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Zhi Quan Zhou ( email@example.com) is an associate
professor in software engineering at the School of
Computing and Information Technology, University of
Wollongong, Wollongong, NS W, Australia.
Liqun Sun ( firstname.lastname@example.org) is pursuing an
M.Phil. degree in computer science at the University of
Wollongong, Wollongong, NSW, Australia, and a software
engineer at Itree, Wollongong, Australia.
© 2019 ACM 0001-0782/19/3
html). Figure 3b to Figure 3e summarize the test results of these categories,
and Figure 3a shows the overall results
corresponding to the Table.
Each vertical column in Figure 3 in-
cludes a subsection in blue, correspond-
ing to MR1 violations. They are labeled
with the actual numbers of |O| > |O′|
cases. We observed that all these num-
bers were greater than 0, indicating
critical errors in the perception of all
four types of obstacles: car, pedestrian,
cyclist, and unknown. Relatively speak-
ing, the error rate of the “car” category
was greatest, followed by “pedestrian,”
“cyclist,” and “unknown.”
Figure 4a and Figure 4b show a real-
world example revealed by our test,
whereby three cars inside the ROI could
not be detected after we added 1,000
random points outside the ROI. Fig-
ure 4c and Figure 4d show another ex-
ample, whereby a pedestrian inside the
ROI (the Apollo system depicted this
pedestrian with the small pink mark in
Figure 4c) could not be detected after
we added only 10 random points out-
side the ROI; as shown in Figure 4d, the
small pink mark was missing. As men-
tioned earlier, we reported the bug to
the Baidu Apollo self-driving car team
on March 10, 2018. On March 19, 2018,
the Apollo team confirmed the error by
acknowledging “It might happen” and
suggested “For cases like that, models
can be fine tuned using data augmen-
tation”; data augmentation is a tech-
nique that alleviates the problem of
lack of training data in machine learn-
ing by inflating the training set through
transformations of the existing data.
Our failure-causing metamorphic test
cases (those with the random points)
could thus serve this purpose.
The 2018 Uber fatal crash in Tempe,
AZ, revealed the inadequacy of con-
ventional testing approaches for mis-
sion-critical autonomous systems.
We have shown MT can help address
this limitation and enable automatic
detection of fatal errors in self-driv-
ing vehicles that operate on either
conventional algorithms or deep
learning models. We have introduced
an innovative testing strategy that
combines MT with fuzzing, reporting
how we used it to detect previously
unknown fatal errors in the real-life
LiDAR obstacle perception system of
Baidu’s Apollo self-driving software.
The scope of our study was limited
to LiDAR obstacle perception. Apart
from LiDAR, an autonomous vehicle
may also be equipped with radar. According to the Apollo website (http://
data.apollo.auto), “Radar could precisely estimate the velocity of moving
obstacles, while LiDAR point cloud
could give a better description of object shape and position.” Moreover,
there can also be cameras, which
are particularly useful for detecting
visual features (such as the color of
traffic lights). Our testing technique
can be applied to radar, camera, and
other types of sensor data, as well as
obstacle-fusion algorithms involving
multiple sensors. In future research,
we plan to collaborate with industry to
develop MT-based testing techniques,
combined with existing verification
and validation methods, to make driverless vehicles safer.
This work was supported in part
by a linkage grant from the Australian Research Council, project ID:
LP160101691. We would like to thank
Suzhou Insight Cloud Information
Technology Co., Ltd., for supporting
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