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The Monthly Newsletter of IEEE Vehicular Technology Society—December 2025

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Connected and Automated Vehicles
Robotrucking
Katrin Sjoberg

The self-driving tech company Aurora has developed a virtual driver for heavy-duty vehicles in the United States to work with any truck manufacturer, addressing the anticipated (and already identified) problem with driver shortage. As of 1 May, Aurora felt confident enough about its safety case to remove its safety drivers from two autonomous Paccar trucks equipped with the Aurora Driver operating commercially carrying goods for customers between Dallas and Houston.

But two weeks later, Paccar got cold feet and wanted the safety driver back behind the steering wheel to be ready to intervene if necessary. Equipping traditional truck manufacturers’ products with self-driving technology from another company is challenging from several perspectives.

For truck manufacturers that have been around for a century, much is at stake when launching driverless products to be operated on public roads. The brand image can be destroyed in the blink of an eye, and the trust that has been built up over decades in society and customers can disappear overnight if something devastating happens.

The road testing of self-driving trucks has been spared from major incidents, but trucks are much bigger than cars and will cause much more damage.

Full Article: IEEE Vehicular Technology Magazine, Volume 20, Number 3, September 2025

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In This Issue
Message from the EiC
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Message from the VP—Publications
VTS Publications Shine in IEEE Review
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Call for Nominations
IEEE VTS Distinguished Lecturer Program
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VTS Industry Engagement Webinar
Electric Rope Shovels
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Chapter News—France
EMR International Summer School 2025
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Chapter News—India
Skills Development Program on EV Systems Design
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Connected and Automated Vehicles
Robotrucking
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Transportation Systems
AI Machine Vision
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Mobile Radio
ETSI Report on ISAC Use Cases for 6G
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From the IEEE Open Journal of Vehicular Technology
Controlling Autonomous UAVs
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From the IEEE Transactions on Vehicular Technology
Vehicular Channel Estimation with Liquid Neural Networks
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Editor-in-Chief

F. Richard Yu

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