Did Full Self-Driving (Supervised), Tesla's driver assistance feature, play a role in a woman's death? On a Texas evening last week, a 76-year-old grandmother named Martha Avila was standing in the front room of her suburban home when a Tesla Model 3 hurtled into her brick home at a reported speed of over 70 miles per hour, killing her. The car's driver, 44-year-old Michael Butler, later told police that he had Tesla's driver assistance features --which the automaker argues make driving safer and less stressful--engaged during the crash. Butler exhibited "no signs of intoxication," the Harris County Sheriff's Office, which responded to the crash, noted in a report. Now Avila's family is suing not only Butler but also Tesla, alleging that the electric-auto maker's Full Self-Driving (Supervised) driver assistance feature, also called FSD, played a role in her death.

U.S. defense firm Anduril in talks for Nissan plant to build drones in Japan, sources say U.S. defense firm Anduril Industries is in talks to acquire the plant to build military drones in Japan, sources say. U.S. defense firm Anduril Industries is in talks to acquire Nissan Motor's Oppama assembly plant near Tokyo as the maker of autonomous weapons looks to build military drones in Japan, according to three sources familiar with the matter. While they say no decision has been made, any deal could transform one of Japan's first large-scale postwar car factories, long a symbol of its industrial revival, into an arms-making hub. The talks over Oppama, which are being reported for the first time, come as Prime Minister Sanae Takaichi's government seeks to expand defense manufacturing amid growing concern that a Taiwan Strait crisis could draw in Japan and run down weapons stocks. In a time of both misinformation and too much information, quality journalism is more crucial than ever. By subscribing, you can help us get the story right.

Faraday Future would like you to purchase an $89,900 robot . As part of its latest revamp, the embattled electric car company is now pitching a lineup of robots, including humanoids, quadrupeds and a robotic arm. If that name doesn't ring any bells, that could be because the company has been going through a bit of a pivot over the past year in an attempt to salvage its bottom line, if not its reputation. That's become something of a theme for Faraday. The business generated a fair bit of hype years before it showed off its first production-ready electric car at CES 2017.

Authorities investigating an accident that sent two people to the hospital after a Tesla crashed through the front of a Katy, Texas, home. Authorities investigating an accident that sent two people to the hospital after a Tesla crashed through the front of a Katy, Texas, home. The US government has opened a second federal investigation into a recent crash of a Tesla that reportedly had driver-assistance technology engaged, struck a Texas home and killed a resident. Meanwhile, the family of Martha Avila, the 76-year-old resident who was killed, has sued over the wreck . The National Transportation Safety Board (NTSB) said on Wednesday that it was launching an investigation into the 19 June crash that killed Avila in the Houston suburb of Katy.

Musk's company denies that its driving assistance system is to blame. The family of a woman killed after a Tesla, which was operating using an automated driving assistance system according to authorities, crashed into her home is suing both the company and the driver of the vehicle. As reported by, a lawsuit was filed in Harris County District Court by Jennifer and Justin Barbour, the daughter and son-in-law of the 76-year-old victim, Martha Avila. It accuses Tesla of a design defect, and the car's owner, Michael Butler, 44, of negligence. Butler's Tesla Model 3 allegedly collided with Avila's Katy, Texas, home at around 8pm on June 19, at which time she was standing in her front room.

Self-knowledge distillation (SKD) enables single-model training by distilling knowledge from the model's own output, eliminating the need for a separate teacher network required in conventional distillation methods. However, current SKD methods focus mainly on replicating common features in the student model, neglecting the extraction of key features that significantly enhance student learning. Inspired by this, we devise a self-knowledge distillation framework entitled Self-Distillation training via Proximal Gradient Optimization or SDPGO, which utilizes gradient information to identify and assign greater weight to features that significantly impact classification performance, enabling the network to learn the most relevant features during training. Specifically, the proposed framework refines the gradient information into a dynamically changing weighting factor to evaluate the distillation knowledge via the dynamic weight adjustment scheme. Meanwhile, we devise the sequential iterative learning module to dynamically optimize knowledge transfer by leveraging historical predictions and real-time gradients, stabilizing training through mini-batch-based KL divergence refinement while adaptively prioritizing task-critical features for efficient self-distillation. Comprehensive experiments on image classification, object detection, and semantic segmentation demonstrate that our method consistently surpasses recent state-of-the-art knowledge distillation techniques.

Filtering Step Remaining Instances Raw images collected 7.5Mimages After deduplication and car detection 4.9M images Initial car bounding boxes 13.22M boxes After score/size thresholding 6.97M boxes After Qwen2.5-7B

AI systems must adapt to the evolving visual landscape, especially in domains where object appearance shifts over time. While prior work on time-aware vision models has primarily addressed commonsense-level categories, we introduce Car Models in Time (CaMiT).

Crash testing with these massive mammals has come a long way from using real cadavers. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. The moose crash test dummy helping Volvo engineers in Sweden build cars. Breakthroughs, discoveries, and DIY tips sent six days a week. By signing up, you confirm you are 16+, will receive newsletters and promotional content and agree to our Terms of Use and acknowledge the data practices in our Privacy Policy .

RJ Scaringe, the CEO of Rivian Automotive, joined us for a wide-ranging interview about how his company's new electric SUV fits into the current EV industry, and what comes next. RJ Scaringe got his PhD from MIT studying internal combustion engines. Then he founded a company to make them obsolete. In 2009, fresh out of grad school, he launched what would become Rivian. The company spent nearly a decade in stealth mode before arriving at the 2018 LA Auto Show with two electric rides nobody had seen coming. The road, however, hasn't been easy. Rivian lost $3.6 billion in 2025, and has burned through nearly $25 billion in the past eight years. It has spent more money over the same period than almost every other pure EV maker. Rivian's IPO was the largest worldwide in 2021, and one of the largest in US history, within days valuing the company at over $100 billion. Its stock has dropped from a high of $130 to around $16. Since the R1 went on sale in 2021, Rivian has sold 175,000 cars.