用提示词模拟真实交通行为，自动生成自动驾驶测试场景。
Test Automation for Interactive Scenarios via Promptable Traffic Simulation
- 用低维目标位置参数化人类行为，通过提示词引导仿真器生成行为。
- 基于贝叶斯优化自动发现安全关键场景，提升测试效率。
- 适合自动驾驶规划算法评估，尤其在复杂交互场景中表现优异。
自动驾驶规划器在大规模部署前需经严格评估，尤其要检验其对人类行为不确定性的鲁棒性。尽管数据驱动的场景生成技术已能模拟交互环境中的真实人类行为，但如何利用这些模型构建全面的测试仍是个挑战。本文提出一种自动化方法，高效生成用于自动驾驶规划器评估的现实且安全关键的人类行为。通过低维目标位置参数化复杂人类行为，并输入可提示的交通仿真器ProSim以引导模拟代理的行为。为实现测试自动生成，引入提示词生成模块，利用贝叶斯优化探索目标空间，高效识别安全关键行为。该方法应用于基于优化的规划器评估，在不同初始条件下均能自动生成多样且真实的驾驶行为，验证了其有效性和效率。
原文摘要 · Abstract (English)
Autonomous vehicle (AV) planners must undergo rigorous evaluation before widespread deployment on public roads, particularly to assess their robustness against the uncertainty of human behaviors. While recent advancements in data-driven scenario generation enable the simulation of realistic human behaviors in interactive settings, leveraging these models to construct comprehensive tests for AV planners remains an open challenge. In this work, we introduce an automated method to efficiently generate realistic and safety-critical human behaviors for AV planner evaluation in interactive scenarios. We parameterize complex human behaviors using low-dimensional goal positions, which are then fed into a promptable traffic simulator, ProSim, to guide the behaviors of simulated agents. To automate test generation, we introduce a prompt generation module that explores the goal domain and efficiently identifies safety-critical behaviors using Bayesian optimization. We apply our method to the evaluation of an optimization-based planner and demonstrate its effectiveness and efficiency in automatically generating diverse and realistic driving behaviors across scenarios with varying initial conditions.
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