SceneFactory：基于GPU的多智能体驾驶仿真与物理驱动车辆动力学
SceneFactory: GPU-Accelerated Multi-Agent Driving Simulation with Physics-Based Vehicle Dynamics
AI总结：
SceneFactory通过GPU向量化技术实现高吞吐量的多智能体驾驶仿真，结合物理驱动车辆动力学与强化学习，展示可扩展的自动驾驶训练无需牺牲物理真实性。
摘要：
自动驾驶仿真器通常在物理真实性与可扩展并行性之间进行权衡。基于物理的平台如CARLA和MetaDrive提供关节式车辆动力学和接触，但其非向量化接口使批量训练困难。GPU批量系统如Waymax和GPUDrive通过将刚体动力学替换为简化动力学模型，省略轮胎-路面相互作用、悬挂系统、接触动力学和路面条件依赖的摩擦力，以扩展到数百个场景。我们引入SceneFactory，一个基于NVIDIA Isaac Sim + Isaac Lab的GPU向量化平台，用于程序化场景构建、基于物理的多智能体仿真和自动驾驶环境中的强化学习。SceneFactory将世界和智能体表示为批量张量：控制、观测、奖励、重置和策略推断作为GPU张量操作在Isaac Lab张量API上运行。SceneFactory将Waymo Open Motion Dataset的道路拓扑转换为仿真准备的USD世界，在一个GPU上并发运行多个世界，每个世界填充多个关节式PhysX车辆，并将降水和路面类型映射到PhysX材料摩擦系数。通过GPU向量化，SceneFactory在相同GPU和物理求解器上比非向量化PhysX基线提高了127倍的吞吐量，达到每秒19,250个受控智能体仿真步骤，在256个世界×16个智能体的情况下。跨仿真器转移揭示了动态学差距的不对称性：基于物理的强化学习策略转移到简化动力学自行车模型的成功率高达99.5%，而反向转移则降至47.3%。在湿路面摩擦下，摩擦感知策略将平均峰值DRAC从58.7减少到27.8 m/s²，而无需牺牲目标达成率。SceneFactory表明，可扩展的自动驾驶训练无需放弃关节式刚体动力学或物理基础的路面条件变化。
英文摘要：
Autonomous-driving simulators typically trade physical fidelity for scalable parallelism. Physics-based platforms such as CARLA and MetaDrive provide articulated vehicle dynamics and contact, but their non-vectorized interfaces make batched training difficult. GPU-batched systems such as Waymax and GPUDrive scale to hundreds of scenarios by replacing rigid-body physics with simplified kinematic models, omitting tire--road interaction, suspension, contact dynamics, and road-condition-dependent friction. We introduce SceneFactory, a GPU-vectorized platform for procedural scene construction, physics-based multi-agent simulation, and RL in autonomous-driving environments. Built on NVIDIA Isaac Sim + Isaac Lab, SceneFactory represents worlds and agents as batched tensors: control, observations, rewards, resets, and policy inference run as GPU tensor operations over the Isaac Lab tensor API. SceneFactory converts Waymo Open Motion Dataset road topologies into simulation-ready USD worlds, runs many worlds concurrently on one GPU, populates each with multiple articulated PhysX vehicles, and maps precipitation and road-surface type to PhysX material friction coefficients. With GPU vectorization, SceneFactory achieves up to 127$\times$ higher throughput than a non-vectorized PhysX baseline on the same GPU and physics solver, reaching 19,250 controlled-agent simulation steps per second at 256 worlds $\times$ 16 agents. Cross-simulator transfer reveals an asymmetric dynamics gap: physics-grounded RL policies transfer to a simplified kinematic bicycle model with 99.5% success, whereas reverse transfer drops to 47.3%. Under wet-road friction, friction-aware policies reduce mean peak DRAC from 58.7 to 27.8,m/s$^2$ without sacrificing goal reach. SceneFactory shows that scalable autonomous-driving training need not discard articulated rigid-body dynamics or physically grounded road-condition variation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
