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

SwapTransformer: highway overtaking tactical planner model via imitation learning on OSHA dataset