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:2607. 22714v1 Announce Type: cross Abstract: Real-time perception is a foundational requirement for advanced driver assistance systems (ADAS) and autonomous vehicles, yet embedded automotive platforms impose severe constraints on compute, memory, and power.
arXiv:2606. 02979v1 Announce Type: cross Abstract: We present a novel compact deep multi-task learning model to handle various autonomous driving perception tasks in one forward pass.
arXiv:2608.28672v1 Announce Type: cross Abstract: Autonomous vehicles operate in dynamic, ever-changing environments where new scenarios and edge cases constantly emerge. As a result, static learning...
arXiv:2602. 21172v3 Announce Type: replace Abstract: Vision-Language-Action (VLA) models are advancing autonomous driving by replacing modular pipelines with unified end-to-end architectures.
LightEMMA is a longitudinal evaluation framework that tests the autonomous driving performance of vision‑language models (VLMs) without fine‑tuning or prompt engineering. Using this protocol, the authors evaluated 15 VLMs from five major families on the nuScenes prediction benchmark and found that larger, more capable models do not consistently outperform earlier generations. The study identifies common failure modes such as overreliance on historical actions and difficulty reconciling conflicting visual cues, underscoring the need for domain‑specific adaptation to enhance VLM safety in autonomous driving.
The paper surveys one‑stage object detectors for autonomous driving, covering the evolution from early models like YOLOv1 and SSD to recent real‑time architectures such as YOLOv10 and anchor‑free detectors like FCOS and CenterNet. It compares these methods on design choices, feature‑fusion strategies, loss functions, deployment trade‑offs, and benchmark performance, while also summarizing datasets, evaluation metrics, open challenges, and future research directions. The survey emphasizes how one‑stage detectors balance speed, accuracy, efficiency, and robustness, noting the gap between benchmark results and dependable real‑world performance.
arXiv:2606. 14010v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have shown strong potential for end-to-end autonomous driving by jointly modeling visual perception, language reasoning, explainability and action prediction.
arXiv:2609.18511v1 Announce Type: cross Abstract: In autonomous driving, perception models often struggle to generalize to new environments due to domain shifts. While unsupervised model adaptation o...
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.
arXiv:2609.09881v1 Announce Type: new Abstract: Semantic segmentation for autonomous driving requires reliable detection of vulnerable road users (VRUs) despite heavy class imbalance. We introduce CL...
arXiv:2607. 02612v1 Announce Type: cross Abstract: Vision Transformers achieve strong image classification accuracy but process all image regions with nearly the same computation, even when many regions are redundant or uninformative.
The paper studies how Bird's‑Eye‑View (BEV) maps predicted by Cross‑View Transformers (CVT) can be used directly as inputs to a Behavior‑Cloning (BC) driving policy in the CARLA simulator. It introduces a six‑channel BEV representation and a Kernel Density Estimation (KDE) weighting scheme to focus learning on underrepresented maneuvers. Closed‑loop tests show that the KDE‑weighted model is the only predicted‑BEV agent to finish an episode without infractions, highlighting that global segmentation scores are poor proxies for driving performance and that prediction quality at critical geometries, especially the route channel, is key to reliable navigation.