NTR adds a self-distillation masked latent reconstruction objective that uses only scene tokens to reconstruct masked patch features, improving visual representation quality and planning performance in end-to-end autonomous driving.
Drive-jepa: Video jepa meets multimodal trajectory distillation for end-to-end driving
6 Pith papers cite this work. Polarity classification is still indexing.
6
Pith papers citing it
citation-role summary
background 1
baseline 1
citation-polarity summary
years
2026 6verdicts
UNVERDICTED 6representative citing papers
DAWN couples a world predictor with a world-conditioned action denoiser in latent space so that each refines the other recursively, yielding strong planning and safety results on autonomous driving benchmarks.
The paper introduces a unified framework for world models that fully incorporates all cognitive functions from Cognitive Architecture Theory, highlights under-researched areas in motivation and meta-cognition, and proposes Epistemic World Models as a new category for scientific discovery agents.
A self-supervised JEPA model on nuPlan data uses temporal prediction error to score driving scenario complexity without labels, assigning higher scores to turns and pedestrian interactions and achieving AP 0.512 in anomaly detection.
Discrete-WAM unifies world modeling and policy learning for autonomous driving by representing observations, states, decisions, and actions as tokens in one space and using hierarchical token editing for planning.
CLEAR achieves state-of-the-art PDMS of 93.7 on NAVSIM v1 by combining single-step VAE latent drift with Qwen 3.5-guided adaptive scheduling and trajectory scoring for end-to-end driving.
citing papers explorer
-
NTR: Neural Token Reconstruction for Scene Token Bottleneck in End-to-End Driving
NTR adds a self-distillation masked latent reconstruction objective that uses only scene tokens to reconstruct masked patch features, improving visual representation quality and planning performance in end-to-end autonomous driving.
-
The DAWN of World-Action Interactive Models
DAWN couples a world predictor with a world-conditioned action denoiser in latent space so that each refines the other recursively, yielding strong planning and safety results on autonomous driving benchmarks.
-
Human Cognition in Machines: A Unified Perspective of World Models
The paper introduces a unified framework for world models that fully incorporates all cognitive functions from Cognitive Architecture Theory, highlights under-researched areas in motivation and meta-cognition, and proposes Epistemic World Models as a new category for scientific discovery agents.
-
Zero-Label Driving Scenario Complexity Detection via Joint Embedding Predictive Architecture
A self-supervised JEPA model on nuPlan data uses temporal prediction error to score driving scenario complexity without labels, assigning higher scores to turns and pedestrian interactions and achieving AP 0.512 in anomaly detection.
-
Discrete-WAM: Unified Discrete Vision-Action Token Editing for World-Policy Learning
Discrete-WAM unifies world modeling and policy learning for autonomous driving by representing observations, states, decisions, and actions as tokens in one space and using hierarchical token editing for planning.
-
CLEAR: Cognition and Latent Evaluation for Adaptive Routing in End-to-End Autonomous Driving
CLEAR achieves state-of-the-art PDMS of 93.7 on NAVSIM v1 by combining single-step VAE latent drift with Qwen 3.5-guided adaptive scheduling and trajectory scoring for end-to-end driving.