Tesla has filed a new patent for “Parallel Processing System Runtime State Reload,” comprising of a system of three or more processors working in conjunction to effectively eliminate the possibility of hardware failure during the use of Autopilot or Full Self-Driving. The patent outlines a robust system of parallel processors that can operate in the event that one of them fails or experiences a runtime state error. “Should one of the parallel processors fail, at least one other processor would be available to continue performing autonomous driving functions,” the patent shows.
The patent was filed and published on August 26th and comes just a week after the company’s Artificial Intelligence Day event that was held last Thursday. Outlining a system of at least three processors operating in parallel, it is monitored by circuitry and can locate and identify if one of the three parallel-operating processors is having a runtime state error. The circuitry will then identify a second processor to switch to in the event of a runtime error, access the runtime state of the second processor, and load the runtime state of the second, operational processor into the first processor, which is experiencing a runtime error.
(Credit: Tesla)
Tesla describes the patent in detail:
“A system on a Chip (SoC) includes a plurality of processing systems arranged on a single integrated circuit. Each of these separate processing systems typically performs a corresponding set of processing functions. The separate processing systems typically interconnect via one or more communication bus structures that include an N-bit wide data bus (N, an integer greater than one). Some SoCs are deployed within systems that require high availability, e.g., financial processing systems, autonomous driving systems, medical processing systems, and air traffic control systems, among others. These parallel processing systems typically operate upon the same input data and include substantially identical processing components, e.g., pipeline structure, so that each of the parallel processing systems, when correctly operating, produces substantially the same output. Thus, should one of the parallel processors fail, at least one other processor would be available to continue performing autonomous driving functions.”
Technically speaking, the autonomous vehicle needs only one processor to function as described in an accurate fashion. However, these processors can be overloaded with data when loading into the Neural Network and could experience short-term and non-permanent operational errors. When this occurs, the system would then switch to one of the other processors for normal operation, with at least two backup processors in this patent, as it repeatedly mentions a series of three.
Tesla details its self-driving Supercomputer that will bring in the Dojo era
The second processor would then activate and load the runtime state into the first processor to make the primary processor chip operational once again:
“Thus, in order to overcome the above-described shortcomings, among other shortcomings, a parallel processing system of an embodiment of the present disclosure includes at least three processors operating in parallel, state monitoring circuitry, and state reload circuitry. The state monitoring circuitry couples to the at least three parallel processors and is configured to monitor runtime states of the at least three parallel processors and identify a first processor of the at least three parallel processors having at least one runtime state error. The state reload circuitry couples to the at least three parallel processors and is configured to select a second processor of the at least three parallel processors for state reload, access a runtime state of the second processor, and load the runtime state of the second processor into the first processor.”
The purpose of this patent is to continue system availability, even when the primary processor is experiencing functionality issues due to overuse. The two additional processors essentially act as “backup” and can determine whether autonomous driving systems are meant to be enabled if the first processor experiences an error. “With one particular example of this aspect, the parallel processing system supports autonomous driving and the respective sub-systems of the at least three parallel processors are safety sub-systems that determine whether autonomous driving is to be enabled.”
FIG. 13 is a timing diagram illustrating clocks of the circuits of FIGS. 8 and 10 according to one or more other described embodiments. As shown, the runtime state (data1) of first processor/first sub-system is determined to have at least one error. In response to this determination by the state monitoring/state reload circuitry, the signal st_reload1 is asserted to initiate the loading of runtime state (data2) from second processor/second sub-system into the first processor/first sub-system. With the embodiment of FIG. 13, a first clock (clk1) is used for the first processor/first sub-system and a second clock (clk1) is used for the second processor/second sub-system. There exists a positive skew between the first clock (clk1) and the second clock (clk2), resulting in a late cycle of the loading of the runtime state (data2) of the second processor/second sub-system into the first processor/sub-system, potentially resulting in errors in the runtime state reload process. (Credit: U.S. Patent Office)
It also appears that this patent aligns with Tesla CEO Elon Musk’s previous description of the Dojo self-driving Supercomputer, which was detailed at AI Day. To increase the accuracy and encourage the parallel operation of the processors, the system will utilize a clock input to calibrate the two processors, increasing the accuracy of the system.
Tesla has focused on accurate FSD operation and has revised its strategy on several occasions. After moving to a camera-only approach earlier this year for the Model 3 and Model Y, the company is experiencing more accurate FSD operation through the harmonized processing of its eight exterior cameras. The operation of internal processors, which are responsible for compiling, compressing, and sending data to the Neural Network, can fail temporarily, so the presence of backup processors to continue comprehending self-driving data is a positive idea.
The full patent is available below:
Tesla Patent Parallel Processing System Runtime State Reload by Joey Klender on Scribd
Elon Musk flew under the radar with his recent purchase of a $1 billion energy company, according to Federal Trade Commission (FTC) documents.
Transaction number 202612350 listed Tesla and SpaceX frontman Elon Musk as the acquiring party and CF APR Super Holdings LLC as the seller, with New APR Energy, LLC as the acquired entity. The deal, which closed without public announcement, came to light on May 14.
BREAKING: Elon Musk acquires Jacksonville power company APR Energy in a deal valued at more than $1,000,000,000.00.
— Polymarket Money (@PolymarketMoney) July 15, 2026
Analysts inferred the deal’s scale from minority stakeholder disclosures, including one report of a 5 percent interest sold for approximately $50.4 million. Fortress Investment Group had purchased APR’s assets in late 2024, rebranded the operation as New APR Energy, and subsequently transferred ownership to Musk.
APR Energy specializes in rapidly deployable power infrastructure. The company maintains one of the world’s largest fleets of mobile gas and diesel turbines, with more than 1.1 gigawatts of generation capacity. Its modular units, which are often trailer-mounted, enable turnkey installations ranging from 20 MW to over 500 MW.
APR provides full engineering, procurement, construction, operation, and maintenance services for behind-the-meter power plants, serving everything from data centers, utilities, and industrial clients.
The firm has expanded aggressively to meet surging demand, recently adding turbines and deploying over 100 MW for a major AI hyperscaler. Its solutions bridge critical gaps where grid interconnections face delays of two to five years, according to Yahoo.
The acquisition means something more for Musk. As he continues to expand projects in artificial intelligence, especially xAI, his AI venture, there is a greater need to supply energy-intensive supercomputing clusters, including the Colossus project, with what they need: reliable and high-capacity power.
Ownership of APR provides immediate access to flexible generation assets that can be deployed adjacent to data centers, reducing dependence on a strained infrastructure. It also complements Tesla’s energy storage business, so Musk will be able to pull from his own entities to address the rapid scaling demands of AI training and compute.
Tesla had a petition protesting a recall to fix a potential issue with 2017-2023 Model Y and Model 3 vehicles’ headlights was denied, as the National Highway Traffic Safety Administration (NHTSA) disagreed with the company’s opinion of things.
The recall covers approximately 19,917 Model Y and Model 3 vehicles built from 2017 to 2023. Tesla initially submitted a noncompliance report for the headlights on these vehicles on March 15, 2024. Tesla then petitioned for an exemption from the fix, which violated FMVSS No. 108 (40 CFR 571.108), arguing that the “noncompliance is inconsequential as it relates to motor vehicle safety.
🚨 Tesla was denied a petition by the NHTSA to avoid a recall of 19,900 2017-2023 Model 3 and Model Y vehicles.
The NHTSA found that the vehicles’ headlights may exceed maximum lighting levels. Tesla argued it was inconsequential and did not require a recall. pic.twitter.com/m8Jmm1teLL
— TESLARATI (@Teslarati) July 16, 2026
The NHTSA disagreed, stating that Tesla’s conclusion that the headlights do not increase any risk was not an opinion it shared. The agency said it disagreed with Tesla’s assumption that glare is not increased to surrounding traffic. This issue could be highlighted even more in certain weather conditions.
Tesla will be required to remedy the issue, the NHTSA ruled:
“In consideration of the foregoing, NHTSA has decided that Tesla has not met its burden of persuasion that the subject FMVSS No. 108 noncompliance is inconsequential to motor vehicle safety. Accordingly, Tesla’s petition is hereby denied, and Tesla is consequently obligated to provide notification of and free remedy for that noncompliance under 49 U.S.C. 30118 and 30120.”
The issue here appears to be the angle of the headlights and the brightness they emit during operation. The NHTSA report states that:
“Tesla’s headlamp supplier, Marelli Automotive Lighting, tested 25 right-hand and 25 left-hand lamps, and for this sample, found the maximum photometric intensity measured in the 10°U to 90°U and 90°L to 90°R zone was between 136.2 cd and 230.1 cd for the right-hand lamps and between 117.5 cd and 160.3 cd for the left-hand lamps. According to Tesla, these tests revealed that the photometric intensity of the right-hand and left-hand headlamp lower beam on the subject vehicles may measure as much as 230.1 cd in the 10°U to 90°U and 90°L to 90°R zone, exceeding the maximum photometric intensity by 105.1 cd. Additionally, Tesla states that a left-hand lamp tested by a Transport Canada recognized laboratory measured a maximum of 171.27 cd in the 10°U to 90°U and 90°L to 90°R zone. Despite these measurements exceeding the allowed photometric maximum of 125 cd, Tesla believes that the subject noncompliance is inconsequential to motor vehicle safety.”
Tesla also argued at some points that the headlights had not been deemed responsible for any complaints, accidents, or injuries related to the noncompliance.
Lifestyle
NTSB findings on fatal Tesla crash tell a very different story
The NTSB confirmed the driver, not Tesla’s FSD, caused the fatal Texas house crash.
The National Transportation Safety Board released preliminary findings Wednesday confirming that a Tesla driver, not the vehicle’s software, caused a fatal crash in Katy, Texas in June. The driver, 44-year-old Michael Butler, had engaged Full Self-Driving Supervised mode on Rose Hollow Lane, a residential street with a 30 mph speed limit, before manually overriding the system by pressing the accelerator pedal all the way to 100%. Data recovered from the 2025 Tesla Model 3 showed the vehicle was traveling over 70 miles per hour when it struck a home and killed 76-year-old Martha Avila, who was inside. Weather was clear, the road was dry, and it was daylight.
Texas man charged in fatal Tesla crash where he blamed Autopilot
Butler told authorities he had passed out at the wheel. But security camera footage obtained by the NTSB told a different story, and showed the car accelerating through an intersection before leaving the road entirely. Police also found that Butler’s phone had Google searches including the terms “Tesla FSD not aggressive enough 2026” and “Tesla FSD too timid,” raising serious questions about how he was using the system before the crash. Butler has since been charged with manslaughter. The victim’s family has filed a lawsuit against both Butler and Tesla, alleging negligence.
The NTSB findings aligned directly with what Tesla VP of AI Software Ashok Elluswamy had already stated publicly on X in the weeks after the crash, writing that “the driver manually overrode self-driving by pressing the accelerator all the way to 100%.” The data confirmed his account.
Yup. In this case, the driver manually overrode self-driving by pressing the accelerator all the way to 100% of the accel pedal in this residential area. They reached a speed of 73 mph during the crash, and had the accelerator pressed even after the crash.
— Ashok Elluswamy (@aelluswamy) June 22, 2026
Elon Musk secretly acquires $1B energy company to power the AI future
Tesla has to fix a big problem with its old headlights, NHTSA says
