  Autonomous driving has been quite promising in recent years. The public has
seen Robotaxi delivered by Waymo, Baidu, Cruise, and so on. While autonomous
driving vehicles certainly have a bright future, we have to admit that it is
still a long way to go for products such as Robotaxi. On the other hand, in
less complex scenarios autonomous driving may have the potentiality to reliably
outperform humans. For example, humans are good at interactive tasks (while
autonomous driving systems usually do not), but we are often incompetent for
tasks with strict precision demands. In this paper, we introduce a real-world,
industrial scenario of which human drivers are not capable. The task required
the ego vehicle to keep a stationary lateral distance (i.e. 3? <= 5
centimeters) with respect to a reference. To address this challenge, we
redesigned the control module from Baidu Apollo open-source autonomous driving
system. A precise (3? <= 2 centimeters) Error Feedback System was first built
to partly replace the localization module. Then we investigated the control
module thoroughly and added a real-time calibration algorithm to gain extra
precision. We also built a simulation to fine-tune the control parameters.
After all those works, the results are encouraging, showing that an end-to-end
lateral precision with 3? <= 5 centimeters has been achieved. Further, we show
that the results not only outperformed original Apollo modules but also beat
specially trained and highly experienced human test drivers.
