Autonomous driving vehicles have drawn a great deal of interests from both academia (e.g. Oxford, MIT) and industry (e.g. Google, Tesla). However, we find that it is very difficult to directly achieve fully autonomous driving (SAE Level 5) due to generalised knowledge. To deal with the problem, deep imitation learning is a promising solution which learns knowledge from the demonstration of human. In this project, we worked on how to use deep imitation learning to achieve vehicle dynamic control (e.g. steering angle, speed). We used a dataset and simulator provided by Udacity (https://github.com/udacity/self-driving-car-sim) and the real-world comma.ai dataset.

You can download the comma.ai driving dataset here.

The instructions described are related to the Windows OS. They are pretty straightforward given that the user has already downloaded the necessary project files and meets the dependencies and requirements mentioned in Chapter B. One can obtain the program scripts from our GitHub repository.

The comma.ai dataset is split into 3 types of raw recordings prior to being preprocessed. This process is carried out manually and no script is developed for the purpose. We present the actions required to preprocess these 3 types of recording data. In contrast, the Udacity dataset is preprocessed on the go while training the models via a batcher function thus no additional steps are required.

• Step 1. Enter a command prompt terminal window;

• Step 2: Navigate to the directory of the comma.ai dataset model training scripts by using the cd command;

  • Alternatively, one could go to the model training program files directory via Windows File Explorer, use the Ctrl+L keyboard shortcut to go to the address bar and enter cmd, as shown in Figure A.1, to launch a command prompt from the current file location;

• Step 3: Run the data preprocessing script by entering the following command line arguments into the terminal:

  python preprocess_data.py rec_type [batch_size]

  • The rec_type should be set to the value of either sunny, cloudy or night since we have 3 types of video files. On the other hand, the optional batch_size argument above can be replaced with the desired numerical size value of each batch. It is set to 64 by default.

You can skip the above steps and directly download the 64-batched, preprocessed dataset here.