Autonomous system (mathematics)

About: Autonomous system (mathematics) is a research topic. Over the lifetime, 1648 publications have been published within this topic receiving 38373 citations.

A transportable neural-network approach to autonomous vehicle following

Abstract: This paper presents the development and testing of a neural-network module for autonomous vehicle following. Autonomous vehicle following is defined as a vehicle changing its own steering and speed while following a lead vehicle. The strength of the developed controller is that no characterization of the vehicle dynamics is needed to achieve autonomous operation. As a result, it can be transported to any vehicle regardless of the nonlinear and often unobservable dynamics. Data for the range and heading angle of the lead vehicle were collected for various paths while a human driver performed the vehicle following control function. The data was collected for different driving maneuvers including straight paths, lane changing, and right/left turns. Two time-delay backpropagation neural networks were then trained based on the data collected under manual control-one network for speed control and the other for steering control. After training, live vehicle following runs were done under the neural-network control. The results obtained indicate that it is feasible to employ neural networks to perform autonomous vehicle following.

An architecture for robust UAV navigation in GPS-denied areas

Abstract: This article presents a software architecture for safe and reliable autonomous navigation of aerial robots in GPS-denied areas. The techniques employed within key modules from this architecture are explained in detail, such as a six-dimensional localization approach based on visual odometry and Monte Carlo localization, or a variant of the Lazy Theta* algorithm for motion planning. The aerial robot used to demonstrate this approach has been extensively tested over the past 2 years for localization and state estimation without any external positioning systems, autonomous local obstacle avoidance, and local path planning among other tasks. This article describes the architecture and main algorithms used to achieve these goals to build a robust autonomous system.

Toward More Realistic Driving Behavior Models for Autonomous Vehicles in Driving Simulators

Abstract: Autonomous vehicles are perhaps the most encountered element in a driving simulator. Their effect on the realism of the simulator is critical. For autonomous vehicles to contribute positively to the realism of the hosting driving simulator, they need to have a realistic appearance and, possibly more importantly, realistic behavior. Addressed is the problem of modeling realistic and humanlike behaviors on simulated highway systems by developing an abstract framework that captures the details of human driving at the microscopic level. This framework consists of four units that together define and specify the elements needed for a concrete humanlike driving model to be implemented within a driving simulator. These units are the perception unit, the emotions unit, the decision-making unit, and the decision-implementation unit. Realistic models of humanlike driving behavior can be built by implementing the specifications set by the driving framework. Four humanlike driving models have been implemented on the basis of the driving framework: (a) a generic normal driving model, (b) an aggressive driving model, (c) an alcoholic driving model, and (d) an elderly driving model. These driving models provide experiment designers with a powerful tool for generating complex traffic scenarios in their experiments. These behavioral models were incorporated along with three-dimensional visual models and vehicle dynamics models into one entity, which is the autonomous vehicle. Subjects perceived the autonomous vehicles with the described behavioral models as having a positive effect on the realism of the driving simulator. The erratic driving models were identified correctly by the subjects in most cases.