Nissan Recalls Titan XD Diesel Trucks for Fuel Gauge
The recall affects more than 12,000 2016-model year pickup trucks that are at a higher risk for running out of fuel.

Photo courtesy of Nissan.

Photo courtesy of Nissan.
Nissan North America is recalling 12,112 Titan XD diesel pickup trucks in the 2016 model year because the fuel gauge in these vehicles might malfunction, according to the National Highway Traffic Safety Administration.
The trucks at issue were manufactured from Aug. 7, 2015, to Sept. 1, 2016. They may not have had the temporary fuel tank breather tube cap removed during vehicle assembly, and the fuel tank breather tube may not have been connected to the bed rail. As a result, the fuel tank may not have adequate ventilation, possibly causing the fuel gauge and the distance-to-empty meter to both display inaccurately, NHTSA said.
“If the fuel gauge reads incorrectly, the vehicle can run out of fuel without the driver being aware, increasing the risk of a crash,” the federal safety agency warned on its website.
Nissan dealers will inspect the fuel tank breather tube, and then replace the fuel tank, fuel-sending unit, and/or fuel tank breather tube as needed. There will be no charge for this service.
To check whether any of your fleet vehicles are involved, you can click here and search by VIN. Nissan customer service can be reached at (800) 647-7261.
More Safety

Managing Road Risk at Scale: Why Fleet Safety Needs a Data-Driven Framework
Insights from the FIA Road and Driver Safety Indexes reveal how to manage road risk on a larger scale.
Read More →
Stellantis Recalls 1.3 Million Jeep Vehicles Worldwide Over Fire Risk
Stellantis is recalling more than 1.3 million Jeep Wrangler and Gladiator models worldwide over a fire risk linked to power steering pump wiring.
Read More →
Coaching Is Not Training, Even When AI Is Doing It
AI-powered safety platforms can detect risky behaviors and deliver immediate feedback. But effective driver development still requires a foundation of training followed by coaching that reinforces those skills.
Read More →
How Emotions Behind the Wheel Can Affect Fleet Safety
During National Safety Month, fleets are encouraged to look beyond distracted driving and recognize how stress, fatigue, and emotional well-being influence driver performance and crash risk.
Read More →
Nominations Open for 2026 Fleet Safety Award
Nominations have officially opened for the 2026 Fleet Safety Award Winner.
Read More →
Turning Connected Vehicle Data Into Decisions That Matter
Fleet leaders have more data than ever, but turning that data into clear, actionable decisions remains a challenge. This white paper shows how leading organizations are using connected vehicle data to improve safety, reduce costs, and optimize fleet performance. Learn how to turn insight into action across your fleet.
Read More →
Cameras, Safety and Insurance: From Reactive Claims to Real-time Prevention
Commercial auto remains one of the most challenging and costly lines of coverage for fleet operators and insurers alike. Learn more about how to effectively address these issues from Onur Aksan, Enterprise Business Development Executive, Geotab.
Read More →
NAFA Fleet Safety Symposium to Collocate With 2026 Fleet Forward Conference
The daylong certificate program will precede the Fleet Forward Conference at the Gaylord National Harbor in Maryland.
Read More →
The Distractions You Can’t Turn Off: What Drivers Face Outside the Vehicle
Fleet drivers face constant visual, cognitive, and environmental interruptions the moment they hit the road. From roadside chaos to mental fatigue and digital overload, today’s biggest driving risks often come from outside the vehicle itself.
Read More →
FLASH Weather AI Launches First Deep-Learning Hail Prediction Model With High-Resolution Forecasting
FLASH Weather AI has launched a first-of-its-kind hail prediction model capable of forecasting hail size and arrival time at 1-kilometer resolution up to 55 minutes ahead, giving fleets and insurers critical time to prepare for severe storms.
Read More →
