  The development of autonomous driving has attracted extensive attention in
recent years, and it is essential to evaluate the performance of autonomous
driving. However, testing on the road is expensive and inefficient. Virtual
testing is the primary way to validate and verify self-driving cars, and the
basis of virtual testing is to build simulation scenarios. In this paper, we
propose a training, testing, and evaluation pipeline for the lane-changing task
from the perspective of deep reinforcement learning. First, we design lane
change scenarios for training and testing, where the test scenarios include
stochastic and deterministic parts. Then, we deploy a set of benchmarks
consisting of learning and non-learning approaches. We train several
state-of-the-art deep reinforcement learning methods in the designed training
scenarios and provide the benchmark metrics evaluation results of the trained
models in the test scenarios. The designed lane-changing scenarios and
benchmarks are both opened to provide a consistent experimental environment for
the lane-changing task.
