How to Program a Self-Driving Car: A Practical Guide

Self-driving cars, once a futuristic fantasy, are rapidly becoming a reality. At the heart of this revolution is sophisticated programming, allowing vehicles to navigate and operate autonomously. If you’re fascinated by the technology behind autonomous vehicles and want to understand How To Program A Self Driving Car, this guide offers a practical, hands-on approach to training an AI model for autonomous driving using behavioral cloning.

Gathering Driving Data for AI Training

The first step in programming your self-driving car is to teach it how to drive like you. This is achieved through a process called behavioral cloning, where the AI learns to mimic your driving behavior by observing and recording your actions. To begin, you’ll need to collect data by manually driving your vehicle while running a data logging script.

Running the Data Collection Script

1. Execute the script by entering the command: CUDA_VISIBLE_DEVICES=0 python3 driveTrainData.py. This command initiates the data collection process.
2. You will be prompted to name your data folder. Choose a descriptive name, such as “run1”, to easily identify your datasets later.
3. Before pressing enter, position yourself in your vehicle and prepare to drive.
4. Set your vehicle’s physical mode button to learn mode. In this mode, the input from your throttle sensor is directed to pin A1 on the Arduino, ensuring your driving actions are recorded.
5. Press enter to start the script and begin driving. Crucially, the driveTrainData.py script should only be active while the vehicle is in motion. Stop the script before halting the vehicle to ensure data integrity.

Driving Techniques for Effective Data Collection

To create a robust AI model, it’s essential to gather comprehensive driving data. This means not just driving as you normally would, but also intentionally driving towards the edges of the road. This technique helps the AI learn to recognize road boundaries and stay within them. For further insights into effective data collection strategies, resources from the Donkeycar community, such as instructional videos, can be incredibly valuable.

Training Your AI Model

Once you have collected sufficient driving data, the next step is to train your AI model. The data folders you created are stored in the root directory of your project. To prepare for training, copy these folders to the designated data directory within your project structure, typically located at “Advanced-Real-Time-Self-Driving/tree/master/Ai_based/donkeycar/data”. Maintaining this folder structure is critical to prevent errors during the training process.

With your data in place, initiate the training process by running the following command in your terminal:

python3 Train.py --model=trained --type=linear

This command starts the training script, utilizing your collected data to train an AI model. Upon completion, you will have a trained.h5 file. This file is your trained AI network, containing the learned driving behaviors.

Testing Your Trained AI Model

Before deploying your newly trained AI model for autonomous driving, it’s wise to test it in a simulated environment. This precautionary step can help identify potential issues and prevent real-world mishaps.

To test your model, you will need to modify the SegmentationOnly.py script.

1. Specify your AI model: In line 101 of SegmentationOnly.py, update the driveModel variable to load your trained model file. For example, change the line to: driveModel = loadModel('trained.h5').
2. Define the test video path: In line 167, set the path to a video file you wish to use for testing. This video will serve as the environment for your AI model to navigate.

Now, run the testing script using the command:

CUDA_VISIBLE_DEVICES=0 python3 SegmentationOnly.py

This will launch a window displaying a segmented view and a red line. This red line represents the AI model’s predictions. The angle of the line indicates the predicted steering direction, and its length signifies the predicted speed. Observe the red line to assess if the AI is making sensible driving decisions. If the predictions appear erratic or random, it may indicate issues with the training process.

Troubleshooting and Refinement

If your AI model doesn’t perform as expected during testing, consider these troubleshooting steps:

1. Increase Training Data: Driving more and gathering a larger dataset can significantly improve the AI’s learning and performance.
2. Review Data Collection Techniques: Re-examine resources like the Donkeycar community videos to refine your data collection methods.
3. Sensor Recalibration: If sensor calibration is suspected to be off, recalibrate your sensors and collect new data. Remember that data collected before recalibration may not be compatible with data collected after.
4. Utilize Pre-trained Resources: Explore pre-trained models and datasets available in repositories like the Github repo. These can serve as a starting point or a reference for comparison.

With a successfully trained and tested AI model, you are now ready to take the exciting step of autonomous driving!