  For any autonomous driving vehicle, control module determines its road
performance and safety, i.e. its precision and stability should stay within a
carefully-designed range. Nonetheless, control algorithms require vehicle
dynamics (such as longitudinal dynamics) as inputs, which, unfortunately, are
obscure to calibrate in real time. As a result, to achieve reasonable
performance, most, if not all, research-oriented autonomous vehicles do manual
calibrations in a one-by-one fashion. Since manual calibration is not
sustainable once entering into mass production stage for industrial purposes,
we here introduce a machine-learning based auto-calibration system for
autonomous driving vehicles. In this paper, we will show how we build a
data-driven longitudinal calibration procedure using machine learning
techniques. We first generated offline calibration tables from human driving
data. The offline table serves as an initial guess for later uses and it only
needs twenty-minutes data collection and process. We then used an
online-learning algorithm to appropriately update the initial table (the
offline table) based on real-time performance analysis. This longitudinal
auto-calibration system has been deployed to more than one hundred Baidu Apollo
self-driving vehicles (including hybrid family vehicles and electronic
delivery-only vehicles) since April 2018. By August 27, 2018, it had been
tested for more than two thousands hours, ten thousands kilometers (6,213
miles) and yet proven to be effective.
