Elon Musk's company is quietly becoming even more important to the future of electric cars in America. When the stainless-steel pickup hit the road in 2023, Tesla was the world's biggest electric-car company. At the time, Elon Musk predicted that he'd eventually sell 250,000 Cybertrucks a year. In the first half of 2026, according to one estimate, Tesla sold roughly 7,000. Tesla has faced several self-inflicted wounds.

Is the All-New Range Rover GT Stepping on Jaguar's Tail? It's "the most car-like Range Rover ever created," but will this all-electric grand tourer spoil Jaguar's Type 01 party? In what looks to be a radical departure from its existing lineup, Range Rover has confirmed an entirely new fifth vehicle: the Range Rover GT, an all-electric grand tourer built on the company's Electrified Modular Architecture (EMA), the same platform that will underpin other midsized cars for the brand. Range Rover has only shared camouflaged prototype pictures for now, as the GT is supposedly still undergoing final testing, ahead of a full launch expected later this year. That means details on this completely new model are scant to say the least: no price, no range, and no power figures have been disclosed.

Tesla Says It's Building a Wheelchair-Accessible Robotaxi A Tesla representative at a DC hearing said the vehicle is "an active product being built." The Tesla Cybercab is not wheelchair accessible, but the company says it's actively developing a wheelchair-accessible autonomous vehicle. Tesla is building a wheelchair-accessible autonomous vehicle, a Tesla representative told lawmakers in Washington, DC, on Monday. "We are in development for a purpose-built, wheelchair-accessible autonomous vehicle," Tesla senior policy advisor India Herdman told members of the DC City Council on Monday, during a hearing focused on a controversial bill that could allow robotaxi services to operate in the District. "We know that paratransit can be very difficult, and people who are confined to wheelchairs permanently should still be able to move around freely, so that is an active product being built by Tesla in Texas," she said.

When you purchase through links in our articles, we may earn a small commission. Acer's 7-in-1 wireless charging station is $50, its best price all year Get Acer's 7-in-1 wireless charging station for 38% off, its lowest price this year. You can get Acer's 7-in-1 wireless charging station for a mere $49.59, which is a smooth 38% off its MSRP. This thing will look great on your desk or your nightstand and make charging all your gear so much easier. It's built with Apple users in mind, but it does enough extra to feel like more than just another charging pad.

Tesla vehicles and super chargers are shown at a Tesla dealership in Buena Park, California, on 28 January 2026. Tesla vehicles and super chargers are shown at a Tesla dealership in Buena Park, California, on 28 January 2026. Strong figures suggest Tesla's auto business is regaining momentum after two straight annual sales declines Tesla blew past Wall Street estimates for second-quarter deliveries on Thursday, posting a record for the period as recovering demand in Europe outweighed persistent weakness in North America. The strong figures suggest Tesla's mainstay auto business is regaining momentum after two straight annual sales declines, providing the spending cushion needed to power its ambitions in autonomous driving and artificial intelligence - the main drivers of the company's roughly $1.6tn valuation. Tesla expects to spend more than $25bn on capital expenditure in 2026, nearly triple the $8.5bn last year, to expand AI infrastructure, battery production, Cybercab manufacturing and Optimus robots.

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.