用生成对抗模仿学习生成真实城市驾驶轨迹，考虑周围车辆与道路结构。
Ctx2TrajGen: Traffic Context-Aware Microscale Vehicle Trajectories using Generative Adversarial Imitation Learning
- 基于GAIL框架，结合PPO与WGAN-GP建模复杂交通交互。
- 在DRIFT数据集上生成轨迹的逼真度、多样性与上下文一致性均优于现有方法。
- 适合自动驾驶仿真与交通行为分析，尤其适用于数据稀缺场景。
精确建模微观车辆轨迹对交通行为分析与自动驾驶系统至关重要。我们提出Ctx2TrajGen，一种基于上下文感知的轨迹生成框架，利用生成对抗模仿学习（GAIL）合成真实的城市驾驶行为。通过结合PPO与WGAN-GP，模型有效缓解了微观交通设置中固有的非线性依赖关系与训练不稳定性。显式地以周边车辆和道路几何为条件，生成具有交互意识且贴合真实情境的轨迹。在无人机采集的DRIFT数据集上的实验表明，该方法在逼真度、行为多样性及上下文保真度方面均优于现有方法，无需依赖模拟即可应对数据稀缺与域偏移问题。
原文摘要 · Abstract (English)
Precise modeling of microscopic vehicle trajectories is critical for traffic behavior analysis and autonomous driving systems. We propose Ctx2TrajGen, a context-aware trajectory generation framework that synthesizes realistic urban driving behaviors using GAIL. Leveraging PPO and WGAN-GP, our model addresses nonlinear interdependencies and training instability inherent in microscopic settings. By explicitly conditioning on surrounding vehicles and road geometry, Ctx2TrajGen generates interaction-aware trajectories aligned with real-world context. Experiments on the drone-captured DRIFT dataset demonstrate superior performance over existing methods in terms of realism, behavioral diversity, and contextual fidelity, offering a robust solution to data scarcity and domain shift without simulation.
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