Automated Lane Change Strategy using Proximal Policy Optimization-based Deep Reinforcement Learning

Understand

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.