Why do Tesla vehicles sometimes make sudden or close cuts (lane changes) near SUVs or trucks on the freeway at high speeds?
This behavior is likely related to Tesla Autopilot or Full Self-Driving (FSD) system behavior, and it has been observed and discussed by many users. Here’s a breakdown of possible causes:
1. Autopilot/FSD Overcautious or Overaggressive Logic
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Tesla’s system may overreact to large vehicles like trucks or SUVs in adjacent lanes.
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It may "think" those vehicles are drifting too close and initiate a lane change or evasive maneuver.
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Sometimes this results in jerky or seemingly unsafe lane changes, especially at high speeds.
? 2. Camera & Sensor Interpretation Errors
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Tesla relies mostly on cameras (vision-only) after phasing out radar and ultrasonic sensors.
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In certain lighting, angles, or shadows, the system may misjudge the position of nearby trucks/SUVs.
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Large boxy vehicles can cause false positives in lane departure or collision avoidance logic.
3. Blind Spot Behavior & Lane Merging
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When merging or overtaking, Tesla may cut in closer than a human would in front of a truck/SUV if it calculates enough space, but without the courtesy humans usually show.
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Some drivers report that Tesla cars change lanes aggressively to avoid being "boxed in", especially around large vehicles.
4. Software Bugs or Version-Specific Behavior
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Tesla continuously updates FSD/Autopilot via over-the-air (OTA) software updates.
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Certain versions may introduce or worsen behavior like:
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Close cuts in front of large vehicles.
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Inaccurate modeling of adjacent lane behavior.
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Overcompensating for "phantom" vehicle drift.
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? 5. Edge Case: Drafting or Misjudged Gaps
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Sometimes, Tesla may attempt to change lanes behind or in front of a truck to optimize flow (like in adaptive cruise or Navigate on Autopilot).
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If the truck is accelerating or decelerating slightly, this may result in the Tesla cutting in abruptly.
Conclusion:
If you or others are noticing sudden lane cuts near trucks or SUVs, it’s likely an FSD/autopilot behavior issue involving perception, logic, or prediction modeling. Tesla logs all driving data, so if this seems dangerous, it’s often reported and addressed in future updates — but it's still not foolproof today.
