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Tesla challenges $137M settlement in racism case, argues $600k is fair

Credit: @TacosandTeslas/Twitter

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Tesla has officially challenged the $137 million settlement in the case of Owen Diaz, a contract worker who won a lawsuit against the electric automaker where he alleged racial discrimination occurred at the company’s Fremont factory in Northern California. Tesla argues that the $137 million sum is “staggering,” and should pay $600,000 to Diaz, based on settlements in other workplace racial harassment cases.

A Tuesday filing from Tesla requested a new trial for the case and also believed that the damages awarded to Diaz in the original case are unreasonable. “The jury award here, a staggering $136.9 million, simply cannot stand,” Tesla said in the filing, originally obtained by Bloomberg. “It is an award without precedent in US anti-discrimination law. It dwarfs awards in similar — and even in the most egregious — cases. And it bears no relationship to the actual evidence at trial.”

(Credit: Tesla)

Tesla is suggesting the case be handled in a manner that is comparable to other workplace harassment cases. This would effectively lower the payout amount considerably, from the $137 million a jury felt Diaz was entitled to, to $600,000: $300,000 for emotional distress and $300,000 for punitive damages.

On the day of the settlement’s finalization by a San Francisco jury, Tesla VP of People Valerie Capers Workman stated that some employees testified that racial slurs were regularly heard in the Fremont factory, regularly used by African-American workers amongst themselves. However, vandalism and graffiti depicting racist symbols were found in the factory’s bathroom. These were removed by custodial staff. Diaz also submitted written complaints to non-Tesla supervisors, as he worked for a third-party contractor. Two contractors were terminated, another was suspended due to Diaz’s requests. He stated later that he was “very satisfied” with the handling of the situation. Diaz also reportedly encouraged family members to work at the plant, and did not complain about racial terms until Tesla denied him a full-time position.

Tesla responds to $137M jury order over alleged racism in Fremont Factory

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Capers Workman did not detail whether Tesla would file an appeal, but the company stated in its 10-Q filing with the SEC in October that it planned to challenge the verdict of the trial. The company wrote:

“On October 4, 2021, in a case captioned Diaz v. Tesla, a jury in the Northern District of California returned a verdict of $136.9 million against Tesla on claims by a former contingent worker that he was subjected to race discrimination while assigned to work at Tesla’s Fremont factory from 2015-2016. The Company does not believe that the facts and law justify the verdict and intends to pursue next steps in post-trial motions and on appeal.”

Tesla will attempt to have its payout amount readjusted considerably.

Don’t hesitate to contact us with tips! Email us at tips@teslarati.com, or you can email me directly at joey@teslarati.com.

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Joey has been a journalist covering electric mobility at TESLARATI since August 2019. In his spare time, Joey is playing golf, watching MMA, or cheering on any of his favorite sports teams, including the Baltimore Ravens and Orioles, Miami Heat, Washington Capitals, and Penn State Nittany Lions. You can get in touch with joey at joey@teslarati.com. He is also on X @KlenderJoey. If you're looking for great Tesla accessories, check out shop.teslarati.com

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Tesla piggybacks recent Supercharger feature with update that takes it further

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Credit: Tesla

Tesla has introduced an enhanced visualization in its Supercharger navigation system, building directly on the Site Maps feature rolled out a few months ago.

This latest software update adds detailed 3D icons that represent specific vehicle models parked at charging stalls, offering drivers a more precise view of site occupancy and layout.

The Site Maps debuted in Tesla’s 2025 Holiday Update, providing 3D overviews of select Supercharger locations with real-time stall availability.

Tesla supplements Holiday Update by sneaking in new Full Self-Driving version

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Drivers could see which spots were open, occupied, or out of service when navigating to supported stations.

Now, the system takes this capability further by rendering accurate representations of Tesla vehicles, including distinctions between models such as the Model 3, Model Y, Model S, Model X, and Cybertruck. These icons appear as lifelike 3D renderings, complete with recognizable shapes and proportions that match the actual cars charging at the site:

This refinement improves the user experience during road trips and daily charging stops. As drivers approach a Supercharger, the navigation display now shows not just generic occupied markers but identifiable vehicle types plugged into each stall.

Blue indicators highlight active charging sessions, while other visual cues denote availability or maintenance status. The feature integrates seamlessly with the existing map interface, allowing quick assessment of the best available spot based on vehicle size and positioning.

Tesla continues to expand the availability of these detailed Site Maps across its global network. Initially piloted at a limited number of locations, the rollout has progressed steadily, with more stations gaining support in recent software versions.

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Owners benefit from better planning, as the system helps identify compatible stalls and reduces uncertainty upon arrival. The update reflects Tesla’s ongoing commitment to refining its navigation and charging ecosystem through iterative software improvements.

In addition to model-specific icons, the enhanced maps maintain all prior functionalities, such as integration with nearby amenities and energy usage predictions. This ensures a comprehensive tool for efficient Supercharging.

As Tesla’s fleet grows and the network scales, such features play a key role in optimizing the overall ownership experience. Future updates may extend similar visualizations to additional sites and incorporate even more data points for drivers.

With this piggyback enhancement, Tesla demonstrates how small but thoughtful additions can elevate an already useful tool, making Supercharger visits smoother and more informed for its customers. The company is expected to broaden the feature’s reach in upcoming releases, further solidifying its leadership in EV charging infrastructure.

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Tesla Full Self-Driving v14.3.3 driver monitoring: We tested it

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Credit: TESLARATI

Tesla Full Self-Driving v14.3.3 driver monitoring was reportedly scaled back in recent releases, but a new version that was released in the early hours of June 3 aimed to do a better job of keeping those in control of their cars honest, according to release notes.

The release notes for FSD v14.3.3, via Software Version 2026.14.6.7 added:

“Improved driver monitoring system sensitivity with better eye gaze tracking, eye wear handling, and higher accuracy in variable lighting conditions.”

However, Tesla said this was already enabled in the first rollout of FSD v14.3.3 in late May. We tested it anyway, especially as the Standard Speed Profile seemed less-than-worried about what you were doing during operation.

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I decided to try out the Hurry and Mad Max Speed Profiles for this test, and it gave me results that I would have expected. Tesla has evidently ramped up driver monitoring based on the Speed Profile you are using to travel.

The more aggressive the Speed Profile, the more on the hook you will be for taking your attention away from the road. Our testing showed that Mad Max was less likely to allow you to do normal things like change music or adjust navigation without getting an on-screen warning or nag from the driver monitoring system.

Hurry Mode Results

On Hurry, the driver monitoring system on FSD v14.3.3, via Software Version 2026.14.6.7, was more restrictive than Standard but less restrictive than Mad Max. I found that I could scroll through music options for a considerable amount of time, more than 30 seconds:

Standard gave me about 80 seconds of phone scrolling with absolutely no nags or warnings in a previous test. It is worth noting that this was a previous branch of v14.3.3, but Standard is such a goodie-two-shoes on the road that it is my impression it would not change much.

Mad Max Results

I spent the majority of the drive on Mad Max to see how it truly reacted to the driver having their attention elsewhere. While I did do a short phone test, I am aiming to steer away from those and use the center screen. I think it is a valid criticism that the phone test is dangerous and, not to mention, illegal in Pennsylvania. Changing the navigation and music is a more reasonable, more responsible, and safer test.

With Mad Max being the fastest and most aggressive Speed Profile, I anticipated this being the quickest mode to give me an alert that I needed to look at the road. That was the case with music:

As well as adjusting Navigation, when I received two nags:

These nags were more than reasonable, and I think it’s probably good that Tesla is ramping up the driver monitoring. I do believe that it should be relatively strict across all of the Speed Profiles, especially with phone use. When using the center screen, the nag intervals should be based on the speed profile you are utilizing at the time.

These driver monitoring adjustments are a great thing to have while FSD is still under its “Supervised” moniker, but I expect Tesla to continue pushing the limits on what it will allow, especially considering CEO Elon Musk has hinted that phone use is capable with the more recent versions.

You can watch the full drive on YouTube below:

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Tesla responds to Robotaxi skeptics with a massive move in Austin

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Credit: @AdanGuajardo/X

Tesla has responded to the skeptics of its Robotaxi program by launching a massive expansion of the unsupervised program in its initial rollout city of Austin.

The company’s geofence, the enabled area of operation for rides, now covers the entire Austin Metropolitan area, an incredible move just days after media headlines attempted to discredit the ride-hailing service.

Those who have access to the Tesla Robotaxi app on their smartphones can now request a ride in any portion of the Austin Metro area. The company confirmed this on the social media platform X:

This is Tesla’s fifth expansion of the geofence, with the others occurring in July, early August, late August, and late October 2025. It has remained at that size since October 26, but Tesla has now more than doubled that size.

It is now covering the entire area, including suburbs like Pflugerville and Manor, as well as I-35 highways, Gigafactory Texas, and the Austin-Bergstrom Airport.

The move comes just days after various media outlets highlighted the small fleet size of Tesla’s Robotaxi fleet in Austin, something that is a reasonable criticism but an understandable move on the company’s part to prioritize safety.

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Tesla expands Robotaxi geofence, but not the garage

Tesla has expanded its Robotaxi geofence many times, but its fleet has remained at a relatively conservative size as the company continues to push safety as its most crucial metric.

The latest expansion is a key indicator of Tesla’s comfort level to expand the ride-hailing service. The move shows Tesla is scaling unsupervised autonomy, as it demonstrates that the company’s Full Self-Driving system has reached sufficient reliability for a broader real-world deployment, which is something the company has worked on extensively.

It also shows Tesla is game for a competition with its rivals in the autonomous ride-hailing sector. Tesla has often matched or exceeded competitors like Waymo in coverage area, despite its smaller fleet. This step highlights Tesla’s iterative, data-driven progress toward a high-margin, app-based Robotaxi network.

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It’s not the absolute largest area expansion ever, but achieving full unsupervised operations across a major metro is a key moment in the Robotaxi story. It shifts the program from limited pilot/testing toward a more mature commercial service, while gathering the miles needed for faster growth.

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