1. The Callstack – An Overview:
Our AI Control operates in a multi-stage environment structured as follows:
• Get Inputs: Collect all relevant input data such as speed, position, and external commands.
• Detect Objects/Signals/Humans: Detection of objects, signals, and people in the vicinity of the train.
• Calculate Train Control Based on Inputs: Calculation of control commands based on the gathered data.
• Send Data to Chatbot: Data is sent to a chatbot that considers the human element.
• Apply Controls: Application of the control commands to the train’s systems.
• Invoke Third Party Controls: Integration of external control systems when necessary.

2. Integration of AI Feedback Loops:
A significant challenge in autonomous train operation is implementing instructions from train dispatchers effectively. Our solution is unique: we not only manually calculate the required actions but also have them reviewed by our AI. This ensures that even unexpected human instructions such as speed adjustments are reliably implemented.

3. Using Data and Formulas:
Precision in autonomous train control requires accurate data and mathematical formulas. We utilize advanced algorithms to:
• Calculate braking distances,
• Adjust acceleration parameters,
• And dynamically manage speed limits.

Example calculations are detailed in the post to illustrate the application of these formulas in real scenarios.

4. The Human Aspect:
Our technology recognizes the indispensability of human intervention in the system. The chatbot in our setup serves to contextualize human commands and ensure they are considered in real-time.

Conclusion:
Our goal at AutoTF is not just to develop technologies that enable trains to drive autonomously but also to ensure that these technologies can be seamlessly integrated into the existing railway system, maintaining the human at the core.

We believe that combining advanced AI with human oversight is the future of safe and efficient rail travel.