用可微交通模拟器优化车辆轨迹，支持实时大规模仿真。
Gradient-based Trajectory Optimization with Parallelized Differentiable Traffic Simulation
- 基于智能驾驶者模型构建可微分并行交通模拟器。
- 支持200万车辆实时仿真，实现轨迹滤噪、补全与预测。
- 适合交通规划、自动驾驶等需要物理一致性轨迹的场景。
我们提出一种基于智能驾驶者模型（IDM）的并行化可微交通模拟器，该模型将驾驶员行为作为关键变量，高效建模车辆运动，生成可监督拟合真实数据的轨迹。利用其可微特性，通过梯度方法优化IDM参数。系统可实时模拟最多200万车辆，具备大规模轨迹优化的可扩展性。我们证明该模拟器可用于输入轨迹去噪（轨迹滤波）、从稀疏数据重建稠密轨迹（轨迹补全），以及未来轨迹预测，所有生成轨迹均遵守物理规律。我们在NGSIM和Waymo Open Dataset等多个数据集上验证了模拟器与算法的有效性。代码已公开：https://github.com/SonSang/diffidm。
原文摘要 · Abstract (English)
We present a parallelized differentiable traffic simulator based on the Intelligent Driver Model (IDM), a car-following framework that incorporates driver behavior as key variables. Our vehicle simulator efficiently models vehicle motion, generating trajectories that can be supervised to fit real-world data. By leveraging its differentiable nature, IDM parameters are optimized using gradient-based methods. With the capability to simulate up to 2 million vehicles in real time, the system is scalable for large-scale trajectory optimization. We show that we can use the simulator to filter noise in the input trajectories (trajectory filtering), reconstruct dense trajectories from sparse ones (trajectory reconstruction), and predict future trajectories (trajectory prediction), with all generated trajectories adhering to physical laws. We validate our simulator and algorithm on several datasets including NGSIM and Waymo Open Dataset. The code is publicly available at: https://github.com/SonSang/diffidm.
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