Benchmarking Lane-changing Decision-making for Deep Reinforcement Learning
This work provides a standardized virtual testing environment for researchers in autonomous driving, though it is incremental as it builds on existing methods without introducing new algorithms.
The authors tackled the problem of evaluating autonomous driving lane-changing decisions by proposing a training, testing, and evaluation pipeline using deep reinforcement learning, resulting in the creation of open-source scenarios and benchmarks with metrics for performance comparison.
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.