Asking Grok questions about FSD - a fun experiment IMHO

Here’s a chat with Grok 3 on FSD. It started with it asking to search for specific mentions from Tesla for a family use case for elderly adults. Then I moved to this part of the convo.

Prompt: explain coming improvements so that I’m not making any incorrect assumptions.

I’m happy to break down these upcoming improvements in Tesla’s Full Self-Driving (FSD) system in plain language, steering clear of heavy AI jargon so you don’t have to guess what they mean. These changes—some teased in official Tesla channels, others echoed in posts on X and analyst chatter—aim to make FSD smarter, safer, and more practical. Let’s tackle each one step-by-step.

1. 3x Model Size Scaling

• What It Means: Think of the AI behind FSD as a brain. “Model size” refers to how much it can “think” at once—how many details and patterns it can juggle. Scaling it 3x means making that brain three times bigger, not physically, but in terms of its capacity to process info.
• Why It Matters: A bigger model can learn more complex stuff—like spotting a kid darting into the street or figuring out a tricky intersection—because it has more “space” to store and connect driving experiences. It’s like upgrading from a small notebook to a giant whiteboard.
• Real-World Impact: The car might react faster and smarter to unexpected situations, reducing hesitation or errors. But it’ll need more computing power—good thing Tesla’s got Hardware 3 and Dojo in play.

2. 3x Model Context Length Scaling

• What It Means: Context length is how far back the AI can “remember” while driving. Scaling it 3x means it can look at three times more of what just happened—like the last 30 seconds instead of 10—to decide what to do next.
• Why It Matters: Driving isn’t just about the moment; it’s about what led up to it. A longer memory helps the car connect the dots—like knowing a truck ahead has been swerving for a while, so it’s safer to hang back.
• Real-World Impact: Smoother decisions, especially on highways or in traffic, where patterns over time (not just snapshots) matter. It could cut down on jerky moves or last-second reactions.

3. Audio Inputs for Better Handling of Emergency Vehicles

• What It Means: Right now, FSD mostly relies on cameras and radar to “see” the world. Adding audio inputs means it’ll also “listen”—picking up sirens or horns to spot ambulances, fire trucks, or police cars faster.
• Why It Matters: Sirens can be heard before lights are seen, especially around corners or in bad weather. This gives the car a heads-up to pull over or slow down sooner.
• Real-World Impact: Safer roads—fewer close calls with emergency vehicles. It’s a big deal for urban driving where sirens bounce off buildings and visuals alone aren’t enough.

4. Improved Reward Predictions for Navigation

• What It Means: The AI learns by guessing what’s “good” (a reward) or “bad” (a penalty)—like staying on route vs. hitting a curb. Improving these predictions means it gets better at picking the best path, not just a random one that works.
• Why It Matters: Smarter navigation—fewer wrong turns, less weaving, and routes that feel more human-like. It’s about teaching the car what drivers actually want, not just what’s technically possible.
• Real-World Impact: You might notice shorter trips or less backtracking, especially in tricky spots like construction zones or dense cities.

5. Improvements to False Braking and Slower Driving in Parking Lots

• What It Means: False braking is when the car slams the brakes for no good reason—like mistaking a shadow for an obstacle. Slower driving in parking lots means it won’t crawl at a snail’s pace unnecessarily. This tweak fixes both.
• Why It Matters: False braking annoys drivers and can spook passengers (or trailing cars). Overly slow parking lot speeds waste time. Better judgment here makes the car feel more trustworthy.
• Real-World Impact: Smoother parking—less jolting stops and a pace that matches the situation, like zipping through an empty lot but slowing near pedestrians.

6. Support for Destination Options (Pulling Over, Parking in a Spot, Driveway, or Garage)

• What It Means: Right now, FSD might just stop at your destination’s GPS pin—like the middle of a street. This adds options: pull over to the curb, find a parking spot, slip into a driveway, or even enter a garage.
• Why It Matters: It’s end-to-end autonomy—handling the last 100 feet, not just the journey. This is huge for convenience, especially for folks like the elderly you mentioned earlier, who need the car to finish the job.
• Real-World Impact: Imagine summoning your Tesla, hopping in, and it parks itself in your garage—no manual fiddling. It’s a step toward that “shared family vehicle” vision.

7. Efficient Representation of Maps and Navigation Inputs

• What It Means: The car uses digital maps plus real-time data (cameras, sensors) to navigate. “Efficient representation” means combining them better—less clutter, more clarity—so the AI isn’t overwhelmed or confused.
• Why It Matters: Cleaner data = faster, smarter choices. It’s like giving the car a crisp roadmap instead of a scribbled napkin—it’ll know exactly where lanes, signs, and turns are.
• Real-World Impact: Fewer missed exits or lane changes, especially in places where maps are outdated but cameras see the truth (e.g., new roundabouts).

8. Improved Handling of Camera Occlusions

• What It Means: Occlusions are when something blocks the camera—like rain, a dirty lens, or a truck in the way. Improving this means the car can still “see” or guess what’s hidden using other cameras or past data.
• Why It Matters: Tesla’s vision-only system leans hard on cameras. If one’s blocked, it can’t just freeze—this fix keeps it rolling safely.
• Real-World Impact: Better driving in bad weather or tight traffic—less “uh-oh, I’m blind” moments. Think navigating a snowy street or a packed parking lot.

How This Ties to Your Thoughts

You mentioned safety and end-to-end features like parking—spot on. These updates hit those nails: - Safety: Audio for emergency vehicles, better camera handling, and fewer false brakes all reduce risks.
- End-to-End: Destination options and parking lot fixes make FSD a full journey companion—crucial for elderly mobility or anyone wanting hands-off convenience.

Big Picture

These aren’t just tweaks—they’re pushing FSD toward reliability and trust. A bigger, sharper AI brain (model size/context) plus practical upgrades (audio, parking, maps) could make v14 a leap over v12. For your elderly-use case, summon-to-garage functionality with safer, smoother operation is exactly what’s needed. It’s not there yet—consistency’s the hurdle—but this list shows Tesla’s aiming high. What part excites you most for that family vehicle idea?

Then I did a follow up prompt with a real issue I’ve had with FSD 13.2.7.

Let’s walk through this scenario—your Tesla’s Full Self-Driving (FSD) system is facing a left turn with two available turn lanes, followed by an immediate right turn, and it picks the innermost left-turn lane. I’ll break down why this could be a problem, thinking it through step-by-step as if I’m encountering it fresh. This tests my ability to reason about driving dynamics and FSD’s decision-making quirks, so let’s dive in.

The Setup

Picture an intersection: you’re approaching a left turn with two left-turn lanes—let’s call them Lane 1 (outer, closer to the center of the road) and Lane 2 (inner, closest to the left curb). After the left turn, the road you’re entering has multiple lanes, and you need to make an immediate right turn. FSD opts for Lane 2 (the innermost left-turn lane). At first glance, this might seem fine—inner lane for a sharper turn, right? But here’s where it gets messy.

Why It’s a Problem

1. Lane Positioning After the Left Turn

   • When you turn left from the innermost lane (Lane 2), you naturally end up in the leftmost lane of the new road—think of it as the tightest arc. If the road you’re turning onto has, say, two or three lanes, you’ll land in the farthest-left lane post-turn.
     
   • An immediate right turn requires you to be in the rightmost lane of that new road. Starting from the leftmost lane means you’ve got to cross one or more lanes of traffic in a very short distance—sometimes just 50–100 feet—before the right turn. That’s a tight squeeze, especially if traffic’s moving.

2. Traffic Flow and Timing

   • After completing the left turn, other cars from Lane 1 (the outer left-turn lane) will fill the middle or right lanes of the new road. If they’re moving steadily—or worse, accelerating—you’ve got little time or space to merge right. FSD’s hesitation or strict lane discipline could leave it stuck, unable to make the right turn without cutting someone off or stopping awkwardly.

3. FSD’s Conservative Nature

   • FSD tends to play it safe (sometimes overly so). It might not aggressively merge across lanes right after the turn, especially if it detects vehicles nearby. From the innermost lane, it could freeze—either missing the right turn entirely or braking hard to wait for a gap, which feels unnatural and could confuse drivers behind you.

4. Road Geometry and Rules

   • Some intersections have painted lines or signage dictating where each turn lane should go. The innermost lane might be intended for sharper left turns or through traffic, not for an immediate right. Picking Lane 2 could violate those cues, which FSD might misread or ignore if its map data or vision system prioritizes the “shortest path” over logical flow.

5. Human Intuition vs. AI Logic

   • A human driver would likely pick Lane 1 (the outer left-turn lane) because it sets you up in a middle or right lane after the turn—closer to where you need to be for the immediate right. FSD’s choice of Lane 2 suggests it’s optimizing for the left turn alone (tightest radius, less steering) without fully grasping the follow-up maneuver. It’s like solving half the puzzle.

Real-World Example

Imagine a busy suburban intersection:
- You’re turning left onto a three-lane road.
- Lane 2 puts you in Lane A (leftmost) after the turn.
- The right turn is 75 feet ahead, requiring Lane C (rightmost).
- Cars from Lane 1 slide into Lane B or C, filling the gap.
- FSD, now in Lane A, has to cross Lanes B and C in seconds—tough with oncoming traffic or a short gap between signals. It might hesitate, brake, or miss the turn, frustrating you and risking a rear-end collision.

Why FSD Chooses the Innermost Lane

FSD’s logic likely stems from:
- Path Optimization: It picks the tightest turn radius, assuming it’s “efficient” for the left turn, ignoring the right-turn dependency.
- Vision Bias: If cameras see Lane 2 as clearer or less obstructed, it might default there.
- Map Data Gaps: If Tesla’s navigation doesn’t flag the immediate right turn as critical, FSD won’t adjust its lane choice proactively.

The Fix (and Upcoming Improvements)

Your scenario ties to those upcoming FSD upgrades:
- Improved Reward Predictions for Navigation: If FSD learns that Lane 1 gets a “higher reward” (smoother path to the right turn), it could shift its choice.
- Efficient Maps and Navigation Inputs: Better integration of turn-by-turn data might flag the right turn’s proximity, nudging it to Lane 1.
- 3x Context Length: Remembering the right turn’s closeness post-left could override the “tightest turn” bias.

Bottom Line

Choosing the innermost lane (Lane 2) is a problem because it misaligns the car for the immediate right turn, forcing a tricky, time-sensitive lane change—or worse, a missed turn. It’s a classic case of FSD thinking one move ahead instead of two, which humans do instinctively. Your example’s a great test of reasoning—it shows FSD’s still catching up to real-world driving nuance. Does this match what you’ve seen on the road?