UNB researcher explores autonomous truck technologies to tackle driver shortage in forestry

From Tim Jaques, UNB newsroom, and the Springboard Content Lab

A University of New Brunswick engineering professor is tackling a shortage of truck drivers in the forestry sector by researching human-led convoys of autonomous vehicles.

 Dr. Yukun Lu, director of the Intelligent Mobility and Robotics Lab (IMRL), is working on a system in which a human-driven truck leads autonomous log-carrying vehicles. The autonomous vehicles mimic the movements of the lead truck and make decisions based on road conditions, vehicle load and environmental hazards.

This system is called a “truck platooning system,” which is designed to make logging transportation safer, more efficient and sustainable in New Brunswick’s back woods. The system uses drones which feed information to vehicles in the convoy. There are also sensors and controllers which allow the driverless trucks to navigate through the woods.

Technology addressing shortage of truck drivers in forestry sector

When Dr. Yukun Lu came to UNB, she brought with her a background in autonomous vehicle control and a clear goal: to apply her expertise to a the skilled truck driver shortage in New Brunswick’s forestry sector.

A platooning system in transportation is a technology that enables a convoy of vehicles to travel closely together in a co-ordinated manner using automated driving systems.

“The human driver is still the boss and retains full control of the system,”

– Dr. Yukun Lu, assistant engineering professor, UNB

She said fully autonomous vehicles are not yet advanced enough to deal with the challenges that would arise on forest roads, which are often remote, rough and hazardous, especially in winter and spring.

Autonomous vehicles guided by sensors

Each autonomous follower vehicle is equipped with sensors, such as LiDAR (Light Detection and Ranging) and cameras, to detect potential obstacles on the road. If a hazard is detected, the follower vehicles can stop independently, even if the lead truck continues. Once the hazard clears, they resume and catch up.

Lu’s lab is developing adaptive algorithms that adjust the following distance between vehicles based on terrain, weather and load. For example, on icy roads or steep hills, the system automatically increases the distance to reduce risk.

The vehicles can switch between different dynamic control systems depending on whether they are empty or fully loaded, using technologies such as electronic stability control, torque vectoring and differential braking.

Communication between vehicles is key. GPS is used as a secondary tool to support perception and navigation systems, even when visibility is poor.

In areas where GPS or data reception is unreliable, the system uses direct vehicle-to-vehicle communication and advanced perception (the cameras and LiDAR) to keep all vehicles co-ordinated and moving safely. More work is planned to help set up stable communications in challenging environments.

System addresses risky human behaviour

Lu’s research also addresses a less obvious challenge: human behaviour. Her team has found that copying the lead driver’s action in all circumstances can be risky if the driver is fatigued, stressed or distracted. The system must monitor the driver’s behaviour and adjust accordingly. If the driver’s conduct is deemed unsafe, the vehicles following may increase their distance or reduce speed.

“Directly copying the human driver’s maneuvers could actually introduce some risk. Our system needs to be smart enough to adapt to human uncertainties.”

– Dr. Yukun Lu

The lab is testing the system using scaled-down electric vehicles equipped with sensors and onboard computers. These tabletop-sized models allow the lab to run experiments safely in controlled environments.

The next step is to integrate drone technology for aerial monitoring, which would help in situations where the terrain blocks the line of sight between vehicles. Drones could also support mapping and co-ordination tasks, making the system more robust.

Lu sees the potential for this technology to transform forestry logistics.

A single driver could lead multiple trucks, each carrying a full load, reducing the need for additional drivers and addressing labour shortages. The system could also be adapted for other industrial sectors, such as agriculture or oil transport.

Lu is hoping to find partnerships with forestry companies, logistics operators and government agencies to move the research closer to real-world applications. Industry feedback and policy support are essential to bridge the gap between lab testing and commercial deployment.

“We’re trying to make people trust human-in-the-loop autonomous driving technology and to see how far this approach can take us toward full autonomy.”

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