Safety driver assistance system for off-road mobile machinery

Abstract

Well-trained operators have been the only way to ensure the functional safety of off-road machinery. Therefore, drivers must undergo challenging training to be licensed to operate off-road machines. Driver assistance systems can help make the training and operation of the machine easier and safer. An integrated safety System will add layers of safety to the machine in case of human error or even in the future autonomous systems. There are safety standards regarding the design and operation of Off-road machines, but still no clear standards for driver assistance system design; therefore, in this thesis, we propose using a combination of concepts from standards developed for road vehicles and standards for robots sharing work common workspace with workers. Using these concepts we can design a safety-oriented driver assistance system. The functionality of these concepts is tested using a case study of a wheel loader machine. This case study is tested in a simulation environment constructed by Mevea and Matlab is used for the control system. The system effectively prevented crashes in all cases provided that the wheel loader speed was less than 40km per hour. when tested object detection cases using 4 LIDAR scanners. Real-time distance sensing techniques using LiDAR scanners allow separation distance control techniques not only object detection techniques as a method to avoid collision. Future autonomous machines will benefit greatly from having a separate safety system with separation distance control.