NBC News35%
Tesla driver whose car slammed into Texas home overrode driver assistance mode, NTSB says 35%
By Rebecca Cohen53% Austin Mullen53%
7/16/2026, 12:16:16 PM
BS Summary: This article contains 9 faulty reasoning types, including Confirmation Bias, Availability Heuristic, and Hasty Generalization, with Negativity Bias as the most egregious example at 12.4% saturation with 104 hits. Analysis detected 493 faulty-reasoning hits from 838 analyzed words, generating a BS Score of 42.5% and a BS Rank of 35% (14,312 of 21,887 articles). This article is better (less manipulative) than 65.40% of the article peer group.
Weeks after a 76-year-old woman was killed when a Tesla, operating on Full Self-Driving, crashed into her Texas home, the National Transportation Safety Board concluded that the car’s driver overrode the driver assistance mode and accelerated into the building.
Michael Butler, 44, was operating the Tesla Model 3 that killed Martha Avila on June 19.
The vehicle was going at 70 mph when the crash occurred, the agency said.
According to a NTSB report released Wednesday, the car was on a residential road when it “partially entered a driveway, and crashed into a residence.”
The driver had engaged Tesla’s Advanced Driver Assistance System, Full Self-Driving (Supervised) at the time of the crash, the NTSB report said.
But, it continued, “electronic data recovered from the vehicle indicated that before the crash, the driver manually overrode FSD (Supervised) by pressing the accelerator pedal to 100%, and the vehicle’s speed was greater than 70 mph when the crash occurred.”
As a result, the woman inside the home was killed, and the driver sustained minor injuries, the NTSB said.
Butler has since been charged with manslaughter in connection with Avila’s death.
Prosecutors and investigators have not yet said whether the car was at all to blame.
An attorney for Butler did not immediately respond to a request for comment on the findings.
The agency pointed out that the residential road, Rose Hollow Lane, has a speed limit of 30 mph.
Also, at the time of the crash, “the weather was clear, the roadway was dry, and daylight conditions were present,” the NTSB report said.
The NTSB findings echo what Ashok Elluswamy, vice president of AI software at Tesla, said days after the crash, defending the vehicle’s systems.
“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,” Elluswamy wrote on X on June 22.
Tesla and its CEO, Elon Musk, did not immediately respond to an emailed request for comment on Thursday.
But Philip Koopman, a professor emeritus at Carnegie Mellon University who has been working on self-driving car safety for 30 years, told NBC News that the findings from the NTSB report might not tell the whole story.
He said the NTSB must also consider whether “computer or electronics malfunction caused a false accelerator pedal reading.”
“As with any allegation of unintended acceleration by a driver, a thorough investigation must consider the potential for human driver error, but also mechanical failure, electronics failure, and software failure,” he said.
According to the arrest affidavit, Butler told police he did not feel sick at the time of the crash and did not have any alcohol or drugs in his system.
He said he was making a DoorDash delivery when he changed the music on the Tesla’s touchscreen and eventually “passed out,” according to the court documents.
No further information was available on Butler’s condition at the time of the crash.
Koopman pointed out that the “100% pedal position” at the time of the crash could be due to a number of other factors, including the driver’s foot, but also a foreign object, a mechanical pedal malfunction, a wiring harness failure, a computer hardware or software malfunction, among other factors or a combination of factors.
“To the degree that Tesla makes relevant information available, NTSB should also be looking for potential common cause failures in otherwise redundant systems,” Koopman suggested.
He added that he hopes the final NTSB report “will make clear what potential causes other than human driver error could not be ruled out because insufficient information was available.”
Brett Schreiber, who represented the families of two victims in another Tesla crash, said the driver’s behavior reported by the NTSB “is not ordinary... on a low-speed residential street.”
“Tesla has faced years of allegations from drivers who reported their vehicles suddenly accelerating without warning, only to be told the recorded data blamed them,” Schreiber explained.
“But electronic data recorders only log the values the vehicle’s own computer generated.”
The NTSB said the crash remains under investigation while it determines probable cause.
The National Highway Traffic Safety Administration has also opened a special crash investigation into the crash.
NBC News previously reported that those involved in other similar Tesla crashes — some of which resulted in deaths — have taken issue with the way the company led by Musk advertises Full Self-Driving, noting that the technology requires more human oversight than the name suggests.
“The more driving tasks automation assumes, the more important it becomes to support the driver’s attention, understanding, and ability to intervene when the system reaches its limits,” Bryan Reimer, an MIT researcher and adviser on the future of mobility and AI, said Thursday after reviewing the NTSB report.
He added: “Names such as Full Self-Driving can communicate a level of capability that the system does not actually provide.
Clearer naming is a basic part of helping drivers understand their continuing responsibility.”
Speakers
4speakers41%attributed speech497writer words
Voice mapSelect a segment to jump to its words
Selected voice
100%flagged-word coverageBrett Schreiber
69 attributed words20% of attributed speech50% writer coverage
Attribution is sentence-level. Pattern percentages are calculated only from words assigned to that voice.
Loading…
Loading…
Loading…
Analysis
Hover over highlighted words in the article to view the associated bias or fallacy analysis.