arXiv:2609.21470v1 Announce Type: new Abstract: Sparse representation formulates the environment perception for the end-to-end driving system as a set of discrete elements like objects and lane lines...
The paper introduces iDriveVLA, a multi‑modal planning framework for autonomous driving that addresses a generation‑evaluation asymmetry by improving candidate trajectory spaces and providing a unified, safety‑aware evaluator. It combines a Safety‑aware Scorer for risk estimation with a VLM‑guided Modulator that adapts weighting to the scene, and employs an oracle‑aligned progressive training strategy. On the NAVSIM v1 leaderboard, iDriveVLA achieves a new state‑of‑the‑art PDMS score of 94.95, surpassing human‑expert performance.
RiskWorld is a risk‑aware world modeling framework that forecasts shared occupancy and selectively replaces planned trajectories in automated driving. It fuses spatial risk fields, temporal actor context, and visual bird’s‑eye‑view features, using flow‑guided evolution to transport occupancy and signed residuals to correct it. In open‑loop planning on nuScenes, RiskWorld achieves the lowest collision rate over a 3‑second horizon and the second‑best average L2 error, running at 11.5 FPS on a single NVIDIA RTX 4090.
arXiv:2606. 00857v1 Announce Type: cross Abstract: Accurate and reliable vehicle trajectory prediction is essential for safe autonomous driving.
arXiv:2607. 00283v1 Announce Type: cross Abstract: Autonomous vehicles must safely navigate complex environments where planning-critical agents may be hidden from view.
arXiv:2606. 24759v1 Announce Type: cross Abstract: Recent multimodal large language models (MLLMs) have shown strong potential for autonomous driving scene understanding, yet existing methods still face a fundamental trade-off between temporal reasoning and spatial precision.
CAR‑VLA is a Vision‑Language‑Action model for autonomous driving that jointly considers scene complexity and dynamic risk to determine reasoning depth, urgency, and focus. It maps four complexity‑risk categories to three reasoning modes—Fast Intuition, Slow Thinking, and Reflex Response—each tailored to different driving scenarios. The model is trained via progressive supervised learning and reinforcement learning, achieving competitive performance on NAVSIM and Navhard benchmarks and demonstrating risk‑aware reasoning in high‑risk scenarios.
arXiv:2608. 04776v1 Announce Type: new Abstract: The ability to accurately assess and anticipate risks in safety-critical scenarios is crucial for autonomous driving systems.
arXiv:2608.30657v1 Announce Type: new Abstract: Fixed-viewpoint infrastructure sensors repeatedly observe the same traffic space, making roadside 3D occupancy structurally different from ego-vehicle...
arXiv:2609.10377v1 Announce Type: cross Abstract: Safety is a fundamental requirement for autonomous driving, yet existing end-to-end driving models still lack explicit risk-aware learning capacities...
HERMES is a holistic end‑to‑end multimodal driving framework that incorporates long‑tail semantic knowledge into trajectory planning for autonomous vehicles. It uses a foundation‑model‑assisted annotation pipeline to build Long‑Tail Scene Context and Long‑Tail Planning Context, capturing hazard‑centric scene information, maneuver intent, and risk‑aware guidance. A Tri‑Modal Driving Module then fuses multi‑view visual observations, historical ego‑motion, and long‑tail semantic instructions to generate intent‑ and risk‑aware trajectories, achieving consistent performance gains on a large‑scale real‑world long‑tail driving benchmark.
arXiv:2609.37871v1 Announce Type: cross Abstract: Average performance on routine driving benchmarks does not establish planner reliability under rare, safety-critical hazards. We proposed ExceptionDr...