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：本文提出一种模仿学习模型，通过模拟人类驾驶行为实现高速公路自动驾驶。研究采用HighD交通数据集，该数据集具有复杂、高维及车辆类型多样的特点。模仿学习作为高速公路自动驾驶的替代方案，相比强化学习能降低复杂任务的样本复杂度。然而，模仿学习存在局限性，如对未观测状态的复合误差脆弱、泛化能力差、无法预测异常驾驶行为等。针对上述问题，本文提出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，并做出更优的车辆动作决策。进一步改进是引入基于GAIL模型的碰撞惩罚机制。本文通过基于真实交通场景的仿真驾驶测试验证了所提方法的有效性，并总结了其对自动驾驶的潜在影响。