arXiv Machine Learning
FrameScope: Temporal Data Valuation for Stream Active Learning in Autonomous Vehicle Systems
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
Related stories
arXiv AI
Jun 19
Class-Incremental Motion Forecasting
arXiv:2603. 09420v3 Announce Type: replace-cross Abstract: Motion forecasting enables autonomous vehicles to anticipate scene evolution by predicting the future trajectories of dynamic agents.
arXiv Machine Learning
Sep 1
AdaptAV: Continuous Adaption of Vision Models for Autonomous Vehicles Using Cloud-based Oracle
The paper introduces AdaptAV, a system that continuously adapts vision models for autonomous vehicles by retraining them on the cloud using data uploaded from the vehicles. It leverages powerful cloud compute resources and a highly accurate oracle model to guide the retraining process, producing a new model that is then transmitted back to the vehicle. This approach aims to improve inference accuracy over time while maintaining the fast inference speeds required for on‑vehicle deployment.
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.
Hugging Face Trending Papers
Sep 3
SV-WAM: An Efficient Surround-View World-Action Model for End-to-End Autonomous Driving
SV-WAM is a surround‑view world‑action model that keeps all six camera views for autonomous driving while enabling efficient inference by discarding the video branch during deployment. It uses future‑video prediction as dense training supervision and introduces an action‑centered causal mask to prevent future‑video tokens from influencing action tokens during joint denoising. A differentiable drivable‑area compliance regularizer further improves safety by penalizing vehicle‑footprint corners that approach or cross drivable boundaries. Experiments on NAVSIMv2 and nuScenes show state‑of‑the‑art planning performance with low latency and strong zero‑shot transfer.
arXiv AI
Jun 10
TaCarla: A comprehensive benchmarking dataset for end-to-end autonomous driving
arXiv:2602. 23499v4 Announce Type: replace-cross Abstract: Collecting a high-quality dataset is a critical task that demands meticulous attention to detail, as overlooking certain aspects can render the entire dataset unusable.
Hugging Face Trending Papers
Jun 2
NVIDIA OmniDreams: Real-Time Generative World Model for Closed-Loop Autonomous Vehicle Simulation
As autonomous vehicle capabilities advance, the safe evaluation of driving policies in long-tail scenarios remains a critical bottleneck. In closed-loop simulation, the driving policy model actively interacts with the environment, where its actions dynamically update the simulator state and directly influence the next set of generated sensor observations.