MISTY delivers state-of-the-art closed-loop scores on nuPlan Test14-hard (80.32 non-reactive, 82.21 reactive) at 10.1 ms latency via single-step MLP-Mixer inference and a latent drifting loss that encourages proactive maneuvers.
Vad: Vectorized scene representation for efficient autonomous driving,
8 Pith papers cite this work. Polarity classification is still indexing.
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The SNG framework and SNG-VLA model enable end-to-end driving systems to better incorporate global navigation for state-of-the-art route following without auxiliary perception losses.
A platform using flow matching for real-world image generation and an adversarial policy creates challenging corner cases to evaluate end-to-end autonomous driving models like UniAD and VAD, showing performance degradation.
VERDI aligns perception, prediction, and planning outputs of end-to-end AD models with VLM-generated text features at training time to embed structured reasoning, yielding up to 11% better l2 distance and 10% higher non-collision rate in closed-loop tests.
HEAT uses a trajectory-driven learning paradigm and a world model predicting future latent features from ego actions to enable a single unified end-to-end autonomous driving model to perform well across heterogeneous domains on nuScenes, NAVSIM, and Waymo benchmarks.
PokeVLA is a lightweight VLA model pre-trained on 2.4M samples for spatial grounding and reasoning, then adapted via multi-view semantics and geometry alignment to achieve state-of-the-art robot manipulation performance.
CooperDrive augments autonomous vehicle perception by sharing object-level data from BEV features, enabling earlier conflict anticipation and safer planning with 90 kbps bandwidth and 89 ms latency in real-world NLOS tests.
The paper introduces a safety framework for datasets in autonomous driving that uses the AI Data Flywheel and lifecycle processes to identify hazards and ensure compliance with ISO/PAS 8800.
citing papers explorer
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MISTY: High-Throughput Motion Planning via Mixer-based Single-step Drifting
MISTY delivers state-of-the-art closed-loop scores on nuPlan Test14-hard (80.32 non-reactive, 82.21 reactive) at 10.1 ms latency via single-step MLP-Mixer inference and a latent drifting loss that encourages proactive maneuvers.
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Unveiling the Surprising Efficacy of Navigation Understanding in End-to-End Autonomous Driving
The SNG framework and SNG-VLA model enable end-to-end driving systems to better incorporate global navigation for state-of-the-art route following without auxiliary perception losses.
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Driving in Corner Case: A Real-World Adversarial Closed-Loop Evaluation Platform for End-to-End Autonomous Driving
A platform using flow matching for real-world image generation and an adversarial policy creates challenging corner cases to evaluate end-to-end autonomous driving models like UniAD and VAD, showing performance degradation.
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VERDI: VLM-Embedded Reasoning for Autonomous Driving
VERDI aligns perception, prediction, and planning outputs of end-to-end AD models with VLM-generated text features at training time to embed structured reasoning, yielding up to 11% better l2 distance and 10% higher non-collision rate in closed-loop tests.
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HEAT: Heterogeneous End-to-End Autonomous Driving via Trajectory-Guided World Models
HEAT uses a trajectory-driven learning paradigm and a world model predicting future latent features from ego actions to enable a single unified end-to-end autonomous driving model to perform well across heterogeneous domains on nuScenes, NAVSIM, and Waymo benchmarks.
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PokeVLA: Empowering Pocket-Sized Vision-Language-Action Model with Comprehensive World Knowledge Guidance
PokeVLA is a lightweight VLA model pre-trained on 2.4M samples for spatial grounding and reasoning, then adapted via multi-view semantics and geometry alignment to achieve state-of-the-art robot manipulation performance.
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CooperDrive: Enhancing Driving Decisions Through Cooperative Perception
CooperDrive augments autonomous vehicle perception by sharing object-level data from BEV features, enabling earlier conflict anticipation and safer planning with 90 kbps bandwidth and 89 ms latency in real-world NLOS tests.
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Dataset Safety in Autonomous Driving: Requirements, Risks, and Assurance
The paper introduces a safety framework for datasets in autonomous driving that uses the AI Data Flywheel and lifecycle processes to identify hazards and ensure compliance with ISO/PAS 8800.