Jev-AD combines a typed decision model, Jev, with a language world model to predict how nearby vehicles will move over the next two seconds for each candidate action. By comparing these predictions to the decision model’s outputs, Jev-AD reduces collision rates dramatically in both simulated highway merging (from 26.0% to 2.4%) and real‑world NGSIM episodes (from 48.2% to 2.8%). The system also achieves passenger intent in 93% of free‑form command trials and foresees 93% of dangerous actions in real traffic.
arXiv:2609.38028v1 Announce Type: cross Abstract: Autonomous vehicles interacting with passengers through natural language must reason beyond immediate commands. Passenger intent may span multiple st...
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:2605.21139v4 Announce Type: replace-cross Abstract: Current end-to-end autonomous driving models are fundamentally constrained by the behavioral cloning ceiling of imitation learning. While rei...
arXiv:2512. 05277v3 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) are increasingly deployed as the perception and reasoning backbone of autonomous agents acting in the wild, with autonomous driving (AD) being one of the most safety-critical instances.
arXiv:2512. 05277v4 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) are increasingly deployed as the perception and reasoning backbone of autonomous agents acting in the wild, with autonomous driving (AD) being one of the most safety-critical instances.
arXiv:2610.07954v1 Announce Type: new Abstract: Language-based trajectory predictors represent coordinates as discrete tokens and learn auxiliary tasks such as destination and group reasoning. This f...
arXiv:2610.02765v1 Announce Type: cross Abstract: Monitoring planned driving trajectories requires accurately estimating the collision likelihood with actors whose motion is itself impacted by the eg...
DriveMind is a multimodal large language model (MLLM) framework for autonomous driving that maintains multiple hypotheses about a traffic scene and updates their weights as new frames arrive, inspired by particle filtering. By propagating each hypothesis through a vision-language model, scoring against current observations, and resampling to preserve diversity, DriveMind improves driving performance on the Bench2Drive benchmark, raising the Driving Score from 68.50 to 81.37 and the Success Rate from 41.36% to 54.09%. The approach yields the largest gains in ambiguous scenarios such as unsignalized intersections and oncoming-traffic interactions, and also enhances fine‑grained interactive driving tasks like merging, emergency braking, and give‑way.
arXiv:2611.06007v1 Announce Type: new Abstract: Motorized Two-Wheelers (MTWs) dominate personal transport across much of the Global South, yet remain largely overlooked in predictive perception resea...
Intent misinterpretation during vehicle interactions causes recurring planning failures. We study a decision layer in which a language-guided intent module reads structured descriptors, computes a smo...
arXiv:2610.31835v1 Announce Type: new Abstract: Temporary traffic control redirects vehicles through work zones, requiring automated vehicles to connect an interpretation of the visible devices to a...
arXiv:2605.10426v3 Announce Type: replace-cross Abstract: Vision-Language-Action (VLA) models have emerged as a promising paradigm for end-to-end autonomous driving. However, existing reasoning mecha...