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esla‘s Full Self-Driving (FSD) Beta Version 9 promises a leap forward in autonomy. In this candid on-road evaluation, Sandy Munro joins Chris—also known as Dirty Tesla—for a hands-on demonstration. The result? A grounded, firsthand look at what Tesla’s Beta 9 is getting right, where it’s improving, and what still needs attention.
First Impressions: Model Y and the New FSD Interface
Right off the bat, Sandy notes the visual difference between the Model Y and his own Model 3. The Model Y’s display renders the FSD Beta 9 environment with clearer, richer detail—showing lane confidence through line darkness and surrounding objects with real-time adaptability.
Sandy, a longtime critic and advocate of lean design, praises the Model Y’s build, particularly its rear casting structure. He reaffirms it’s the only electric vehicle he has openly recommended, thanks to its advanced engineering and value for performance.
Testing Autonomy: Parking Lots, Curves, and Signals
Chris initiates the test by letting FSD Beta 9 drive in a parking lot—a space Tesla has not officially optimized the system for. Surprisingly, the car navigates with poise. While still rough around the edges, the software confidently tracks its path, reflecting ongoing improvements in Tesla’s vision-based system (notably, the company has phased out radar in favor of camera-only perception).
The vehicle handles tight turns, including more-than-90-degree curves and off-banked corners, with minimal intervention. These complex maneuvers demonstrate Tesla’s commitment to refining real-world driving behavior, especially in suburban settings where traditional Autopilot systems struggle.
Lane Changes, Left Turns, and Curb Awareness
FSD Beta 9 handles protected and unprotected left turns better than previous versions, though it still occasionally requires nudging from the driver. Chris shares that his “interventions per mile” metric has improved, indicating tangible software progress.
One recurring issue is premature or undesired lane changes—particularly to the right-hand “slow” lane. Sandy expresses frustration that, despite disabling it on his Model 3, Beta 9 still favors this behavior. It’s a user-experience issue that Tesla should prioritize correcting.
Sandy also highlights an improvement in how the system negotiates curbs. The vehicle now adjusts its nose outward before completing turns, enhancing cornering safety and spatial awareness.
Smart Summon and Speech Navigation
Chris touches on Smart Summon functionality but admits it’s still limited—slow and less practical in busy environments. However, voice navigation proves useful. A few attempts were needed to recognize the correct address, but once set, the system responded well—mapping routes and executing turns without hesitation.
The inclusion of features like auto wipers (which Sandy appreciates from his own Model 3) adds convenience, while voice commands begin to offer a glimpse of a more intuitive driving future.
The Challenge of Complex Environments
While highways are a breeze for FSD Beta 9, inner-city areas remain the system’s Achilles’ heel. Downtown environments, with frequent stops, tight corners, and unpredictable pedestrian behavior, still cause the software to falter. Chris notes it’s usable but not fully trustworthy in those settings—an important distinction for would-be adopters.
Tesla emphasizes that users must maintain attention and keep hands on the wheel. Chris confirms this is wise, as the car can still make quick, unexpected moves—especially when avoiding cyclists or navigating construction zones.
Real-Time Detection and Reality Mapping
A critical point discussed is object recognition. While the FSD Beta can recognize cones and basic lane markers, it struggles with elements like stanchions and wildlife. Chris shares an incident where the car stopped for deer but did not display them on-screen. Elon Musk later acknowledged this limitation, suggesting that future updates aim to mirror real-world visuals more accurately within the display.
This aligns with Tesla’s long-term vision of full situational awareness—offering not just safety but driver confidence in how the system “sees” the world.
Engineering Implications: What Beta 9 Reveals About Tesla’s Roadmap
From a systems engineering perspective, FSD Beta 9 provides a clear window into Tesla’s design philosophy. By abandoning radar and relying purely on camera vision, Tesla has committed to a software-first approach that demands massive training data, real-time processing power, and confidence in neural network adaptation. This vision-only path sets Tesla apart from other OEMs that continue to pursue hybrid or LiDAR-supported autonomy.
The visualization interface—now more detailed and intuitive—also suggests an increased focus on driver transparency. Darker lines for confident path planning, object recognition displays, and traffic signal responsiveness contribute to situational awareness not just for the car, but also for the driver acting as backup.
Another notable takeaway is how Tesla is leveraging OTA (Over-the-Air) updates to iterate quickly. Chris’s comment about tracking “interventions per mile” over time highlights the data-centric feedback loop that fuels Tesla’s improvement cycle. Each software release refines the behavior, not the hardware—saving on cost and accelerating time-to-market.
That said, the car’s struggle with edge cases—especially inconsistent lane markings, poorly lit environments, and complex urban geometry—suggests that physical infrastructure still matters. As Sandy noted, future road systems may require embedded visual aids or even low-cost camera infrastructure to fully support autonomous navigation.
Final Thoughts: Keep Watching the Road Ahead
Tesla’s Full Self-Driving Beta Version 9 is an engineering milestone. It shows promising advancements in suburban navigation, complex curve handling, and perceptual accuracy. But full autonomy remains just out of reach—especially in unpredictable urban environments.
For automotive engineers, this represents a fascinating case study in real-world AI performance and the iterative nature of lean design. And for investors and EV enthusiasts, it signals progress toward the future Tesla envisions. STEM students and tech analysts, too, have a goldmine of insights into edge-case scenarios, computer vision, and driver-AI integration.
Munro’s live, unscripted review of Tesla’s FSD Beta 9 brings clarity to a topic often clouded by hype or skepticism. The system is evolving—and with it, the conversation about what it means to trust technology with your life behind the wheel.
As Chris puts it: “It’s exciting to see it get better.” But as Sandy cautions: “There’s still work to be done.”
Stay tuned for more teardown insights and EV technology breakdowns from Munro & Associates. Want to dive deeper into Tesla’s innovation journey? Check out our previous teardowns to stay ahead of the curve.