Simulated terrible drivers cut the time and cost of AV testing by a
factor of one thousand
New virtual testing environment breaks the 'curse of rarity' for
autonomous vehicle emergency decision-making

Date:
March 22, 2023
Source:
University of Michigan
Summary:
The push toward truly autonomous vehicles has been hindered by the
cost and time associated with safety testing, but a new system
shows that artificial intelligence can reduce the testing miles
required by 99.99%.


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FULL STORY ==========================================================================
The push toward truly autonomous vehicles has been hindered by the cost
and time associated with safety testing, but a new system developed at
the University of Michigan shows that artificial intelligence can reduce
the testing miles required by 99.99%.


==========================================================================
It could kick off a paradigm shift that enables manufacturers to more
quickly verify whether their autonomous vehicle technology can save
lives and reduce crashes. In a simulated environment, vehicles trained
by artificial intelligence perform perilous maneuvers, forcing the AV
to make decisions that confront drivers only rarely on the road but are
needed to better train the vehicles.

To repeatedly encounter those kinds of situations for data collection,
real world test vehicles need to drive for hundreds of millions to
hundreds of billions of miles.

"The safety critical events -- the accidents, or the near misses -- are
very rare in the real world, and often time AVs have difficulty handling
them," said Henry Liu, U-M professor of civil engineering and director
of both Mcity and the Center for Connected and Automated Transportation,
a regional transportation research center funded by the U.S. Department
of Transportation.

U-M researchers refer to the problem as the "curse of rarity," and they're tackling it by learning from real-world traffic data that contains rare
safety- critical events. Testing conducted on test tracks mimicking
urban as well as highway driving showed that the AI-trained virtual
vehicles can accelerate the testing process by thousands of times. The
study appears on the cover of Nature.

"The AV test vehicles we're using are real, but we've created a mixed
reality testing environment. The background vehicles are virtual, which
allows us to train them to create challenging scenarios that only happen
rarely on the road," Liu said.

U-M's team used an approach to train the background vehicles that strips
away nonsafety-critical information from the driving data used in the simulation.

Basically, it gets rid of the long spans when other drivers and
pedestrians behave in responsible, expected ways -- but preserves
dangerous moments that demand action, such as another driver running a
red light.

By using only safety-critical data to train the neural networks that
make maneuver decisions, test vehicles can encounter more of those rare
events in a shorter amount of time, making testing much cheaper.

"Dense reinforcement learning will unlock the potential of AI for
validating the intelligence of safety-critical autonomous systems such as
AVs, medical robotics and aerospace systems," said Shuo Feng, assistant professor in the Department of Automation at Tsinghua University and
former assistant research scientist at the U-M Transportation Research Institute.

"It also opens the door for accelerated training of safety-critical
autonomous systems by leveraging AI-based testing agents, which may create
a symbiotic relationship between testing and training, accelerating
both fields." And it's clear that training, along with the time and
expense involved, is an impediment. An October Bloomberg article stated
that although robotaxi leader Waymo's vehicles had driven 20 million
miles over the previous decade, far more data was needed.

"That means," the author wrote, "its cars would have to drive an
additional 25 times their total before we'd be able to say, with even a
vague sense of certainty, that they cause fewer deaths than bus drivers." Testing was conducted at Mcity's urban environment in Ann Arbor, as
well as the highway test track at the American Center for Mobility
in Ypsilanti.

Launched in 2015, Mcity, was the world's first purpose-built test
environment for connected and autonomous vehicles. With new support
from the National Science Foundation, outside researchers will soon be
able to run remote, mixed reality tests using both the simulation and
physical test track, similar to those reported in this study.

Real-world data sets that support Mcity simulations are collected from
smart intersections in Ann Arbor and Detroit, with more intersections
to be equipped.

Each intersection is fitted with privacy-preserving sensors to
capture and categorize each road user, identifying its speed and
direction. The research was funded by the Center for Connected and
Automated Transportation and the National Science Foundation.

* RELATED_TOPICS
o Matter_&_Energy
# Automotive_and_Transportation # Vehicles #
Transportation_Science # Engineering
o Computers_&_Math
# Robotics # Computer_Modeling # Virtual_Reality #
Communications
* RELATED_TERMS
o Computer_vision o Road-traffic_safety o Safety_engineering
o Scale_model o Artificial_intelligence o Wi-Fi o
Automobile_safety o Ethanol_fuel

========================================================================== Story Source: Materials provided by University_of_Michigan. Original
written by Jim Lynch.

Note: Content may be edited for style and length.


========================================================================== Journal Reference:
1. Shuo Feng, Haowei Sun, Xintao Yan, Haojie Zhu, Zhengxia Zou,
Shengyin
Shen, Henry X. Liu. Dense reinforcement learning for safety
validation of autonomous vehicles. Nature, 2023; 615 (7953):
620 DOI: 10.1038/s41586- 023-05732-2 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2023/03/230322140354.htm

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