Truck & Off-Highway Engineering – June 2019

20 June 2019 TRUCK & OFF-HIGHWAY ENGINEERING

ALL IMAGES: TUSIMPLE

EXECUTIVE VIEWPOINTS

T

he developers who are creating autonomous systems have

plenty of complex technical questions that must be analyzed

and solved, but their challenges don’t stop there. Design teams

also have to plan for the use cases of driverless vehicles, even

going so far as to plan for what might happen if the vehicle has to sac-

rifice itself to avoid a serious accident.

The challenges associated with the sea change

wrought by autonomous trucking has opened the

door for startups like TuSimple, a Chinese startup

that’s partially funded by Nvidia. The company is

developing digital control systems, focusing on both

the prototypes needed to prove the safety of driver-

less trucks as well as the many issues associated

with getting them into day-to-day operations.

“Until you have hardware that’s compliant with safety

standards and common automotive standards for sur-

vival in harsh environments, plus supplies for 10 years,

you’re not really ready,” Chuck Price, Chief Product

Officer at TuSimple, told Truck & Off Highway

Engineering. “When you’re getting suppliers to agree,

it’s one thing to supply prototype volumes, it’s another

to get to production. That’s a real challenge.”

Pathway to production

Price and company, which has a U.S. headquarters in

San Diego, are also looking at the bigger picture of

getting production vehicles on the highways. Some

companies have proposed using autonomous con-

trols only on highways, guiding driverless trucks into

transfer hubs where drivers would take over for more

intricate aspects of delivery. Price contends that this

approach has several issues that make it impractical.

“We’ve got pretty definitive views how autonomous

trucks will be integrated into the transportation envi-

ronment,” he explained. “Using transportation hubs

means you’re only using autonomy on highways, mov-

ing into a manual mode for off-highway deliveries. We

feel building transfer hubs brings major requirements

for zoning and buying land. You’re talking about hun-

dreds of thousands of trucks going in and out.”

TuSimple therefore feels that using autonomous

technologies through the whole loading to unloading

process is a more preferable approach. Driving au-

tonomously on both highways and city streets, going

from depot to depot using automated systems, is the

most efficient way to move freight. TuSimple is ad-

dressing that by developing sensors and controls that

are now being tested on roadways. Real-world tests

are important, but virtual testing is also a necessity.

“Simulation is an integral part of the development

program,” Price said. “It allows us to do experiments

very quickly in a very accurate way. It

also lets us do things that are difficult

or dangerous to do on highways.”

These simulations and validation

runs will increasingly utilize artificial

intelligence. AI helps vehicle controls

identify objects and take action in

response to their location. Though AI

is generally accepted as one of the

technologies needed to teach autono-

mous systems how to respond to the

complex nuances of navigating along

roadways, critics raise issues.

Detractors note that it’s difficult to

verify the accuracy of software re-

sponses that change depending on

circumstances. TuSimple is addressing

that by using more than one type of AI.

“There are different forms of artificial

intelligence,” Price said. “Some people

are trapped into just thinking about

neural networks. We use convolutional

neural networks for perception only,

using it on sensor feeds from cameras,

radar and lidar. When you get into pre-

diction and planning, statistical AI is

Autonomous developers at TuSimple address many technical issues, but they

also must consider regulations and operating modes.

by Terry Costlow, based on exclusive interview with TuSimple’s Chuck Price

Getting driverless trucks

onto roadways

“When you get

into prediction

and planning,

statistical AI

is better than

neural networks.

It’s more readily

characterized.”

TuSimple tests its hub-to-hub SAE L4 self-driving trucks in Arizona, this particular

run occurring in heavy rain with zero disengagements.

Chuck Price