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。

自主高速公路驾驶的预测决策

Prediction Based Decision Making for Autonomous Highway Driving

In highway scenarios, an alert human driver will typically anticipate early
cut-in and cut-out maneuvers of surrounding vehicles using only visual cues. An
automated system must anticipate these situations at an early stage too, to
increase the safety and the efficiency of its performance. To deal with
lane-change recognition and prediction of surrounding vehicles, we pose the
problem as an action recognition/prediction problem by stacking visual cues
from video cameras. Two video action recognition approaches are analyzed:
two-stream convolutional networks and spatiotemporal multiplier networks.
Different sizes of the regions around the vehicles are analyzed, evaluating the
importance of the interaction between vehicles and the context information in
the performance. In addition, different prediction horizons are evaluated. The
obtained results demonstrate the potential of these methodologies to serve as
robust predictors of future lane-changes of surrounding vehicles in time
horizons between 1 and 2 seconds.

利用两种视频动作识别方法对高速公路情境中的自动驾驶变道识别进行了预测，研究结果表明这些方法在 1 到 2 秒的时间范围内预测周围车辆的未来车道变更具有潜在的预测能力。