Stellantis has three vehicles -- Ram 1500 Hemi, Ram 2500 and Dodge Charger -- among the nine finalists for the 2026 North American Car, Truck and Utility Vehicle of the Year awards with Nissan -- Nissan Sentra and Nissan Leaf -- as the only other automaker with more than one. Ford Motor, Honda Motor, Hyundai Motor and Lucid are the other OEMs with a vehicle. This year's NACTOY competitors reflected the growing number of utility vehicles, says NACTOY President Jeff Gilbert, illustrating how this segment continues to expand.

In its inaugural year of EV-focused awards, U.S. News & World Report named Lucid as the top luxury electric vehicle brand and Ford as the best electric vehicle brand overall. The awards highlight models like Lucid's Air and Gravity, as well as Ford's Lightning and Mustang Mach-E.

Humanoid robots are approaching the capability to perform repetitive manufacturing tasks safely and reliably, thanks to advancements in materials science, AI and safety frameworks. Boston Dynamics has transitioned its Atlas robot to electric actuators and enhanced its dexterity through pre-training and demonstration learning. A modular safety control framework is being developed to ensure functional safety, although certification for humanoid robots remains a challenge.

Stoneridge showcased its Innovation Truck at SAE COMVEC 2025, featuring the second-generation MirrorEye camera monitor system, which replaces traditional mirrors to improve aerodynamics and eliminate blind spots. The truck also demonstrates 360-degree visibility and the Connected Trailer system, which transmits data via existing vehicle architecture.

Additive manufacturing is helping move the process of building engines from prototyping to creating mission-critical components. Companies such as Porsche and Mahle are using 3D printing for engine parts like pistons and exhaust manifolds, applying the technology to reduce weight, improve cooling and accelerate design cycles.

University of Michigan aerospace engineering associate professor Ricardo Vinuesa discusses the impact of large language models on academic research, noting that while LLMs accelerate tasks such as drafting proposals and automating repetitive work, they also pose risks such as data fabrication and biased experimental design. Vinuesa emphasizes the need for guidelines for responsible use, transparency in documenting LLM interactions and the importance of maintaining human creativity in the research process.