SHRED is a framework that trains vehicles, pedestrians, and cyclists through large‑scale heterogeneous self‑play from random initialization, without using expert demonstrations or logged trajectories. By combining type‑specific dynamics, procedural scenario generation, and per‑agent randomization, SHRED allows diverse, realistic behaviors to emerge through closed‑loop interaction. In benchmarks such as the Waymo Open Sim Agents Challenge and InterPlan, SHRED outperforms demonstration‑free baselines and prior self‑play methods, showing that heterogeneity in self‑play improves both traffic realism and planning performance.
arXiv:2607. 13028v1 Announce Type: cross Abstract: Training robust autonomous driving agents requires a simulator that is fast enough for reinforcement learning at scale, realistic enough to ground behavior in real-world map structure, and diverse enough to cover the safety-critical long tail that logged data rarely contains.
arXiv:2606. 19370v1 Announce Type: cross Abstract: Self-play reinforcement learning has recently emerged as a way to train driving policies without any human data.
arXiv:2608.22187v1 Announce Type: cross Abstract: Modern driving action models are increasingly improved in a self-improvement loop, where a learned world simulator imagines future observations and t...
The paper introduces Ctrl-CWM, a multi‑agent controllable crowd world model that merges crowd generation with run‑time control. It uses an encoder to learn human motion dynamics, an actor to propose pedestrian displacements, a critic to evaluate imagined trajectories, and a planner that selects actions based on critic scores and user‑defined costs. Experiments show that Ctrl‑CWM outperforms state‑of‑the‑art methods in realism and collision metrics while allowing new control objectives to be added without retraining.
arXiv:2506. 12283v2 Announce Type: replace Abstract: Modeling vehicle interactions at unsignalized intersections is a challenging task due to the complexity of the underlying game-theoretic processes.
arXiv:2607. 10630v1 Announce Type: cross Abstract: Robust motion planning in dense traffic requires autonomous vehicles to interact in rare and safety-critical scenarios that are underrepresented in naturalistic driving data.
arXiv:2607. 21488v1 Announce Type: cross Abstract: Coordinating autonomous vehicles at unsignalized intersections remains a critical challenge for multi-agent reinforcement learning (MARL) systems, which typically struggle with combinatorial action spaces, reliance on privileged information, or rigid agent designs.
arXiv:2508. 16947v2 Announce Type: replace-cross Abstract: Despite significant progress, imitation learning-based autonomous driving planners remain largely restricted to reproducing high-frequency biased behaviors, overlooking the inherent behavioral diversity of human driving.
Closed-loop traffic simulation remains challenging because it must generate interactive multi-agent behaviors that are scene-consistent and controllable throughout rollout. Prior diffusion-based approaches achieve strong realism, but their computational cost can hinder deployment in time-constrained replanning loops for autonomous vehicle planning and simulation.
arXiv:2609.35916v1 Announce Type: cross Abstract: Real-world embodied agents often pursue independent objectives within a shared physical environment, where their actions can alter the conditions fac...
arXiv:2606. 17386v1 Announce Type: cross Abstract: End-to-end autonomous driving has achieved state-of-the-art performance on benchmarks and real-world deployments.
arXiv:2408.15538v4 Announce Type: replace Abstract: While modern Autonomous Vehicle (AV) systems can develop reliable driving policies under regular traffic conditions, they frequently struggle with...