Simulation-based testing has emerged as an essential tool for verifying and validating autonomous vehicles (AVs). However, contemporary methodologies, such as deterministic and imitation learning-based driver models, struggle to capture the variability of human-like driving behavior. Given these challenges, we propose HAD-Gen, a general framework for realistic traffic scenario generation that simulates diverse human-like driving behaviors. The framework first clusters the vehicle trajectory data into different driving styles according to safety features. It then employs maximum entropy inverse reinforcement learning on each of the clusters to learn the reward function corresponding to each driving style. Using these reward functions, the method integrates offline reinforcement learning pre-training and multi-agent reinforcement learning algorithms to obtain general and robust driving policies. Multi-perspective simulation results show that our proposed scenario generation framework can simulate diverse, human-like driving behaviors with strong generalization capability. The proposed framework achieves a 90.96% goal-reaching rate, an off-road rate of 2.08%, and a collision rate of 6.91% in the generalization test, outperforming prior approaches by over 20% in goal-reaching performance. The source code is released at https://github.com/RoboSafe-Lab/Sim4AD.

翻译：基于仿真的测试已成为验证和确认自动驾驶车辆（AVs）的重要工具。然而，现有方法，如确定性驾驶模型和基于模仿学习的驾驶员模型，难以捕捉类人驾驶行为的多样性。针对这些挑战，我们提出了HAD-Gen，一个用于生成真实交通场景的通用框架，能够模拟多样化的人性化驾驶行为。该框架首先根据安全特征将车辆轨迹数据聚类为不同的驾驶风格。随后，对每个聚类应用最大熵逆强化学习，以学习与各驾驶风格对应的奖励函数。利用这些奖励函数，该方法整合了离线强化学习预训练和多智能体强化学习算法，以获得通用且鲁棒的驾驶策略。多视角仿真结果表明，我们提出的场景生成框架能够模拟多样化、人性化的驾驶行为，并具有较强的泛化能力。在泛化测试中，所提框架实现了90.96%的目标到达率、2.08%的驶出道路率以及6.91%的碰撞率，其目标到达性能较先前方法提升了20%以上。源代码发布于 https://github.com/RoboSafe-Lab/Sim4AD。