High-quality driving data are essential for autonomous-driving systems and generative world models. However, rare and safety-critical scenarios involving adverse weather, braking under low tire--road friction, and uneven road geometry are costly and risky to collect at scale.
arXiv:2607. 13319v1 Announce Type: cross Abstract: High-speed off-road autonomy requires precise closed-loop control for a target vehicle while remaining robust across changing terrains.
arXiv:2609.31374v1 Announce Type: cross Abstract: Closed-loop driving simulation requires rendered observations to remain reliable as the ego vehicle and surrounding actors move beyond their recorded...
arXiv:2606. 07366v1 Announce Type: cross Abstract: Self-driving simulations typically rely on data collected in a small number of cities or on hand-authored synthetic scenarios.
arXiv:2607. 09764v1 Announce Type: cross Abstract: The synthesis of safety-critical scenarios (SCS) and their evaluation through closed-loop simulations are crucial for developing robust autonomous driving systems.
Traffic microsimulators rely on hand-crafted behavior models that reproduce aggregate flow but miss the heterogeneous interactions between vehicles at signalized intersections. Learned trajectory predictors capture richer interactions but are short-horizon and tend to be unstable when run in closed loop.
arXiv:2607. 07601v1 Announce Type: cross Abstract: Safety evaluation for autonomous driving is dominated by rare, safety-critical interactions, motivating simulators that can deliberately synthesize corner cases with photorealistic observations.
The paper introduces Feel‑WM, an off‑road navigation world model that incorporates proprioceptive data to predict both visual scenes and the robot’s physical sensations such as slip, tilt, and shake. By learning a future proprioceptive state and failure risk from the robot’s own experience, the model can evaluate planned trajectories using a separable score that balances goal similarity with predicted failure risk. Experiments on real and simulated off‑road data show that Feel‑WM outperforms visual‑only models in both open‑loop planning and closed‑loop navigation for wheeled and legged robots, and it successfully guides a Husky robot around rough terrain on mountain trails where an end‑to‑end policy fails.
DrivingBench is the first benchmark that tests general‑purpose vision‑language models on the task of driving a real Toyota Corolla around a parking‑lot cone course. The models receive live camera frames and issue steering and velocity commands, with inference latency counted as part of the challenge. In tests, only GPT‑6 Astra completed the course, while other models showed limited progress or failed to pass half the course.
arXiv:2606. 02762v1 Announce Type: new Abstract: Knowledge of real-time road slipperiness, or even better, a refined estimate of peak grip potential, is a critical input for vehicle warning and intervention control systems.
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:2606. 15749v1 Announce Type: cross Abstract: Traffic scene understanding requires models to reason beyond object recognition, including lane topology, multi-view geometry, temporal evolution, and signal-phase semantics.
arXiv:2609.22762v1 Announce Type: new Abstract: Generative world-action models (WAMs) jointly generate future video and vehicle actions, while their action branches remain primarily optimized by expe...