Kinematics Design of a MacPherson Suspension Architecture Based on Bayesian Optimization.

Engineering design is traditionally performed by hand: an expert makes design proposals based on past experience, and these proposals are then tested for compliance with certain target specifications. Testing for compliance is performed first by computer simulation using what is called a discipline model. Such a model can be implemented by finite element analysis, multibody systems approach, etc. Designs passing this simulation are then considered for physical prototyping. The overall process may take months and is a significant cost in practice. We have developed a Bayesian optimization (BO) system for partially automating this process by directly optimizing compliance with the target specification with respect to the design parameters. The proposed method is a general framework for computing the generalized inverse of a high-dimensional nonlinear function that does not require, for example, gradient information, which is often unavailable from discipline models. We furthermore develop a three-tier convergence criterion based on: 1) convergence to a solution optimally satisfying all specified design criteria; 2) detection that a design satisfying all criteria is infeasible; or 3) convergence to a probably approximately correct (PAC) solution. We demonstrate the proposed approach on benchmark functions and a vehicle chassis design problem motivated by an industry setting using a state-of-the-art commercial discipline model. We show that the proposed approach is general, scalable, and efficient and that the novel convergence criteria can be implemented straightforwardly based on the existing concepts and subroutines in popular BO software packages.

Dataset for trailer angle estimation using radar point clouds.

The automotive industry is interested in the estimation of vehicle trailer rotation (trailer angle or hitch angle) due to its use in trailer control algorithms. We present an experimental dataset collected in a study of the estimation problem [1] and a MATLAB code implementation of the study. The data collection apparatus is a truck mock-up that is attached to a flatbed trailer at the hitch ball. Two radars are installed in the taillight fixtures of the truck and a camera is installed in the truck's tailgate like a typical backup camera installation. A rotary motion sensor is also installed at the hitch ball to provide ground truth measurement of the trailer angle. To aid analysis of the dataset, both radar detections are transformed onto a vehicle coordinate system (VCS) having its origin at the hitch ball i.e. the different radar viewpoints are combined into one with respect to the hitch ball. The MATLAB code presented with this article has two major functionalities. The first functionality is the visualization of both radar detections, the combined radar detections in the VCS, the camera images, and the ground truth angles, as the trailer rotates. The second functionality of the code is the replication of the estimation results in [1], which used only the radar detections from the dataset.