Lane-change maneuvers are commonly executed by drivers to follow a certain
routing plan, overtake a slower vehicle, adapt to a merging lane ahead, etc.
However, improper lane change behaviors can be a major cause of traffic flow
disruptions and even crashes. While many rule-based methods have been proposed
to solve lane change problems for autonomous driving, they tend to exhibit
limited performance due to the uncertainty and complexity of the driving
environment. Machine learning-based methods offer an alternative approach, as
Deep reinforcement learning (DRL) has shown promising success in many
application domains including robotic manipulation, navigation, and playing
video games. However, applying DRL to autonomous driving still faces many
practical challenges in terms of slow learning rates, sample inefficiency, and
safety concerns. In this study, we propose an automated lane change strategy
using proximal policy optimization-based deep reinforcement learning, which
shows great advantages in learning efficiency while still maintaining stable
performance. The trained agent is able to learn a smooth, safe, and efficient
driving policy to make lane-change decisions (i.e. when and how) in a
challenging situation such as dense traffic scenarios. The effectiveness of the
proposed policy is validated by using metrics of task success rate and
collision rate. The simulation results demonstrate the lane change maneuvers
can be efficiently learned and executed in a safe, smooth, and efficient
manner.

这篇论文研究了如何利用无人驾驶中的深度强化学习技术来优化车辆变道的策略，通过使用近端策略优化来提高学习效率和性能表现，进一步验证了该策略的有效性和安全性。