Efficient Online Calibration for Autonomous Vehicle’s Longitudinal Dynamical System: A Gaussian Model Approach

Abstract

In this paper, we present an efficient online calibration system for longitudinal vehicle dynamics of driverless cars. Instead of modeling vehicle’s longitudinal dynamical system analytically, we employ a data-driven method to generate an "end-to-end" numerical model with a look-up table which saves vehicle’s velocity, control command, and acceleration. This reference table should be calibrated to account for variations of vehicle’s hardware status over time. To reduce the expensive labor in calibration process, we propose an effective algorithm to update this reference look-up table with a Gaussian model approach. We introduce a 2-D Gaussian distribution to model acceleration error between interpolated one from look-up table and actual one from vehicle sensors. We estimate model’s standard deviations with a "three-sigma rule" heuristic and calculate its height with a backtracking method such that monotonicity constraint between acceleration and control command is strictly satisfied in the updated table. The effectiveness of our proposed system is verified in realworld road tests with Lincoln MKZ.