An End-to-End autonomous driving model based on visual perception for temporary roads

The research on autonomous driving using deep learning has made significant progress on structured roads, but there has been limited research on temporary roads. The End-to-End autonomous driving model is highly integrated, allowing for the direct translation of input data into desired driving actions. This method eliminates inter-module coupling, thereby enhancing the safety and stability of autonomous vehicles.

Therefore, we propose a novel End-to-End model for autonomous driving on temporary roads specifically designed for mobile robots. The model takes three road images as input, extracts image features using the Global Context Vision Transformer (GCViT) network, plans local paths through a Transformer network and a gated recurrent unit (GRU) network, and finally outputs the steering angle through a control model to manage the automatic tracking of unmanned ground vehicles. To verify the model performance, both simulation tests and field tests were conducted.

The experimental results demonstrate that our End-to-End model accurately identifies temporary roads. The trajectory planning time for a single frame is approximately 100 ms, while the average trajectory deviation is 0.689 m. This performance meets the real-time processing requirements for low-speed vehicles, enabling unmanned vehicles to execute tracking tasks in temporary road environments.