Autonomous driving decision-making is a challenging task due to the inherent
complexity and uncertainty in traffic. For example, adjacent vehicles may
change their lane or overtake at any time to pass a slow vehicle or to help
traffic flow. Anticipating the intention of surrounding vehicles, estimating
their future states and integrating them into the decision-making process of an
automated vehicle can enhance the reliability of autonomous driving in complex
driving scenarios. This paper proposes a Prediction-based Deep Reinforcement
Learning (PDRL) decision-making model that considers the manoeuvre intentions
of surrounding vehicles in the decision-making process for highway driving. The
model is trained using real traffic data and tested in various traffic
conditions through a simulation platform. The results show that the proposed
PDRL model improves the decision-making performance compared to a Deep
Reinforcement Learning (DRL) model by decreasing collision numbers, resulting
in safer driving.

本文提出了一种基于预测的深度强化学习决策模型，考虑了周围车辆的操作意图，并使用真实交通数据进行训练。经过模拟测试，结果表明和基于深度强化学习的模型相比，该模型在复杂的高速公路驾驶场景中提高了决策性能，从而减少了碰撞数量，实现了更安全的驾驶。