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IIHS announces new ratings set for the safeguards of semi-autonomous vehicles

Credit: Andy Slye/YouTube

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The Insurance Institute for Highway Safety (IIHS) has announced that it is developing a new ratings program that evaluates the safeguards that vehicles with partial automation employ to help drivers stay attentive.

The IIHS will use four levels for rating the safeguards: good, acceptable, marginal, or poor. Vehicles with “good” safeguard system ratings will need to ensure that the driver’s eyes are directed at the road and their hands are either on the wheel or ready to grab it at any point. Vehicles with escalating alert systems and appropriate emergency procedures when a driver does not meet those conditions will also be required, the IIHS said.

Expectations for the IIHS are that the first set of ratings will be released in 2022. The precise timing is currently not solidified as supply chain bottlenecks have affected the IIHS’ ability to obtain test vehicles from manufacturers.

IIHS President David Harkey believes a rating system for these “driver monitoring” systems could determine their effectiveness and whether safeguards actually hold drivers accountable. “Partial automation systems may make long drives seem like less of a burden, but there is no evidence that they make driving safer,” Harkey said. ” In fact, the opposite may be the case if systems lack adequate safeguards.”

Self-driving cars are not yet available to consumers, the IIHS reassures in its press release. While some advertising operations or product names could be somewhat misleading, the IIHS admits that some vehicles have partial automation. However, the human driver is still required to handle many routine driving tasks that many of the systems simply cannot perform. The driver always needs to be attentive and monitor the vehicle’s behavior, especially in case of an emergency where the driver needs to take over control of the car. The numerous semi-autonomous or partially automated programs on the market, like Tesla Autopilot, Volvo Pilot Assist, and GM’s Super Cruise, to name a few, all have safeguards in place to help ensure drivers are focused and ready. However, the IIHS says that “none of them meet all the pending IIHS criteria.”

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The previously named partially automated driving systems all use cameras, radar, or other sensors to “see” the road. Systems currently offered on the market combine Adaptive Cruise Control (ACC) and lane centering with other driver assistance features. Automated lane changing is becoming common as well, and is a great example of one of these additional features.

Regardless of how many features a semi-autonomous driving program has, all of them still require the driver to remain attentive and vigilant during operation. This does not mean that all drivers maintain attention, as some may use cheat devices or other loopholes to operate a vehicle with semi-autonomous features in a fully autonomous way. Additionally, the IIHS mentions in its press release that some manufacturers “have oversold the capabilities of their systems, prompting drivers to treat the systems as if they can drive the car on their own.”

RELATED:

Level 2 systems like Tesla Autopilot can improve drivers’ attentiveness: IIHS study

The main issue is the fact that many operators deliberately misuse the systems. IIHS Research Scientist Alexandra Mueller is spearheading the new ratings program, and she says that abuse of the systems is one of many problems with semi-autonomous vehicle features.

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“The way many of these systems operate gives people the impression that they’re capable of doing more than they really are,” Mueller said regarding the features. “But even when drivers understand the limitations of partial automation, their minds can still wander. As humans, it’s harder for us to remain vigilant when we’re watching and waiting for a problem to occur than it is when we’re doing all the driving ourselves.”

There is no way to monitor a driver’s thoughts or their level of focus on driving. However, there are ways to monitor gaze, head and hand position, posture, and other indicators that, when correctly displayed, could be consistent with someone who is actively engaged in driving.

The IIHS’ new ratings program aims to encourage the introduction of safeguards that can help reduce intentional and unintentional misuse. They would not address the functional aspects of some systems and whether they are activating properly, which could also contribute to crashes. It will only judge the systems that monitor human behaviors while driving.

“To earn a good rating, systems should use multiple types of alerts to quickly remind the driver to look at the road and return their hands to the wheel when they’ve looked elsewhere or left the steering unattended for too long. Evidence shows that the more types of alerts a driver receives, the more likely they will notice them and respond. These alerts must begin and escalate quickly. Alerts might include chimes, vibrations, pulsing the brakes, or tugging on the driver’s seat belt. The important thing is that the alerts are delivered through more channels and with greater urgency as time passes,” the IIHS says. Systems that work effectively would perform necessary maneuvers, like bringing the vehicle to a crawl or a stop if drivers that fail to respond to the numerous alerts. If an escalation of this nature occurs, the driver should be locked out of the system or the remainder of the drive, or until the vehicle is turned off and back on.

The rating criteria may also include certain requirements for automated lane changes, ACC, and lane centering. Automated lane changes should be initiated, or at least confirmed, by the driver before they are performed. If a vehicle comes to a complete stop when an ACC system is activated, the system “should not automatically resume if the driver is not looking at the road or the vehicle has been stopped for too long.” Lane centering features should also encourage the driver to share in steering, rather than switching off automatically when the driver adjusts the wheel. This could discourage some drivers from participating in driving, the IIHS said. Systems should also not be used if a seatbelt is unfastened, or when AEB or lane departure prevention is disabled.

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“Nobody knows when we’ll have true self-driving cars, if ever. As automakers add partial automation to more and more vehicles, it’s imperative that they include effective safeguards that help drivers keep their heads in the game,” Harkey said.

I’d love to hear from you! If you have any comments, concerns, or questions, please email me at joey@teslarati.com. You can also reach me on Twitter @KlenderJoey, or if you have news tips, you can email us at tips@teslarati.com.

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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Elon Musk

Tesla’s Elon Musk: 10 billion miles needed for safe Unsupervised FSD

As per the CEO, roughly 10 billion miles of training data are required due to reality’s “super long tail of complexity.” 

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Credit: @BLKMDL3/X

Tesla CEO Elon Musk has provided an updated estimate for the training data needed to achieve truly safe unsupervised Full Self-Driving (FSD). 

As per the CEO, roughly 10 billion miles of training data are required due to reality’s “super long tail of complexity.” 

10 billion miles of training data

Musk comment came as a reply to Apple and Rivian alum Paul Beisel, who posted an analysis on X about the gap between tech demonstrations and real-world products. In his post, Beisel highlighted Tesla’s data-driven lead in autonomy, and he also argued that it would not be easy for rivals to become a legitimate competitor to FSD quickly. 

“The notion that someone can ‘catch up’ to this problem primarily through simulation and limited on-road exposure strikes me as deeply naive. This is not a demo problem. It is a scale, data, and iteration problem— and Tesla is already far, far down that road while others are just getting started,” Beisel wrote. 

Musk responded to Beisel’s post, stating that “Roughly 10 billion miles of training data is needed to achieve safe unsupervised self-driving. Reality has a super long tail of complexity.” This is quite interesting considering that in his Master Plan Part Deux, Elon Musk estimated that worldwide regulatory approval for autonomous driving would require around 6 billion miles. 

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FSD’s total training miles

As 2025 came to a close, Tesla community members observed that FSD was already nearing 7 billion miles driven, with over 2.5 billion miles being from inner city roads. The 7-billion-mile mark was passed just a few days later. This suggests that Tesla is likely the company today with the most training data for its autonomous driving program. 

The difficulties of achieving autonomy were referenced by Elon Musk recently, when he commented on Nvidia’s Alpamayo program. As per Musk, “they will find that it’s easy to get to 99% and then super hard to solve the long tail of the distribution.” These sentiments were echoed by Tesla VP for AI software Ashok Elluswamy, who also noted on X that “the long tail is sooo long, that most people can’t grasp it.”

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Tesla earns top honors at MotorTrend’s SDV Innovator Awards

MotorTrend’s SDV Awards were presented during CES 2026 in Las Vegas.

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Credit: Tesla China

Tesla emerged as one of the most recognized automakers at MotorTrend’s 2026 Software-Defined Vehicle (SDV) Innovator Awards.

As could be seen in a press release from the publication, two key Tesla employees were honored for their work on AI, autonomy, and vehicle software. MotorTrend’s SDV Awards were presented during CES 2026 in Las Vegas.

Tesla leaders and engineers recognized

The fourth annual SDV Innovator Awards celebrate pioneers and experts who are pushing the automotive industry deeper into software-driven development. Among the most notable honorees for this year was Ashok Elluswamy, Tesla’s Vice President of AI Software, who received a Pioneer Award for his role in advancing artificial intelligence and autonomy across the company’s vehicle lineup.

Tesla also secured recognition in the Expert category, with Lawson Fulton, a staff Autopilot machine learning engineer, honored for his contributions to Tesla’s driver-assistance and autonomous systems.

Tesla’s software-first strategy

While automakers like General Motors, Ford, and Rivian also received recognition, Tesla’s multiple awards stood out given the company’s outsized role in popularizing software-defined vehicles over the past decade. From frequent OTA updates to its data-driven approach to autonomy, Tesla has consistently treated vehicles as evolving software platforms rather than static products.

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This has made Tesla’s vehicles very unique in their respective sectors, as they are arguably the only cars that objectively get better over time. This is especially true for vehicles that are loaded with the company’s Full Self-Driving system, which are getting progressively more intelligent and autonomous over time. The majority of Tesla’s updates to its vehicles are free as well, which is very much appreciated by customers worldwide.

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Elon Musk

Judge clears path for Elon Musk’s OpenAI lawsuit to go before a jury

The decision maintains Musk’s claims that OpenAI’s shift toward a for-profit structure violated early assurances made to him as a co-founder.

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Gage Skidmore, CC BY-SA 4.0 , via Wikimedia Commons

A U.S. judge has ruled that Elon Musk’s lawsuit accusing OpenAI of abandoning its founding nonprofit mission can proceed to a jury trial. 

The decision maintains Musk’s claims that OpenAI’s shift toward a for-profit structure violated early assurances made to him as a co-founder. These claims are directly opposed by OpenAI.

Judge says disputed facts warrant a trial

At a hearing in Oakland, U.S. District Judge Yvonne Gonzalez Rogers stated that there was “plenty of evidence” suggesting that OpenAI leaders had promised that the organization’s original nonprofit structure would be maintained. She ruled that those disputed facts should be evaluated by a jury at a trial in March rather than decided by the court at this stage, as noted in a Reuters report.

Musk helped co-found OpenAI in 2015 but left the organization in 2018. In his lawsuit, he argued that he contributed roughly $38 million, or about 60% of OpenAI’s early funding, based on assurances that the company would remain a nonprofit dedicated to the public benefit. He is seeking unspecified monetary damages tied to what he describes as “ill-gotten gains.”

OpenAI, however, has repeatedly rejected Musk’s allegations. The company has stated that Musk’s claims were baseless and part of a pattern of harassment.

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Rivalries and Microsoft ties

The case unfolds against the backdrop of intensifying competition in generative artificial intelligence. Musk now runs xAI, whose Grok chatbot competes directly with OpenAI’s flagship ChatGPT. OpenAI has argued that Musk is a frustrated commercial rival who is simply attempting to slow down a market leader.

The lawsuit also names Microsoft as a defendant, citing its multibillion-dollar partnerships with OpenAI. Microsoft has urged the court to dismiss the claims against it, arguing there is no evidence it aided or abetted any alleged misconduct. Lawyers for OpenAI have also pushed for the case to be thrown out, claiming that Musk failed to show sufficient factual basis for claims such as fraud and breach of contract.

Judge Gonzalez Rogers, however, declined to end the case at this stage, noting that a jury would also need to consider whether Musk filed the lawsuit within the applicable statute of limitations. Still, the dispute between Elon Musk and OpenAI is now headed for a high-profile jury trial in the coming months.

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