This paper proposes a imitation learning model for autonomous driving on highway traffic by mimicking human drivers' driving behaviours. The study utilizes the HighD traffic dataset, which is complex, high-dimensional, and diverse in vehicle variations. Imitation learning is an alternative solution to autonomous highway driving that reduces the sample complexity of learning a challenging task compared to reinforcement learning. However, imitation learning has limitations such as vulnerability to compounding errors in unseen states, poor generalization, and inability to predict outlier driver profiles. To address these issues, the paper proposes mixture density network behaviour cloning model to manage complex and non-linear relationships between inputs and outputs and make more informed decisions about the vehicle's actions. Additional improvement is using collision penalty based on the GAIL model. The paper includes a simulated driving test to demonstrate the effectiveness of the proposed method based on real traffic scenarios and provides conclusions on its potential impact on autonomous driving.

翻译：本文提出了一种通过模仿人类驾驶员驾驶行为，用于高速公路交通的自主驾驶模仿学习模型。研究使用了HighD交通数据集，该数据集复杂度高、维度高，且车辆变异性多样。模仿学习是高速公路自主驾驶的一种替代解决方案，与强化学习相比，它降低了学习复杂任务的样本复杂度。然而，模仿学习存在局限性，例如在未见状态中易受复合误差影响、泛化能力差，以及无法预测异常驾驶员分布。为解决这些问题，本文提出了混合密度网络行为克隆模型，以管理输入与输出之间的复杂非线性关系，并对车辆动作做出更明智的决策。另一项改进是基于GAIL模型使用碰撞惩罚。本文通过基于真实交通场景的模拟驾驶测试，展示了所提方法的有效性，并对其在自主驾驶中的潜在影响进行了总结。