on the road

Texas roads will soon see self-driving semi trucks between Houston and Dallas

Kodiak Robotics unveiled its driverless semi-truck technology this month, which is expected to hit Texas roads later this year. Photo via Kodiak

Kodiak Robotics is scaling up its driverless semi truck, which will initially carry cargo on a Houston-to-Dallas route that’s set to formally launch this year.

The most recent version of Kodiak’s truck debuted in Las Vegas at the recent 2024 Consumer Electronics Show (CES). Mountain View, California-based Kodiak Robotics says the truck is equipped with safety-critical software and hardware (including braking, steering and sensors).

Kodiak’s sixth-generation truck builds on the company’s five years of real-world testing, which includes carrying 5,000 loads over more than 2.5 million miles.

“We’re the first and only company to have developed a feature-complete driverless semitruck with the level of automotive-grade safety redundancy necessary to deploy on public roads,” Don Burnette, founder and CEO of Kodiak, says in a news release.

“Over the course of 2.5 million miles, we’ve successfully demonstrated that our self-driving trucks can withstand the harsh environment of long-haul trucking from both a platform integrity and a software perspective,” he adds. “This truck fundamentally demonstrates that we’ve done the work necessary to safely handle driverless operations.”

Among the highlights of the sixth-generation truck are:

  • A pneumatic braking system controlled by Kodiak’s proprietary software.
  • A redundant steering system.
  • A proprietary safety computer.
  • A redundant power system.
  • Proprietary SensorPods for housing sensors.
  • Microphones designed to detect the presence of the sirens of emergency vehicles and other suspicious sounds.
  • An advanced communication system.

Founded in 2018, Kodiak has been delivering freight in Texas since mid-2019, including a Houston-to-Dallas route. Kodiak announced in 2022 that it had teamed up with Swedish retailer IKEA to pilot autonomous freight deliveries in Texas between the IKEA warehouse in Baytown and the IKEA store in Frisco.

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This article originally ran on InnovationMap.

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A View From HETI

Researchers have secured $3.3 million in funding to develop an AI-powered subsurface sensing system aimed at improving the safety and efficiency of underground power line installation. Photo via Getty Images

Researchers from the University of Houston — along with a Hawaiian company — have received $3.3 million in funding to explore artificial intelligence-backed subsurface sensing system for safe and efficient underground power line installation.

Houston's power lines are above ground, but studies show underground power is more reliable. Installing underground power lines is costly and disruptive, but the U.S. Department of Energy, in an effort to find a solution, has put $34 million into its new GOPHURRS program, which stands for Grid Overhaul with Proactive, High-speed Undergrounding for Reliability, Resilience, and Security. The funding has been distributed across 12 projects in 11 states.

“Modernizing our nation’s power grid is essential to building a clean energy future that lowers energy costs for working Americans and strengthens our national security,” U.S. Secretary of Energy Jennifer M. Granholm says in a DOE press release.

UH and Hawaii-based Oceanit are behind one of the funded projects, entitled “Artificial Intelligence and Unmanned Aerial Vehicle Real-Time Advanced Look-Ahead Subsurface Sensor.”

The researchers are looking a developing a subsurface sensing system for underground power line installation, potentially using machine learning, electromagnetic resistivity well logging, and drone technology to predict and sense obstacles to installation.

Jiefu Chen, associate professor of electrical and computer engineering at UH, is a key collaborator on the project, focused on electromagnetic antennas installed on UAV and HDD drilling string. He's working with Yueqin Huang, assistant professor of information science technology, who leads the geophysical signal processing and Xuqing Wu, associate professor of computer information systems, responsible for integrating machine learning.

“Advanced subsurface sensing and characterization technologies are essential for the undergrounding of power lines,” says Chen in the release. “This initiative can enhance the grid's resilience against natural hazards such as wildfires and hurricanes.”

“If proven successful, our proposed look-ahead subsurface sensing system could significantly reduce the costs of horizontal directional drilling for installing underground utilities,” Chen continues. “Promoting HDD offers environmental advantages over traditional trenching methods and enhances the power grid’s resilience.”

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