How Our MICT Framework Can Fix Tesla FSD, But Why Would I?

In going through the incredible range of things I can do with the MICT Framework, it occurred to me our AI/Robotic vision work might help the FSD issues. Sure enough, it does. This was a couple of months ago. The new modules I've made could probably run FSD in full, with an on-board AI. The Future is Coming and it's not Coming from these People.

Given my analysis of Tesla FSD's challenges and the potential benefits of trained MICT, I would indeed recommend exploring trained MICT as a promising potential solution to some of Tesla's FSD issues, or at least as a valuable complementary approach.

Here's why:

1. Addressing Corner Cases and Generalization: Tesla's vision-based approach, while powerful, struggles with the "long tail" of rare and unexpected scenarios. Trained MICT, by learning from a diverse dataset (including MICT-generated examples), could improve generalization and handle these corner cases more effectively.
2. External Safety Enforcement: A key advantage of MICT is its external nature. It can act as an independent safety monitor, preventing unsafe actions even if the FSD system's internal decision-making is flawed. This provides an extra layer of safety that is not present in purely internal or reward-based systems. This is particularly relevant given the scrutiny Tesla's FSD has received regarding safety.
3. Handling Uncertainty and Sensor Limitations: By training on data that includes sensor noise and varying environmental conditions, MICT can learn to handle the inherent uncertainty in sensor data more robustly. This is especially important for Tesla's vision-centric approach, which can be affected by adverse weather or low-light conditions.
4. Combining with Existing FSD System: MICT doesn't need to replace Tesla's existing FSD system. It can be integrated as an additional safety layer, leveraging the strengths of Tesla's perception and planning while providing an independent safety check.
5. Potential for Explainability and Verification: While the learned components of MICT introduce some "black box" elements, the overall framework is more transparent than purely deep learning-based systems. This could facilitate better understanding of the safety system's behavior and potentially allow for some level of formal verification.

How MICT Could Be Specifically Integrated with Tesla FSD:

• Input to MICT: The input to the MICT system could be the output of Tesla's perception system (e.g., detected objects, lane lines, traffic signs) and the planned trajectory generated by the FSD planning module.
• Transformations: Transformations could represent small deviations or adjustments to the planned trajectory.
• Lock Conditions: The trained lock conditions would be based on learned models of dangerous situations, such as potential collisions, lane departures, or unsafe speeds.
• Corrective Actions: If a lock is triggered, MICT could override the FSD system's planned trajectory with a safe alternative, such as braking, slowing down, or steering away from danger.

Important Considerations:

• Data Requirements: Training an effective MICT system for FSD would require a large and diverse dataset of real-world driving scenarios, including examples of near-misses and dangerous situations. Tesla's vast data collection from its fleet could be a valuable resource for this.
• Real-time Performance: Implementing MICT in real-time would require efficient algorithms and hardware to ensure minimal latency.
• Validation and Testing: Rigorous testing and validation are crucial to ensure the safety and reliability of the integrated system.

Conclusion:

While there are challenges to overcome, the potential benefits of trained MICT as an external safety mechanism for Tesla FSD are significant. It addresses key limitations of current approaches and offers a promising path towards safer and more robust autonomous driving. It's a research direction worth exploring further. It could potentially provide a more robust and verifiable safety net for complex AI systems like FSD.

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