arXiv:2606. 21165v2 Announce Type: replace-cross Abstract: We present OmniV2X, a generative foundation model for vehicle-to-everything (V2X) cooperative driving.
arXiv:2609.37098v1 Announce Type: cross Abstract: Vehicle-infrastructure cooperation can complement onboard sensing with broader and more informative observations of the traffic environment, providin...
arXiv:2606. 06219v1 Announce Type: cross Abstract: End-to-end autonomous driving models often struggle to balance multi-modal maneuver generation with real-time inference constraints.
arXiv:2607. 23910v1 Announce Type: cross Abstract: Cooperative perception through vehicle-to-everything (V2X) communication can overcome the inherent physical limitations of individual autonomous vehicles, such as occlusions and limited sensor range.
arXiv:2607. 00283v1 Announce Type: cross Abstract: Autonomous vehicles must safely navigate complex environments where planning-critical agents may be hidden from view.
Efficient Multi-Modal Planning with Reward-Guided Preference Optimization for Autonomous Driving proposes EMPlan, a hybrid trajectory planning method that combines sparse anchors with an offset refinement module for low-latency, high-accuracy predictions. The approach uses a two-stage training paradigm—pretraining followed by reward-guided fine-tuning—to improve safety without extra inference cost, leveraging rule-based reward signals and unpaired preference supervision. EMPlan is evaluated on the non-reactive NAVSIM benchmark, achieving a favorable balance between planning accuracy and efficiency under real-time constraints.
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.
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.
ForeDrive introduces a planning-relevant latent world model that is asymmetrically coupled to a Diffusion Transformer planner. The model learns multi‑horizon latent futures with a JEPA‑style world model, while planning gradients update the shared encoder and stop‑gradient routing trains the predictor with forecasting losses only. Gated visual fusion, future‑status injection, and Trajectory‑Adaptive Bias are used to guide trajectory generation without overriding current observations, achieving high performance on NAVSIM benchmarks using only front‑view images and pure imitation learning.
arXiv:2606. 03159v1 Announce Type: cross Abstract: As autonomous vehicle capabilities advance, the safe evaluation of driving policies in long-tail scenarios remains a critical bottleneck.
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.
arXiv:2608.20890v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models have emerged as a powerful paradigm for end-to-end autonomous driving by jointly integrating perception, reasoning,...
arXiv:2609.35823v1 Announce Type: new Abstract: Vision-language models (VLMs) have made substantial progress in autonomous driving, but their success has primarily been studied in ego-centric scenes....