Tesla appears close to addressing one of the most persistent complaints from owners of vehicles equipped with matrix LED headlights, the abrupt, jarring dimming that occurs when the system detects reflective surfaces like road signs. An internal software flag discovered in upcoming firmware suggests the automaker is developing a two-stage fix for the problem, which could smooth out a behavior that has frustrated drivers on nighttime roads for months.
A Code Flag Hints at a Targeted Fix
Firmware analysis of an upcoming Tesla software release revealed an internal feature label called “matrix_two_stage_reflection_dip,” according to Carscoops reporting on the discovery. The name itself is telling. Rather than the current binary behavior, where matrix headlights either dim sharply or hold full brightness when they detect a reflective surface, a two-stage approach would likely introduce a smoother, less abrupt response. That kind of transition could reduce the sudden flicker effect that drivers describe as distracting, especially at highway speeds when reflective road signs appear and disappear in quick succession.
Tesla has not publicly confirmed the feature or provided a rollout timeline. The flag exists in code, not in any release notes available to owners. But the specificity of the label, referencing both the matrix headlight system and the reflection-triggered dimming behavior, strongly suggests this is a targeted response to the exact complaint drivers have raised. If it ships, it would represent the first direct software remedy for the reflection dip issue since Tesla began enabling matrix headlight functionality through over-the-air updates, a pattern that has already turned dormant hardware into active features across its lineup.
How Tesla Built Its Adaptive Lighting System
Tesla’s matrix headlights did not arrive fully formed. The company has layered in capabilities through successive software releases, turning hardware that shipped in vehicles into an increasingly active lighting system. The 2024.20 update, for example, enabled adaptive headlights that follow curves and extend illumination range on highways and motorways. Owners could toggle the feature on through the vehicle’s user interface, reinforcing Tesla’s broader strategy of deploying physical components first and unlocking functionality later via software.
That incremental rollout created a side effect. Each new capability introduced new edge cases, and the reflection dip turned out to be one of the most noticeable. The matrix system uses individually controllable LED segments to shape its beam pattern, selectively dimming zones to avoid blinding oncoming drivers. But the same logic that dims for an approaching car’s headlights also fires when the system reads a highly reflective road sign, producing a brief but sharp drop in illumination that catches drivers off guard. The two-stage flag in the new firmware suggests Tesla’s engineers are now trying to teach the system to distinguish between a surface that needs a full dim and one that only warrants a gentle reduction, ideally without sacrificing the core safety goal of avoiding glare.
Tesla’s Track Record on Lighting Compliance
This would not be the first time Tesla has recalibrated its lighting output through a software push. The company issued a voluntary recall affecting more than 63,000 Cybertrucks after coverage in outlets cataloged by Google News highlighted that the front parking lights were too bright, creating a risk of distraction and potential collision. That recall, listed by NHTSA under Recall ID 25V700, addressed front parking lamp output that exceeded the photometric intensity limits set by Federal Motor Vehicle Safety Standard No. 108, the federal rule that governs how bright and how precisely aimed vehicle lighting can be on U.S. roads.
Tesla’s remedy was an over-the-air software update, identified as version 2025.38.3, which adjusted the lamp intensity without requiring a trip to a service center. The Cybertruck episode illustrates two things about Tesla’s relationship with vehicle lighting. First, the company can and does fine-tune photometric output remotely, which means the technical infrastructure for a matrix headlight fix already exists. Second, Tesla has had to react to regulatory scrutiny around light levels before, and FMVSS 108’s detailed candela limits leave little room for experimentation that strays outside the rulebook. Any update to the matrix headlight behavior will need to stay within those boundaries while also solving the user experience problem of distracting dips.
The Regulatory Framework Behind Adaptive Beams
The legal foundation for the kind of adaptive lighting Tesla uses traces back to a final rule from the National Highway Traffic Safety Administration that amended FMVSS No. 108 to allow adaptive driving beam headlights on new vehicles sold in the United States. In announcing the change, NHTSA said adaptive beams “will improve visibility for drivers, pedestrians, and cyclists by providing more illumination on the road without the glare associated with fixed high beams.” The agency framed the technology as a way to modernize decades-old lighting rules to match what LED arrays and real-time sensing can now do.
The U.S. Department of Transportation formally codified the change in the Federal Register, with the final rule text spelling out performance requirements for glare control, beam distribution, and automatic dimming behavior. That framework gives Tesla and other manufacturers the green light to move beyond simple high-beam and low-beam switching, but it also demands that an adaptive system prove it can dim effectively for oncoming traffic and pedestrians. The tension between “more light where you need it” and “less light where it could blind someone” is exactly where the reflection dip bug lives. Tesla’s matrix system appears to be erring too far on the side of caution when it encounters reflective surfaces, treating a road sign the same way it treats an oncoming vehicle’s headlights, and a two-stage approach could let the system apply a lighter initial dim for signs while reserving a full cutoff for genuine glare hazards.
What This Means for Nighttime Driving
Most coverage of Tesla’s matrix headlights focuses on the technology itself, but the real question is whether incremental updates like the two-stage reflection dip will change how confident drivers feel at night. Owners who have experienced the current behavior describe a disconcerting strobe-like effect as the beam repeatedly cuts and restores full power when passing a series of reflective signs or lane markers. On dark rural roads, that momentary loss of illumination can feel worse than a conventional low-beam setup, undermining the promise that adaptive systems will always deliver more useful light without extra effort from the driver.
If Tesla successfully rolls out a smoother dimming curve, the improvement may be felt more than it is seen. A two-stage response would likely keep the overall road scene brighter while still trimming just enough intensity to satisfy glare limits, reducing the sense of the headlights “blinking” in response to every reflective object. Because Tesla can deploy these changes over the air, any refinement can reach a large fleet quickly, and future iterations could further tune how the system differentiates between static signs, moving vehicles, and other light sources. As with the Cybertruck recall, the combination of regulatory pressure and real-world feedback is pushing Tesla’s lighting software to evolve, and the matrix reflection fix appears to be the next test of how well the company can balance compliance, comfort, and the promise of smarter headlights.
For observers tracking how software-defined vehicles change over time, that evolution is increasingly visible across multiple reports and analyses aggregated through platforms such as Google News feeds, where incremental firmware discoveries now hint at future driving dynamics. In that context, a single code flag like “matrix_two_stage_reflection_dip” is more than a line in a changelog: it is an early signal of how automakers are using software to fine-tune safety-critical systems long after a vehicle leaves the factory, and of how quickly driver feedback can be translated into tangible changes on the road.
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*This article was researched with the help of AI, with human editors creating the final content.
