Autonomous Driving Vehicle Control Auto-Calibration System: An Industry-Level, Data-Driven and Learning-based Vehicle Longitudinal Dynamic Calibrating Algorithm

The control module is a crucial part for autonomous driving systems, a typical control algorithm often requires vehicle dynamics (such as longitudinal dynamics) as inputs, which, unfortunately are difficult to calibrate in real time. Further, it is also a challenge to reflect instantaneous changes in longitudinal dynamics (e.g. load changes) using a calibration table. As a result, control performance may deteriorate when load changes considerably (especially for small cargoes). In this paper, we will show how we build a data-driven longitudinal calibration procedure using machine learning techniques to adapt load changes in real time. We first generated offline calibration tables from human driving data. The offline table serves as an initial guess for later uses, and it only requires twenty minutes of data collection and processing. We then used an online learning algorithm to appropriately update the initial table (the offline table) based on real-time performance analysis. Experiments indicated (a) offline auto-calibration leads to a better control accuracy, compared with manual calibration; (b) online auto-calibration is capable to handle load changes and significantly reduce real time control error. This system has been deployed to more than one hundred Baidu self-driving vehicles (both hybrid and electronic vehicles) since April 2018. By January 2019, the system had been tested for more than 2,000 hours and over 10,000 kilometers (6,213 miles) and was still proven to be effective.