The journey to autonomous vehicles is paved with fascinating technologies, and at the heart of it lies Artificial Intelligence (AI). If you’re curious about how to program a self-driving car, you might be surprised to learn that one accessible method involves teaching an AI to mimic human driving behavior. This technique, known as behavioral cloning, is a powerful approach to creating autonomous systems without explicitly coding every driving rule. Let’s explore how you can get started training an AI for your own self-driving car project.
Understanding Behavioral Cloning for Autonomous Driving
Behavioral cloning is an AI training method where the system learns to replicate actions by observing examples. In the context of self-driving cars, this means showing the AI how a human drives and letting it learn to imitate those driving patterns. Instead of programming complex rules for every possible driving scenario, you train the AI by driving the car yourself while the system records your actions and sensor data. This data becomes the foundation for the AI to learn autonomous driving.
Data Collection: Teaching Your AI to See and Drive
The first step in programming your self-driving car is gathering training data. This involves driving your vehicle while recording the inputs and outputs that the AI will learn to associate. Using a script like driveTrainData.py, you can initiate this data collection process.
To begin, execute the script using the command line interface:
CUDA_VISIBLE_DEVICES=0 python3 driveTrainData.pyYou will be prompted to name a folder for your data, for example, “run1”. Before pressing enter, position yourself in your vehicle and activate the ‘learn mode’ via the physical mode button (refer to step 3 in your vehicle’s setup guide for button details). In this mode, the throttle sensor signal is directed to pin A1 on the Arduino, enabling data recording during driving. Once ready, press enter and commence driving. It’s crucial that the driveTrainData.py script runs only when the vehicle is in motion. To stop data collection, first halt the script before stopping the vehicle.
For effective AI training, it’s essential to drive not just in your typical style but also to demonstrate driving near the edges of the road. This helps the AI understand road boundaries. For more insights on optimizing your data collection, resources from the Donkeycar community, such as instructional videos, can be very beneficial.
Your collected data folders will be located in the root directory of your Github repository. Transfer these folders to the designated “Advanced-Real-Time-Self-Driving/tree/master/Ai_based/donkeycar/data” directory within your Github repository. Ensure you copy all folders to maintain the correct structure, as alterations can lead to errors during the training phase.
Training Your AI Model for Autonomous Navigation
With your data compiled, you can now proceed to train your AI model. This is accomplished using a training script, such as Train.py. Execute the following command in your terminal:
python3 Train.py --model=trained --type=linearUpon completion of the training process, a file named “trained.h5” will be generated. This file encapsulates your trained AI network, the core component enabling autonomous driving capabilities.
Testing Your Trained Self-Driving AI Model
Before deploying your newly trained AI model in full autonomous mode, it’s wise to test its performance in a simulated environment. This precautionary step can prevent potential mishaps during initial real-world testing.
To test your model, modify line 101 in the SegmentationOnly.py script to reflect the name of your AI model file. For example:
driveModel = loadModel('trained.h5')Additionally, on line 167 of SegmentationOnly.py, specify the path to a video file you wish to use for testing.
Now, run the SegmentationOnly.py script using the command:
CUDA_VISIBLE_DEVICES=0 python3 SegmentationOnly.pyThis will display a window showing a segmented area and a red line. The red line visualizes the predictions made by your AI drive model. The angle of the line indicates the intended steering direction, and its length represents the desired speed. Observe the red line’s behavior to assess if the AI is making sensible predictions. Erratic or random movements from the red line may indicate issues with the training data or process.
Troubleshooting and Improving Your Self-Driving Car Program
If your AI model isn’t performing as expected, several factors could be at play. Here are a few troubleshooting steps:
- Increase Training Data: Collect more driving data by driving the vehicle for longer periods and in varied conditions. More data can lead to a more robust and accurate model.
- Review Data Collection Practices: Re-watch the recommended video from the Donkeycar community to ensure you are collecting high-quality data effectively.
- Sensor Recalibration: If you suspect sensor inaccuracies, recalibrate your sensors and gather new data. Remember that data collected before recalibration may not be compatible with data collected after.
- Utilize Pre-trained Models or Datasets: Consider experimenting with pre-existing models or datasets, which may offer a benchmark or a starting point for comparison and further training. These resources are often available in the project’s Github repository.
Conclusion: Taking the Wheel with AI
Programming a self-driving car, especially using behavioral cloning, offers an engaging and practical entry point into the world of AI and autonomous systems. By carefully collecting data, training your AI model, and rigorously testing its performance, you can create a system capable of autonomous navigation. This guide provides a foundational understanding and the initial steps to get you started on your journey to building your own self-driving car. Continue experimenting, refining your data collection techniques, and iterating on your model to further enhance your self-driving car’s capabilities.
