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.

Image caption, Elon Musk has repeatedly boasted of Tesla's self-driving capabilities. Jennifer Barbour filed her lawsuit in a local court on Tuesday, just days after her 76-year-old mother Martha Avila died from injuries she sustained after a Tesla Model 3 sped into their shared home . The Tesla driver told police that he was using the car's autonomous or full self-driving technology at the time of the crash. In the lawsuit Barbour accuses Elon Musk's electric vehicle company of defective design and negligence by promoting technology that is unsafe, while Musk on social media denied the technology was to blame. Tesla was approached for comment.

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.

Vehicle aerodynamics optimization has become critical for automotive electrification, where drag reduction directly determines electric vehicle range and energy efficiency. Traditional approaches face an intractable trade-off: computationally expensive Computational Fluid Dynamics (CFD) simulations requiring weeks per design iteration, or simplified models that sacrifice production-grade accuracy. While machine learning offers transformative potential, existing datasets exhibit fundamental limitations--inadequate mesh resolution, missing vehicle components, and validation errors exceeding 5%--preventing deployment in industrial workflows. We present DrivAerStar, comprising 12,000 industrial-grade automotive CFD simulations generated using STAR-CCM+ software. The dataset systematically explores three vehicle configurations through 20 Computer Aided Design (CAD) parameters via Free Form Deformation (FFD) algorithms, including complete engine compartments and cooling systems with realistic internal airflow. DrivAerStar achieves wind tunnel validation accuracy below 1.04%-- a five-fold improvement over existing datasets--through refined mesh strategies with strict wall y` control. Benchmarks demonstrate that models trained on this data achieve production-ready accuracy while reducing computational costs from weeks to minutes. This represents the first dataset bridging academic machine learning research and industrial CFD practice, establishing a new standard for data-driven aerodynamic optimization in automotive development. Beyond automotive applications, DrivAerStardemonstrates a paradigm for integrating highfidelity physics simulations with Artificial Intelligence (AI) across engineering disciplines where computational constraints currently limit innovation.

Multi-Task Learning (MTL) in Neural Combinatorial Optimization (NCO) is a promising approach for training a unified model capable of solving multiple Vehicle Routing Problem (VRP) variants. However, existing Reinforcement Learning (RL)-based multi-task methods can only train light decoder models on small-scale problems, exhibiting limited generalization ability when solving large-scale problems. To overcome this limitation, this work introduces a novel multi-task learning method driven by knowledge distillation (MTL-KD), which enables efficient training of heavy decoder models with strong generalization ability. The proposed MTL-KD method transfers policy knowledge from multiple distinct RL-based single-task models to a single heavy decoder model, facilitating label-free training and effectively improving the model's generalization ability across diverse tasks. In addition, we introduce a flexible inference strategy termed Random Reordering Re-Construction (R3C), which is specifically adapted for diverse VRP tasks and further boosts the performance of the multi-task model. Experimental results on 6 seen and 10 unseen VRP variants with up to 1,000 nodes indicate that our proposed method consistently achieves superior performance on both uniform and real-world benchmarks, demonstrating robust generalization abilities.

While end-to-end adaptive graph learning methods have demonstrated promising results in capturing latent spatiotemporal dependencies, they often suffer from high computational complexity and limited expressive capacity. In this paper, we propose MAGE for efficient spatiotemporal forecasting. We first conduct a theoretical analysis demonstrating that the ReLU activation function employed in existing methods amplifies edgelevel noise during graph topology learning, thereby compromising the fidelity of the learned graph structures. To enhance model expressiveness, we introduce a sparse yet balanced mixture-of-experts strategy, where each expert perceives the unique underlying graph through kernel-based functions and operates with linear complexity relative to the number of nodes. The sparsity mechanism ensures that each node interacts exclusively with compatible experts, while the balancing mechanism promotes uniform activation across all experts, enabling diverse and adaptive graph representations. Furthermore, we theoretically establish that a single graph convolution using the learned graph in MAGE is mathematically equivalent to multiple convolutional steps under conventional graphs. We evaluate MAGE against advanced baselines on multiple real-world spatiotemporal datasets, and MAGE achieves competitive performance while maintaining strong computational efficiency. Our code is available at official repository.

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.

Why it's nearly impossible to build a robot without China Building on the country's electric vehicle industry, Chinese companies are making robot parts at a scale and price point others can't match. Japan led the world in robotics for decades. More than 50 years ago, Japanese researchers captured imaginations with the first robot capable of grasping objects and walking on two legs. In 1984, a team in Japan built one that could read sheet music and play the piano. When Honda unveiled its first humanoid in 2000, it seemed to cement the country's lead.

An AI agent system proposed by researchers in Spain promises to prevent energy theft and damage to EV chargers, as well as the critical energy infrastructure that powers them. The number of electric vehicles on roads around the world continues to grow. The boom in EV adoption has driven the development of accessible, fast, and efficient charging infrastructure. However, this expansion also brings with it new cybersecurity risks that have been not been widely studied, and for which there are still few viable solutions. Cristina Alcaraz, an infrastructure-security researcher at Spain's University of Malaga, explains that the liability of electric-vehicle charging stations is due to the fact that they integrate multiple physical and digital components.

The automaker today is turning on vehicle-to-grid charging for its GM Energy customers. Will people actually use it? Some 250,000 electric vehicles manufactured by General Motors are driving around the US today--right now!--with an oft-secret capability: Their big, powerful batteries can charge other things. Potentially appliances, homes, and now, thanks to a software update pushed by the automaker this week, an electrical grid . Twelve of GM's EVs have this "bidirectional charging" capability, way more than US competitors' battery-electrics.