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Oshkosh launches EV and other tech for specialty vehicles with safety, quiet and cleanliness in mind

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More At CES 2025 for the first time ever, Oshkosh launched a number of specialty vehicles with electric power, safety, quiet and cleanliness in mind. It’s an example of the trend of non-tech companies showing up with […]

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At CES 2025 for the first time ever, Oshkosh launched a number of specialty vehicles with electric power, safety, quiet and cleanliness in mind.

It’s an example of the trend of non-tech companies showing up with technology that makes products better but doesn’t take center stage. In this case, the 107-year-old company is bringing us EV postal service trucks, collision avoidance for first responders, and garbage pickup robots.

Oshkosh announced its Collison Avoidance Mitigation System designed specifically to keep first responders and service workers safe while operating in high-risk roadside environments. The Oshkosh, Wisconsin-based company also launched AI and electrified solutions that improve refuse and recycling collection in communities and an innovative, autonomous robot designed for on-demand refuse collection that reduces weekly chores.

“Technology is only as valuable as the positive impacts it has on our lives and our communities,” said John Pfeifer, Oshkosh CEO, in a statement. “Oshkosh has been working closely with our customers to find solutions to the toughest challenges for some of the hardest jobs in the world – from protecting firefighters to reducing pollution to tackling weekly chores. Our latest solutions use AI, autonomous, connected, and electrification technology to transform our neighborhoods and put technology to work for all of us to make our world safer, cleaner, quieter, and more sustainable.”

Pfeifer said in a press briefing that the company leverages tech born on race tracks with its PrattMiller division, and then it takes them into areas like reducing risks for those working on the road with fire trucks or two trucks.

Pfeifer said the company is working on tech for the airport of the future, the job site of the future and the neighborhood of the future.

Smarter airports, postal trucks and garbage trucks

An Oshkosh smart jet bridge.

In airports, anyone can see inefficiencies in airports like sitting around airports because of long lines or delays. AI-driven docks, cargo handling and airplane parking tools can minimize human error or delays in getting planes and luggage to where they need to be.

iOPS tech can digitally connect the entire gate operation for an aircraft and can synchronize everything, reducing aircraft turnaround time by two minutes for each flight, based on a test with one airline with 200 planes. The cumulative savings in time, efficiency and cost becomes significant, Pfeifer said.

To make job sites safer, Oshkosh is making autonomous vehicles and similar hubs that can manage vehicles so they’re ready when needed. Oshkosh makes purpose-built, route-based vehicles like postal service trucks.

Electric cars have problems with unpredictable driving distances and scarce EV chargers. But the fleets that the postal service uses have predictable routes and can be tailored to maximize the use of the EV infrastructure. Oshkosh is delivering 165,000 vehicles to the USPS, and 70% of them are zero-emission EV trucks, Pfeifer said.

Reducing roadside risks for first responders

The CAMS system can protect first responders.

Oshkosh noted that first responders and service workers, such as tow truck drivers, take incredible risks every time they respond to a call that involves an active roadway. More than 250 first responders have died from being struck by a vehicle (mostly driven by distracted drivers) on the job in the last four years.

To improve the safety of first responders and service workers, Oshkosh developed the Collison Avoidance Mitigation System (CAMS) – the first system designed specifically for roadside emergency scenarios. CAMS accurately detects the trajectory, speed, and proximity of oncoming vehicles relative to a parked response vehicle by using advanced camera technology with AI, advanced autonomous driving sensors, and radar, Iyenger said. This tech came from the PrattMiller car racing business.

CAMS provides two to three seconds of advance notice of an impending collision, significantly improving the safety of everyday heroes during roadside operations, Iyenger said. The camera system also records a continuous video feed that supports accident reconstruction and insurance documentation, adding another layer of operational safety.

Whether responding to accidents, performing roadside assistance, or managing snow removal, CAMS is an essential tool that enhances situational awareness.

Improving neighborhoods with better refuse and recycling collection

This Volterra electric garbage truck is quieter and it uses AI to pick up bins.

Oshkosh also introduced three market-leading technologies that complement its McNeilus Volterra ZSL – a first-of-its-kind integrated, electric garbage truck for refuse and recycling collection in North America. Most refuse trucks are outdated in terms of modern safety, comfort and noise pollution principles, said Jay Iyenger, CTO of Oshkosh, said in a briefing. But the new truck can do things like detect a bin and reach out accurately with a robotic arm to pick it up on the first try. Normally, a driver has to stop and use a joystick to pick up a bin through trial and error.

Iyenger said Oshkosh is working on ways to make electrificiation, autonomy, and AI technologies to make gargage collection and recyling more efficient. There are problems like cross-contamination of recyclables and refuse. It is also designed for operator comfort and safety. It also has a new McNeilus Volterra ZFL front-loading truck to complement its side-loading truck.

The new technologies include:

  • AI-enabled bin detection: This will allow for safer and smarter refuse pickup. Using AI and machine learning to enable semi-autonomous bin handling, collection vehicles will detect refuse bins autonomously. With the push of a button, the electric arm will deploy to collect the bin with high precision. The streamlined process reduces human error, accelerates cycle times, and is expected to decrease training time, Iyenger said. It also enhances safety since the arm will not deploy if anything other than a bin is detected.
  • Electrified arm: Oshkosh is replacing the traditional hydraulic arm on refuse and recycling collection vehicles with a new electric version. The electrified arm operates faster than hydraulic arms, saving up to 50 minutes to 60 minutes per day and enabling collection companies to service more households, Iyenger said. The electrified arm also operates more quietly and helps control maintenance costs compared to its hydraulic counterpart, Iyenger said.
  • AI-powered contamination detection for recyclables: Customers say contamination in the recycling stream causes extra work and cost. According to the EPA, up to 25% of materials placed in recycling bins do not belong there. Oshkosh developed an advanced vision system using AI to overcome this challenge and identify contaminated materials. This includes on-the-edge processing that helps to divert the maximum amount of clean recyclables from landfills for a more sustainable future. It can also reduce thermal events and enhance safety when flammable garden waste or batteries end up in refuse containers.

Eliminating weekly chores for consumers

HARR-E is a garbage robot that can detect your refuse level and come pick it up from your house.
HARR-E is a garbage robot that can detect your refuse level and come pick it up from your house when it’s full.

Oshkosh is also pioneering a new way to tackle a required weekly chore – taking out the trash and recycling. At CES, it is showcasing a Hail-able Autonomous Refuse Robot – Electric (HARR-E). HARR-E is an innovative, autonomous robot designed for on-demand garbage collection. It provides a smarter, cleaner, and safer solution than traditional door-to-door waste collection methods.

HARR-E allows residents to request a trash pick-up service using a smartphone app or a virtual assistant smart speaker. The robot will then make its way from a central refuse collection area within the neighborhood. It will navigate to the resident’s home autonomously using a known map of the area and perception sensors, Iyenger said.

Upon reaching the resident’s home, HARR-E automatically opens its lid, and residents can place their trash inside. An internal scale weighs the deposited garbage, which monitors the payload. This gives apartment complexes the option to charge a collection fee by weight.

After taking the refuse, HARR-E closes its lid and returns to the central neighborhood collection area to drop off its load and recharge for the next pick-up, Iyenger said. HARR-E is designed to benefit all residents while also helping with accessibility for people with disabilities and aging individuals who find typical curbside refuse collection challenging.

Additionally, HARR-E is expected to reduce waste management costs by 25% by eliminating labor expenses and minimizing vehicle maintenance. It can improve the cleanliness of homes and neighborhoods by reducing the need to store waste in bins waiting for pick up.

Attendees can check out Oshkosh’s latest innovations and more at booth#5616 in the West Hall
of the Las Vegas Convention Center (LVCC).

Separately, Oshkosh leaders will participate in sessions at CES Tech Talks at CES 2025, with insights into they are deploying electrification, AI, autonomy and connectivity technology to transform their businesses, redefine customer experiences, increase productivity, boost safety and sustainability, and enhance employee engagement.

Oshkosh has more than 18,000 employees and it was founded in 1917. It has $9.7 billion in annual revenue.

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US EIA forecasts declining oil prices as supply disruptions ease

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Eni lets EPCI contract for Kutei North Hub field FPSO

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Building the AI Optical Layer: Connectivity, Standards, and the Future of AI Infrastructure

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Data Center Jobs: Engineering, Construction, Commissioning, Sales, Field Service and Facility Tech Jobs Available in Major Data Center Hotspots

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OpenAI’s red teaming innovations define new essentials for security leaders in the AI era

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More OpenAI has taken a more aggressive approach to red teaming than its AI competitors, demonstrating its security teams’ advanced capabilities in two areas: multi-step reinforcement and external red teaming. OpenAI recently released two papers that set a new competitive standard for improving the quality, reliability and safety of AI models in these two techniques and more. The first paper, “OpenAI’s Approach to External Red Teaming for AI Models and Systems,” reports that specialized teams outside the company have proven effective in uncovering vulnerabilities that might otherwise have made it into a released model because in-house testing techniques may have missed them. In the second paper, “Diverse and Effective Red Teaming with Auto-Generated Rewards and Multi-Step Reinforcement Learning,” OpenAI introduces an automated framework that relies on iterative reinforcement learning to generate a broad spectrum of novel, wide-ranging attacks. Going all-in on red teaming pays practical, competitive dividends It’s encouraging to see competitive intensity in red teaming growing among AI companies. When Anthropic released its AI red team guidelines in June of last year, it joined AI providers including Google, Microsoft, Nvidia, OpenAI, and even the U.S.’s National Institute of Standards and Technology (NIST), which all had released red teaming frameworks. Investing heavily in red teaming yields tangible benefits for security leaders in any organization. OpenAI’s paper on external red teaming provides a detailed analysis of how the company strives to create specialized external teams that include cybersecurity and subject matter experts. The goal is to see if knowledgeable external teams can defeat models’ security perimeters and find gaps in their security, biases and controls that prompt-based testing couldn’t find. What makes OpenAI’s recent papers noteworthy is how well they define using human-in-the-middle

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