arXiv AI
Jun 8
Planning-aligned Token Compression for Long-Context Autonomous Driving
arXiv:2606. 07464v1 Announce Type: cross Abstract: Monolithic vision-action models represent an emerging paradigm in autonomous driving.
MomADv2: Reliable Temporal Memory for End-to-End Autonomous Driving
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Hugging Face Trending Papers
4d ago
HorizonFlow: Variable-Length Planning for Offline Goal-Conditioned RL
Recent advances in generative planning have made trajectory inpainting a promising approach to offline goal-conditioned reinforcement learning. However, these methods typically specify the planning ho...
arXiv AI
4d ago
HorizonFlow: Variable-Length Planning for Offline Goal-Conditioned RL
HorizonFlow is a hierarchical planner for offline goal-conditioned reinforcement learning that treats the planning horizon as an output rather than a fixed input. It uses a subgoal route planner and an action-prefix controller, both employing insertion-based generation and flow matching, to jointly generate continuous plan content and its length. The method leverages the partially generated plan to guide token insertion and to steer generation toward shorter plans, achieving superior performance on Maze2D, Multi2D, and OGBench benchmarks.
arXiv AI
Sep 15
Planning in the Backbone: DiffAdapterVLA for Native Continuous Trajectory Generation with Driving VLMs
arXiv:2609.15322v1 Announce Type: cross Abstract: Pretrained driving vision-language models (VLMs) integrate visual, route, language, and driving context into rich driving priors, yet their represent...
arXiv AI
Jun 6
PLAN-S: Bridging Planning with Latent Style Dynamics for Autonomous Driving World Models
arXiv:2606. 06014v1 Announce Type: new Abstract: Latent world models (LWMs) have strengthened end-to-end autonomous driving by forecasting compact scene dynamics for downstream planning.
arXiv AI
Jun 30
LWDrive: Layer-Wise World-Model-Guided Vision-Language Model Planning for Autonomous Driving
arXiv:2606. 29879v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) provide powerful semantic understanding and commonsense reasoning for End-to-End Autonomous Driving (E2E-AD) planning.
arXiv AI
Jul 7
AnchorVLA: Bridging Discrete Decisions and Continuous Trajectories for Vision-Language-Action Planning
arXiv:2607. 03182v1 Announce Type: cross Abstract: Autonomous driving planning requires translating navigation intent, traffic rules, dynamic interactions, and language instructions into executable continuous trajectories.
arXiv Computer Vision
4d ago
AlignDrive: Aligned Lateral-Longitudinal Planning for End-to-End Autonomous Driving
arXiv:2601.01762v4 Announce Type: replace-cross Abstract: Practical autonomous driving requires models that generalize by reasoning through spatial-temporal possibilities to exclude unsafe outcomes....
arXiv AI
Jun 11
ConsistencyPlanner: Real-time Planning with Fast-Sampling Consistency Models
arXiv:2606. 11569v1 Announce Type: cross Abstract: Closed-loop planning in complex, real-world driving scenarios presents a critical challenge for autonomous driving systems.
arXiv AI
3d ago
Efficient Multi-Modal Planning with Reward-Guided Preference Optimization for Autonomous Driving
Efficient Multi-Modal Planning with Reward-Guided Preference Optimization for Autonomous Driving proposes EMPlan, a hybrid trajectory planning method that combines sparse anchors with an offset refinement module for low-latency, high-accuracy predictions. The approach uses a two-stage training paradigm—pretraining followed by reward-guided fine-tuning—to improve safety without extra inference cost, leveraging rule-based reward signals and unpaired preference supervision. EMPlan is evaluated on the non-reactive NAVSIM benchmark, achieving a favorable balance between planning accuracy and efficiency under real-time constraints.
arXiv AI
Jun 10
Diffusion Forcing Planner: History-Annealed Planning with Time-Dependent Guidance for Autonomous Driving
arXiv:2606. 11019v1 Announce Type: cross Abstract: Learning-based motion planners, despite recent progress, often suffer from temporal inconsistency.
arXiv AI
Sep 23
AdaReP:Adaptive Re-Planning under Model Mismatch for Neural World-Model Predictive Control
arXiv:2606.23079v2 Announce Type: replace-cross Abstract: Neural world models coupled with model predictive control (MPC) replan at every environment step to bound accumulated prediction error, but t...