SwapTransformer: highway overtaking tactical planner model via imitation learning on OSHA dataset
This addresses the specific problem of highway tactical planning for autonomous driving systems, representing an incremental improvement in this domain.
This paper tackles the problem of automatic overtaking and lane changing for highway travel assist systems by developing SwapTransformer, an imitation learning model trained on a new simulated dataset of 9 million samples. The model outperformed baseline approaches in simulation across various traffic densities on metrics including lap completion time and number of overtakes.
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.