Autonomous Driving

Today's Stories

1. 1

   Apple skips M6 chip, accelerates M7 with Neural Engine upgrades for 2027

   Apple is skipping the Pro, Max, and Ultra versions of its upcoming M6 chip and instead accelerating development of the M7, which should arrive in the first half of 2027 with significant Neural Engine upgrades. The M7 Ultra is expected to be the basis for a new server product from Apple as well, with support for up to 1.5TB of RAM. Apple's powerful on-device AI processing capabilities stem from the Neural Engine, which originated in the company's failed self-driving car program. By concentrating on Neural Engine improvements rather than broad M6 iterations, Apple is doubling down on hardware as a cornerstone of its AI strategy going forward, differentiating itself through on-device processing that supports privacy by reducing cloud data transmission.

   The M7 is scheduled to arrive in the first half of 2027 with significant Neural Engine upgrades. The M7 Ultra will support up to 1.5TB of RAM for the new server product.

2. 2

   South Korean startup Mobilint pushes NPUs for robots, drones, autonomous vehicles

   Mobilint, a South Korean AI semiconductor startup, is building neural processing units (NPUs)—specialized chips for edge devices—to power physical AI applications like robots, autonomous vehicles, and drones, as AI work shifts from cloud computing to on-device processing. Physical AI (AI embedded in machines that interact with the physical world) represents a new frontier beyond cloud-based language models; edge processing means faster response times and lower latency, which are critical for real-time autonomous systems. For businesses deploying robots or autonomous vehicles, this shift may reduce their dependence on cloud infrastructure and improve performance.

   Mobilint CEO Shin Dong-joo is urging South Korea to accelerate development in this area, suggesting the company sees government support as key to competing in the emerging physical AI chip market.

3. 3

   Waymo's new robotaxi stuck in free-ride limbo by California regulator delay

   Waymo's application to the California Public Utilities Commission to expand its service area and add its new Ojai vehicles to its fleet remains pending. The company cannot yet charge passengers for rides in the Ojai, a pale blue Chinese-made car that started picking up riders last month, while it continues to charge for rides in its Jaguar I-PACE robotaxis. Unlike other states that allow robotaxis to launch with minimal oversight, California requires approval from both the Department of Motor Vehicles and the Public Utilities Commission before companies can carry paying passengers. This regulatory requirement is delaying Waymo's expansion into Northern California (from Sea Ranch and Sacramento through Berkeley, Oakland, and San Jose) and Southern California (from Los Angeles into Thousand Oaks, Santa Clarita, and down to the Tijuana border past San Diego).

   Waymo's Ojai rides could remain free until the end of September and beyond if the regulatory delay persists, creating an unusual situation where one vehicle type in the fleet is subsidized while another generates revenue.

4. 4

   U.S. deploys 100+ autonomous vehicles in Ukraine combat

   Forterra, a U.S. autonomous vehicle builder, has deployed more than 100 of its self-driving ATVs in Ukrainian conflict zones over the past nine months—what it claims is the largest deployment of autonomous ground vehicles in combat by any U.S. defense tech company. The Lancer vehicles, based on Polaris ATVs and equipped with custom sensors and computing hardware, have completed more than 1,100 missions, driven over 2,500 miles, carried 777,440 pounds of cargo, and evacuated 52 casualties. Ukrainian forces face constant aerial drone threats that make movement extremely dangerous, creating demand for remote-operated and autonomous ground vehicles to transport supplies, munitions, and wounded soldiers. Forterra's vehicles can carry 750 kilograms of cargo and run on gasoline, outperforming Ukraine's existing battery-powered vehicles, which carry only up to 250 kilograms. The real-world combat experience is teaching Forterra and competitors how autonomous systems must evolve to handle military conditions—lessons that will shape future U.S. defense contracts.

   Ukrainian soldiers currently teleoperates the vehicles rather than relying on full autonomy, because autonomous systems cannot yet identify and react to unexpected enemy threats in real time. Forterra has raised more than $500 million（約800億円） in venture funding and faces competition from Scout AI (which raised $100 million（約160億円） earlier this year), Field AI, and Overland AI, all developing autonomous platforms for the military.

5. 5

   World models emerge as key to self-driving autonomy, says South Korea researcher

   An automotive technology researcher in South Korea has identified world models—AI systems that can reason through unknown driving scenarios—as the critical advantage in autonomous vehicle development, beyond end-to-end self-driving technology alone. As global competition in self-driving technology intensifies, understanding which technical approaches unlock genuine physical AI autonomy will shape which companies and regions lead the market. For businesses building autonomous systems, this signals that scenario reasoning may be more decisive than current architectural priorities.

   The assertion that world models enable reasoning through unknown scenarios suggests this capability will likely become a focal point for competitive advantage in autonomous driving development.

6. 6

   Oak Ridge Lab reveals workforce building autonomous research facilities

   Oak Ridge National Laboratory has highlighted the Facilities and Operations (F&O) workers who build and maintain the infrastructure supporting its autonomous laboratories. These facilities run with minimal human intervention, relying instead on robotics, sensors, and automation. The report shows that behind every autonomous research system sits a largely invisible team of people doing essential physical and technical work. This underscores that AI-powered labs do not eliminate the need for skilled workers — they shift the work from direct experimentation to infrastructure building and upkeep.

   The story surfaces a broader labor dynamic in scientific automation: as labs adopt autonomous systems, the demand for specialized F&O expertise becomes a bottleneck rather than a cost savings. Organizations planning similar deployments may need to invest in hiring and training such teams.