Examining the use of B-splines in parking assist systems

The main objective of the presented study and simulations conducted was to investigate the prospect of using B-spline curves for the automatic parking, i.e. self-driving, or intelligent vehicles. We consider the problem of parallel parking for a non-holonomic vehicle with a known maximum path curvature. The relationship between the properties of the path and the geometry of corresponding parking spot is revealed. The unique properties of B-splines are exploited to synthesize a path that is smooth and of continuous curvature. The contributions of this project are in the generations of better, smooth continuous paths. This improves passenger comfort during the parallel parking maneuver and allow vehicles to park in tighter spots by increasing the feasible range of the parking manoeuver

Model Predictive Control System Design of a passenger car for Valet Parking Scenario

A recent expansion of passenger cars’ automated functions has led to increasingly challenging design problems for the engineers. Among this the development of Automated Valet Parking is the latest addition. The system represents the next evolution of automated system giving the vehicle greater autonomy: the efforts of most automotive OEMs go towards achieving market deployment of such automated function. To this end the focus of each OEM is on taking part to this competitive endeavor and succeed by developing a proprietary solution with the support of hardware and software suppliers. Within this framework the present work aims at developing an effective control strategy for the considered scenarios. In order to reach this goal a Model Predictive Control approach is employed taking advantage of previous works within the automotive OEM in the automated driving field. The control algorithm is developed in a Simulink® simulation according to the requirements of the application and tested; results show the control strategy successfully drives the vehicle on the predefined path

Simultaneous Localization and Mapping (SLAM) for Autonomous Driving: Concept and Analysis

The Simultaneous Localization and Mapping (SLAM) technique has achieved astonishing progress over the last few decades and has generated considerable interest in the autonomous driving community. With its conceptual roots in navigation and mapping, SLAM outperforms some traditional positioning and localization techniques since it can support more reliable and robust localization, planning, and controlling to meet some key criteria for autonomous driving. In this study the authors first give an overview of the different SLAM implementation approaches and then discuss the applications of SLAM for autonomous driving with respect to different driving scenarios, vehicle system components and the characteristics of the SLAM approaches. The authors then discuss some challenging issues and current solutions when applying SLAM for autonomous driving. Some quantitative quality analysis means to evaluate the characteristics and performance of SLAM systems and to monitor the risk in SLAM estimation are reviewed. In addition, this study describes a real-world road test to demonstrate a multi-sensor-based modernized SLAM procedure for autonomous driving. The numerical results show that a high-precision 3D point cloud map can be generated by the SLAM procedure with the integration of Lidar and GNSS/INS. Online four–five cm accuracy localization solution can be achieved based on this pre-generated map and online Lidar scan matching with a tightly fused inertial system