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Waymo's Flaw: Is Algorithmic Driving Doomed?
22 Dec
Summary
- Waymo's system is algorithmic, not true AI.
- Fixed weights limit Waymo's ability to handle driving nuances.
- End-to-end AI like Tesla's FSD offers better scalability.
The operational logic of Waymo's self-driving technology has come under scrutiny, with key aspects suggesting its reliance on an algorithmic framework rather than genuine artificial intelligence. This system, while capable of assessing various driving plans in real-time, employs fixed weighting factors, limiting its adaptability.
This algorithmic foundation presents a notable challenge for autonomous driving, particularly in navigating the myriad of unpredictable, infrequent scenarios often termed "long-tail" problems. These complex situations require a more flexible and adaptive approach.
In contrast, systems like Tesla's Full Self-Driving (FSD) are built on an end-to-end AI model. This architecture is posited to offer superior scalability and a more robust capability for managing the diverse and unforeseen circumstances encountered on public roads, indicating a potential divergence in development strategies.
