Hugging Face Trending Papers
SlackDrive: Reclaiming Runtime Slack for Adaptive Driving Inference
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arXiv AI
Sep 24
SlackDrive: Reclaiming Runtime Slack for Adaptive Driving Inference
SlackDrive is a pre‑inference compute allocator that dynamically selects the compute budget for each driving control step by reusing the realized latency from previous inferences. By profiling a small set of discrete budgets once, it estimates the current compute state online and chooses the highest‑utility budget that stays within the admissible latency envelope. On the NAVSIM v2 benchmark with DriveDreamer‑Policy, SlackDrive boosts latency‑constrained EPDMS performance by 21.7% compared to the best baseline, while full‑budget and token‑pruning approaches exceed the latency limits under runtime contention.
arXiv AI
Aug 18
Efficient Block-Layer Parallel Inference for Vision-Language-Action on Hybrid Architectures
arXiv:2608. 14586v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models are becoming a promising paradigm for autonomous driving, but their deployment on existing vehicle platforms remains difficult because they introduce both high inference latency and strong GPU-side resource pressure.
Hugging Face Trending Papers
Aug 3
TALSC: Timeliness-Aware Large-Small VLM Collaboration for Infrastructure-Assisted Autonomous Driving
The deployment of Vision-Language Models (VLMs) in autonomous driving (AD) systems is constrained by on-board computing power, restricting vehicles to small VLMs (SVLMs) with limited perception and reasoning capabilities. Infrastructure-assisted AD alleviates this resource constraint by enabling collaboration with large VLMs (LVLMs) at edge servers.
arXiv AI
6d ago
Guiding End-to-End Driving Models with Endpoint-Constrained Trajectory Optimization
The paper introduces Endpoint-Constrained Optimization (ECO), a lightweight postprocessing layer that corrects intermediate waypoints of end-to-end driving policies while preserving the predicted endpoint. ECO does not require maps, privileged simulator state, or additional training, and can be applied to a wide range of waypoint-emitting policies. Experiments on two closed-loop simulators show that ECO significantly improves closed-loop performance, achieving top results in the HUGSIM Closed-Loop Driving Challenge and boosting scene scores on AlpaSim.
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.
arXiv AI
Jul 7
CritiqueDriveVLM: From Verifier-Guided Reinforcement Learning to Latent Thought Distillation for Autonomous Driving
arXiv:2607. 04179v1 Announce Type: cross Abstract: End-to-end Vision-Language Models (VLMs) show immense potential in autonomous driving.