IEEE Robotics & Automation Magazine - March 2021 - 57

uncertainty from the perception modules. This uncertainty comes from the limited accuracy of the perception
system as well as the time delay in processing. For the
planning, we avoided hitting the critical conditions and
left safe distances for the planned trajectory.
For the control part, we found that the greatest advantage of using an MPC can be gained by adding multiple
constraints in the control process. When the vehicle operates at low speed, the kinematic constraints restrain the
vehicle motion planning and control. But, with the increment of speed, dynamic characteristics become more
influential. As mentioned previously, the dynamic-based
MPC is much more accurate than the kinematic-based
MPC since the predictive model is more accurate. However, we found that complex model prediction was not the
best option. With regards to low- and middle-speed operation environments, a simplified predictive model with
multiple constraints would be sufficient.
From our real-life operations, we found that more conservative settings for obstacle detection could lead to
more false positives. This would decrease the speed or
even freeze the vehicle and hence cause traffic jams. On
some roads, the minimum allowed speed is not indicated,
so we need to maintain a vehicle speed that is not too slow
in order not to cause annoyance for other vehicle drivers.
A clearly and easily identified human-machine interface
is also important. It can be used to inform other vehicle
drivers in advance what the autonomous vehicle will do.
Otherwise, the other vehicle drivers could feel frightened
because they might not be able to anticipate the behaviors
of the autonomous vehicle. For example, our vehicle often
startled other vehicle drivers when it was reversing, even
when the reversing light was flashing. Using a screen to
notify the reversing behavior could alleviate the issue. In
some cases, not strictly obeying the traffic rules would be
good for autonomous navigation. For example, it would
be wise to change lanes when a traffic accident happens
ahead in the ego lane, even if the lane changing behavior
is not allowed according to the traffic rules. Otherwise,
the vehicle would not be able to move forward.
The successful daily operations demonstrated that using
our autonomous logistic vehicle could effectively avoid
virus spread due to human contact. It effectively builds a
virtual wall between the recipient and sender during goods
transportation. For quantitative measures, we can compute
from Table 1 that the average distance for each task per
vehicle is (9.6 + 5.4 + 1.2 + 0.6 + 1.6 + 4.0) /6 . 3.7 km.
As the total running distance is 2,500 km, the number of
tasks is 2, 500/3.7 . 676. According to our observation,
there are usually four instances of person-to-person contact in each task of traditional goods transportation. So the
number of avoided contacts would be 4 # 676 = 2, 704. As
we have 25 running vehicles, the total number of avoided
contacts would be 25 # 2, 704 = 67, 600. Currently, there
is a huge demand for contactless goods transportation in
many infected areas. We believe that continuous long-term

operations could extensively improve our vehicle and
enhance the maturity of our technologies.
Acknowledgments
This work was supported by the National Natural Science
Foundation of China, under grant U1713211, Collaborative
Research Fund by Research Grants Council Hong Kong,
under Project C4063-18G, and HKUST-SJTU Joint
Research Collaboration Fund, under project SJTU20EG03,
awarded to Prof. Ming Liu.
References
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Tianyu Liu, Shenzhen Unity Drive Innovation Technology Co.
Ltd., Shenzhen, 518000, China. Email: liutianyu@unity-drive
.com.
Qinghai Liao, The Hong Kong University of Science and
Technology, Clear Water Bay, Hong Kong, 999077, China.
Email: qinghai.liao@connect.ust.hk.
Lu Gan, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, 999077, China. Email:
lganaa@connect.ust.hk.
MARCH 2021

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IEEE Robotics & Automation Magazine - March 2021

Table of Contents for the Digital Edition of IEEE Robotics & Automation Magazine - March 2021

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