Summary of Swaptransformer: Highway Overtaking Tactical Planner Model Via Imitation Learning on Osha Dataset, by Alireza Shamsoshoara et al.
SwapTransformer: highway overtaking tactical planner model via imitation learning on OSHA dataset
by Alireza Shamsoshoara, Safin B Salih, Pedram Aghazadeh
First submitted to arxiv on: 2 Jan 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Robotics (cs.RO)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a solution for improving Travel Assist features for automatic lane changing and overtaking on highways. The researchers collect 9 million samples of lane images and dynamic objects through simulation, creating the Overtaking on Simulated HighwAys (OSHA) dataset. They design an architecture called SwapTransformer using imitation learning on this dataset, incorporating auxiliary tasks to enhance the model’s understanding of its environment. The performance is compared with MLP and multi-head self-attention networks as baselines in a simulation environment, showing that the proposed solution outperforms others in various traffic densities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study aims to help cars make better decisions on highways by improving their ability to change lanes and pass slower vehicles. To do this, researchers created a big dataset of highway scenarios using computer simulations. They then developed an AI model called SwapTransformer that can learn from this data and make good decisions about when to change lanes or overtake other cars. The model performs better than previous approaches in different traffic conditions. |
Keywords
* Artificial intelligence * Self attention
