Tuning for Ride Quality in Autonomous Vehicle : Application to Linear Quadratic Path Planning Algorithm

Abstract: When introducing autonomous functionality in personal vehicles the ability to control the quality of the ride is transferred from the driver to the vehicle control system. In this context, a reference method for quantifying ride quality may be a useful tool in the development and tuning process. This master’s thesis investigates whether general quantitative measures of ride quality can be of value in the tuning of motion controllers for autonomous vehicles. A set of tools is built for a specific case study, analysing a lateral path planning algorithm, based on a finite horizon linear quadratic tracking controller, and how its tuning affects ride quality performance. A graphical user interface is built, with functionality for frequency domainanalysis of the path planning algorithm, individually and in combination with the remaining lateral control system, as well as ride quality evaluation based on lateral acceleration data, from logged test runs and simulation results. In addition, a simulation environment for the lateral control system is modified to be used in combination with the evaluation tool. Results of the case study indicate a measurable difference in ride quality performance when comparing manual and autonomous driving with the current implementation. Attempts were made to improve ride quality by re-tuning the path planning algorithm but little or no improvement from the previous tuning was made. The work has recognized the potential of using ride quality measures in the development and tuning process for autonomous vehicles as well as devising a tuning strategy incorporating frequency analysis and ride quality evaluation through simulation for the lateral control system. To further increase ride quality performance via the path planning algorithm an altered controller structure, such as a frequency weighted linear quadratic controller is suggested.