By Clifford Winston, Barron’s

Congestion and delays at ports and on highways are raising fears of a disrupted holiday shopping season. It may be too late to rescue this year’s Christmas stockings, but there is a way to reduce congestion delays significantly and prevent future breakdowns of the supply chain. Policymakers should see the supply-chain crisis as a reason to prioritize autonomous transportation across the economy.

Congestion delays begin to accumulate when raw materials and finished goods  arrive by ship a port and wait to be unloaded. Container vessels were recently waiting seven to 12 days at the key U.S. ports in Los Angeles and Long Beach, and up to six days in Rotterdam. Shipping cargo by truck to industrial warehouses increases delays because the shortage of truck drivers prevents cargo from being loaded quickly onto trucks and because congested highways, especially in urban areas, slow deliveries. Additional delays occur because congestion slows trucks that deliver finished goods to stores, and vans that deliver products to consumers. Congestion that increases the operating costs of ships and trucks is borne by consumers in higher product prices.

Autonomous transportation modes can reduce labor-related and congestion delays that disrupt the supply chain. Autonomous ships operate smoothly in crowded shipping lanes and close to shore. Autonomous cranes and straddle carriers that load and unload containers reduce maritime accidents and a vessel’s berthing and unberthing time at a port. Autonomous ships can operate 24/7 at all ports because they require far less labor on land to assist with their operations.

The first autonomous container ship, the Yara Birkeland, is scheduled to be tested on a journey between two Norwegian ports at the end of the year. Policymakers can help expedite the adoption of autonomous ships by setting congestion charges at ports to reduce traffic at peak times. The revenues can fund infrastructure improvements that will enable ships to engage safely in autonomous operations by communicating in real time with each other and port coordination facilities to avoid collisions.

As I discuss in my co-authored book, Autonomous Vehicles: The Road to Economic Growth?, autonomous vehicles represent a watershed moment in the development of surface transportation. They can significantly improve travel speed and reliability, and virtually eliminate highway fatalities and injuries, by creating a much smoother traffic flow where vehicles travel together at higher speeds and travelers do not cause accidents. Vehicle makers, tech companies, and research universities have been working to perfect the technology for many years.

Autonomous trucking companies, including TuSimple, PlusAI, Embark Trucks, Waymo Via, and Aurora Innovation, are attracting funds and testing their vehicles on highways to meet the challenges of responding safely to real-world driving conditions. Their operations would improve supply-chain efficiency by increasing the speed and reliability of deliveries and by mitigating the shortage of truck drivers, which contributes to delays at ports and throughout the supply chain. But trucks share the road with cars, which create the bulk of congestion. It’s essential for cars to be automated, too, so that the public can realize the full benefits of automated transportation.

Governments will play a critical role in the adoption of autonomous cars and trucks by upgrading the highway infrastructure to develop a fully optimized system, where AVs are connected to other vehicles and their surroundings, including pedestrians, infrastructure, and the road network. Devices to facilitate vehicle-to-infrastructure communication, for example, may be put on lampposts. Fiber-optic lines will be put in roads to send electronic warnings to AVs if hazards are ahead, as well as other information to keep them aware of their surroundings.

As in the case of autonomous ships, policymakers can help expedite adoption of autonomous vehicles by setting highway congestion charges, which can fund the essential infrastructure improvements. Indeed, cities throughout the world, including London, Stockholm, and Singapore, are gradually adopting congestion pricing. Recently, New York City’s legislators approved a congestion pricing plan for motorists driving into Manhattan south of 60th Street. However, London, for example, broadly allocates congestion toll revenues to its entire transport system. New York City plans to earmark some $15 billion in toll revenues for public transit.

Instead, all cities should take a long-run perspective and use a large share of the congestion toll revenues to prepare their roads for autonomous vehicles and secure a better transportation future with far less likelihood of supply chain disruptions. Efforts by some ports to reduce delays by operating 24/7 have not produced meaningful improvements to date. Highway delays are expected to grow as more economic activity returns from the pandemic.

The public is now more aware that transportation delays at any part of the supply chain can adversely affect an entire economy. That means policymakers can justify allocating revenues from congestion pricing to fund the infrastructure to facilitate autonomous transportation. Those are the investments we need to ensure Christmas stockings are filled with goods, not backorder slips.

By Wimberly Patton, CDLLife

A former truck driver has taken on the trucking job of the future – supervising autonomous trucks are they make their way through the city.

Maureen “Mo” Fitzgerald says that she was reading the paper one day when she noticed a job ad for ‘autonomous truck driver’ and figured that she’d give it a shot.

“I saw the ad in the paper that said autonomous truck driver and I thought that’s interesting so I applied and here I am now,” she said.

Fitzgerald is more of a chaperone than a driver, though. She comically praises the truck when it does well, but doesn’t do much apart from that. It’s hands off the wheel and feet off the pedals as the truck travels the freeways of Tucson, Arizona.

“I think the drivers in the trucks are learning together. We’re teaching the trucks and the companies are putting that information into the trucks and we’re all hoping they will be the safest trucks on the road,” she said.

The real driver? A big box loaded into the backseat, filled with artificial intelligence capable of making 400 trillion operations per second. It’s army of cameras scan a full 360’ around the truck and can, in theory, react 10 times faster than a human driver. The artificial intelligence controlling the truck has been instructed to watch everything from snowy roads to bad drivers to pot holes. The rig even comes complete with a plan for unexpected breakdowns.

“If the vehicle has a breakdown, it blows a tire or has an oil leak, or an air leak, the vehicle will detect that and pull itself off the road. And our oversight system will know what happened and will send rescue for that vehicle,” explained Chuck Price, chief product officer for TuSimple, the company responsible for this high-tech trucking.

“The race right now is to get a vehicle system built, but also to make a viable commercial solution that fleets can actually use,” said Price. “It never sleeps, it never texts, it’s never distracted so it’s a safer vehicle on the highway,” he continued.

Fitzgerald agrees. She says this sort of technology could help to keep truckers close to their families.

“They want to be home with their families, they want to stay local. So if we can take that long, monotonous driving out and let this truck do it and do it safely, then there’s a future in this,” she said.

The company expects the trucks to be ready for completely autonomous trips by the end of 2024.

By Kim Horner, The University of Texas at Dallas

Those cute food-delivery robots aren’t the only self-driving vehicles on The University of Texas at Dallas campus.

You might also see Voltron, an electric vehicle that looks like a cross between a golf cart and a minibus, cruising around campus this fall.

In the Engineering and Computer Science West building, you might also get a glimpse of Super COMO, a toy-sized truck, navigating the halls and practicing what to do at stop signs.

These autonomous vehicles don’t deliver snacks like the white robots from UT Dallas Dining and Starship Technologies. Voltron and Super COMO are part of research projects in the Erik Jonsson School of Engineering and Computer Science to advance autonomous vehicle technology. Researchers demonstrated Voltron and Super COMO last summer at an AI Self-Driving Small Vehicle Showcase at UT Dallas.

Voltron’s home is the lab of Dr. Justin Ruths, assistant professor of mechanical engineering. The vehicle is part of the hardware testbeds the lab aims to use to study the security of control systems. Ruths and his team of undergraduate researchers are hard at work coordinating the network of sensors and implementing algorithms to enable the vehicle’s growing autonomous capabilities.

A mechanical engineering team in the UTDesign senior capstone program retrofitted the Polaris GEM e6 vehicle with sensors, including 3D lidar, which measures the distance between the vehicle and surrounding objects, and stereoscopic cameras, which capture 3D images. A computer science UTDesign team developed a first version of the software to collect sensing data.

“All of the development of this platform has been done by undergraduates at UT Dallas, and that makes me excited about the kinds of exposure these students are getting and the expertise that we’re building,” Ruths said.

Ruths and his students also work on the theory needed to understand how to detect, quantify and mitigate attacks that could compromise the sensors.

“As you see us driving around, we’re collecting data to analyze so that we can build mathematical models to approximate these dynamics,” Ruths said.

Super COMO is another type of artificial intelligence (AI) vehicle — an autonomous car designed in the Control, Optimization, and Networks Lab run by Dr. Tyler Summers, associate professor of mechanical engineering.

UTDesign teams added sensors — like those on full-sized autonomous cars — and an onboard computer that runs machine-learning, detection, planning and control algorithms to help the vehicles avoid collisions, drive along a line or a wall, and recognize and respond to stop signs.

Sleiman Safaoui BS’19, a doctoral electrical engineering student, was a member of one of the UTDesign teams during his senior year, when he was supported through the Research Experiences for Undergraduates program sponsored by the National Science Foundation. Now, he mentors undergraduates to further develop the technology. Safaoui and fellow researchers are trying to solve problems of how noise and uncertainty — such as inaccurate measurements, misclassifications or unexpected changes in the environment — could compromise the systems that control the vehicles.

“We’re always looking for motivated and self-driven undergraduate and graduate students who are interested in autonomous vehicles and would like to work on Super COMO,” Safaoui said.

Computer engineering junior Will Heitman, who leads the team of student engineers working on Voltron, said he got involved after seeing the vehicle during a campus tour when he was in high school.

“It’s a large, red, strange-looking golf cart, so it was hard to miss,” he said.

The research projects are part of larger efforts to solve questions that need to be addressed before autonomous vehicle technology goes mainstream.

“I’m interested in the potential of self-driving cars to make roads safer, reduce traffic congestion and cut carbon emissions,” Heitman said. “Of course, I also want to help eliminate the chore of driving. I’m from Baton Rouge, Louisiana, and I drive 16 hours round trip to visit my family there. Often, I wish I could just press a button, get the car to drive me home, and I could just take a nap or something. I don’t think I’m the only person on the road who wants to do that.”

Summers’ research is supported by grants from the Air Force Office of Scientific Research, the Army Research Office and the National Science Foundation. Ruths’ project received funding through the UT System Science and Technology Acquisition and Retention (STARs) program.