Merging behaviour is a fundamental yet challenging driving task which has significant impact on traffic flow operations. While numerous efforts have been made on the modelling of decision-making of lane-based merging behaviour, little was focusing on the simulation of the complete merging process, which generates two-dimensional merging trajectories allowing for the investigation of merging behaviour’s impact on the traffic flow. This study thus aims to develop a two-dimensional data-driven simulation model for merging behaviour based on the emerging imitation learning framework for generating realistic vehicle trajectories and traffic characteristics. To account for the specific goal of merging vehicles (reaching the planned destination) and the presence of suboptimal behaviours in real traffic, we incorporate goal-conditioned and confidence-aware mechanisms into adversarial inverse reinforcement learning (is referred to as GC-AIRL) to learn the merging behaviour from real-traffic demonstrations. Using the vehicle trajectories data extracted from the NGSIM dataset, we demonstrate that the proposed model is capable of generating two-dimensional vehicle trajectories with superior efficiency, safety, and comfort performance compared to human drivers. The superiority of the GC-AIRL model is validated by comparing with several bench-marking models, including the basic AIRL model, generative adversarial imitation learning (GAIL) model, a reinforcement learning (RL) based model, and long short-term memory (LSTM) model. Moreover, we examine the transferability of the proposed model in producing merging behaviour at a new site that indicates its applicability in diverse environments. The findings of this study highlight the great potential of the developed two-dimensional merging behaviour model for future application in connected and automated vehicles.

中文翻译：

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通过模仿学习从车辆轨迹中学习二维并道行为

并道行为是一项基本但具有挑战性的驾驶任务，对交通流运行具有重大影响。尽管人们在基于车道的并道行为决策建模方面做出了大量努力，但很少关注完整的并道过程的模拟，该过程会生成二维并道轨迹，以便研究并道行为对交通的影响流动。因此，本研究旨在基于新兴的模仿学习框架开发一种二维数据驱动的并道行为仿真模型，以生成真实的车辆轨迹和交通特征。为了考虑合并车辆的具体目标（到达计划目的地）以及实际交通中存在的次优行为，我们将目标条件和置信感知机制纳入对抗性逆强化学习（称为 GC-AIRL）中，以从真实交通演示中学习并道行为。使用从 NGSIM 数据集中提取的车辆轨迹数据，我们证明所提出的模型能够生成二维车辆轨迹，与人类驾驶员相比，具有更高的效率、安全性和舒适性。通过与基本 AIRL 模型、生成对抗性模仿学习（GAIL）模型、基于强化学习（RL）的模型和长短期记忆模型等多个基准模型的比较，验证了 GC-AIRL 模型的优越性。 LSTM）模型。此外，我们检查了所提出的模型在新站点产生合并行为的可迁移性，这表明其在不同环境中的适用性。这项研究的结果凸显了所开发的二维并道行为模型在未来联网和自动驾驶车辆中应用的巨大潜力。