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.

翻译：本文研究了高速公路场景中涉及变道及超越其他慢速车辆的高层决策问题。具体而言，旨在提升高速公路自动超车与变道功能的旅行辅助特性。通过仿真采集了包含车道图像及其他动态物体在内的约900万个样本；为应对这一挑战，发布了名为“高速公路超车仿真”（OSHA）的数据集。针对该问题，设计并实现了一种名为SwapTransformer的架构，作为基于OSHA数据集的模仿学习方法。此外，提出了未来轨迹点预测及车距网络预测等辅助任务，以帮助模型更好地理解周围环境。在仿真环境中，将所提方案与多层感知机（MLP）及多头自注意力网络等基线模型进行了性能对比。本文还展示了有无辅助任务时模型的表现差异。所有模型均基于不同指标进行评价，包括每圈完成时间、超车次数以及与限速的速度差值。评估结果表明，SwapTransformer模型在推理阶段不同交通密度下均优于其他模型。