This paper investigates the high-level decision-making problem in highway
scenarios regarding lane changing and over-taking other slower vehicles. In
particular, this paper aims to improve the Travel Assist feature for automatic
overtaking and lane changes on highways. About 9 million samples including lane
images and other dynamic objects are collected in simulation. This data;
Overtaking on Simulated HighwAys (OSHA) dataset is released to tackle this
challenge. To solve this problem, an architecture called SwapTransformer is
designed and implemented as an imitation learning approach on the OSHA dataset.
Moreover, auxiliary tasks such as future points and car distance network
predictions are proposed to aid the model in better understanding the
surrounding environment. The performance of the proposed solution is compared
with a multi-layer perceptron (MLP) and multi-head self-attention networks as
baselines in a simulation environment. We also demonstrate the performance of
the model with and without auxiliary tasks. All models are evaluated based on
different metrics such as time to finish each lap, number of overtakes, and
speed difference with speed limit. The evaluation shows that the
SwapTransformer model outperforms other models in different traffic densities
in the inference phase.

该研究聚焦于高速公路场景中的高层次决策问题，旨在改善高速公路上自动超车和变道的旅行辅助功能。研究提出了名为 SwapTransformer 的架构作为模仿学习方法，通过辅助任务来帮助模型更好地理解周围环境。实验结果表明，SwapTransformer 模型在不同的交通密度下在推理阶段表现优于其他模型。