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

Baidu Apollo Auto-Calibration System - An Industry-Level Data-Driven and Learning based Vehicle Longitude Dynamic Calibrating Algorithm