision rates by 23.3% and attaining the highest EPDMS score on NAVSIM v2. These results indicate significant improvements in both safety and temporal consistency for long-horizon planning, validating the effectiveness of integrating structured memory mechanisms with stage-aware training in complex driving scenarios.

0 citationsRead paper

PhyLatent: Learning Dynamics-Relevant Representations for JEPA World Models

Aug 06, 2026

This work systematically identifies three distinct modes of representation collapse in JEPA-based world models—physical invariance, identifiability, and counterfactual dynamics—even when global latent collapse is avoided. To address these failure modes, the paper introduces PhyLatent, a novel training objective that jointly optimizes dynamics-relevant representations through physical state anchoring, future representation alignment, static visual invariance constraints, counterfactual branch disentanglement, and latent denoising. Moving beyond reliance on global non-collapse assumptions alone, PhyLatent significantly reduces the three collapse rates to 7.53%, 0.95%, and 4.62% on OGBench-Cube, yielding a model-predictive control (MPC) success rate of 78.1%. It further achieves a 98.0% success rate on the TwoRooms task and maintains state-of-the-art performance on Reacher and PushT benchmarks.

0 citationsRead paper