--Simulation-based testing has emerged as an essential tool for verifying and validating autonomous vehicles (A Vs). 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. 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. UTONOMOUS vehicles (A Vs) represent a groundbreaking advancement in transportation technology, with the potential to enhance road safety, reduce traffic congestion, and improve overall mobility efficiency [1]. Over the past decade, significant progress has been made in transforming A Vs from theoretical concepts into practical implementations, with numerous prototypes successfully navigating urban environments [2]. Despite these advancements, ensuring the safety and reliability of A Vs across diverse real-world scenarios remains a significant challenge [3]. This work was funded by UK Research and Innovation (UKRI) under the UK government's Horizon Europe funding guarantee [grant number EP/Z533464/1]. Cheng Wang is with the School of Engineering and Physical Sciences, Heriot-Watt University, Edinburgh EH14 4AS, United Kingdom (e-mail: cheng.wang@hw.ac.uk). Lingxin Kong is with the School of Automation and Software Engineering, Shanxi University, Taiyuan 030031, China (e-mail: 202223601006@email.sxu.edu.cn).

Goal recognition (GR) involves inferring the goals of other vehicles, such as a certain junction exit, which can enable more accurate prediction of their future behaviour. In autonomous driving, vehicles can encounter many different scenarios and the environment may be partially observable due to occlusions. We present a novel GR method named Goal Recognition with Interpretable Trees under Occlusion (OGRIT). OGRIT uses decision trees learned from vehicle trajectory data to infer the probabilities of a set of generated goals. We demonstrate that OGRIT can handle missing data due to occlusions and make inferences across multiple scenarios using the same learned decision trees, while being computationally fast, accurate, interpretable and verifiable. We also release the inDO, rounDO and OpenDDO datasets of occluded regions used to evaluate OGRIT.