Abstract
Abstract A new hierarchical model predictive controller (HMPC) for autonomous vehicle steering control is presented. The controller generates a path of shortest distance by determining lateral coordinates on a longitudinal grid, while respecting road bounds. This path is then parameterized by arc length before being optimized to restrict the normal acceleration values along the trajectory's arc length. The optimized trajectory is then tracked using a nonlinear MPC scheme using a bicycle plant model to calculate an optimal steering angle for the tires. The proposed controller is evaluated in simulation during a double-lane-change maneuver, where it generates and tracks a reference trajectory while observing the road boundaries and acceleration limits. Its performance is compared to a controller without path optimization, along with another that uses a smooth, predetermined, reference path instead of creating its own initial reference. It is shown that the proposed controller improves the tracking compared to a controller without path optimization, with a four-times reduction in average lateral tracking error. The average lateral acceleration is also reduced by 6%. The controller also maintains the tracking performance of a controller that uses a smooth reference path, while showing a much greater flexibility due to its ability to create its own initial reference path rather than having to follow a predetermined trajectory.
Highlights
Autonomous driving technology has become an increasingly popular research topic [1], in both academic and industrial environments
Direct Yaw-moment Control (DYC) [3] via either differential braking or active torque distribution to manipulate the longitudinal force of individual tires, is one such example of a control technique that is able to achieve more control authority by utilizing longitudinal tire forces, in a manner that is not achievable by human drivers
As there are three controllers in three separate tiers of the hierarchy, there are two call ratios, ρ1 which determines how many times the path optimization module is called for single call to the path planning module, and ρ2 determines how many times the vehicle controller is called for every trajectory optimization call: 3.1.1 Choosing the preview horizons One key design choice, critical for both the performance and stability of the hierarchical system, is the preview horizon value chosen for each of the three control modules [8]
Summary
Autonomous driving technology has become an increasingly popular research topic [1], in both academic and industrial environments. In [12] a two-tier control framework is presented consisting of path optimization, where a reference trajectory for the vehicle’s lateral displacement and yaw angle, generated via trigonometric functions, is optimized to ensure that the paths normal acceleration does not exceed a specified limit This optimal path is passed to a nonlinear model predictive controller, which calculates an optimal steering angle to track the target lateral displacement and yaw angle. This controller was evaluated on a simulation vehicle in a low friction environment during a double-lane-change maneuver.
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