Road Orientation Research Articles

Land mobile satellite Ka‐band shadowing statistics in built‐up areas for different road orientations

Land mobile satellite Ka‐band shadowing statistics in built‐up areas for different road orientations

Land mobile satellite Ka‐band shadowing statistics in built‐up areas for different road orientations

Land mobile satellite Ka‐band shadowing statistics in built‐up areas for different road orientations

Fast road classification and orientation estimation using omni-view images and neural networks

This paper presents the results of integrating omnidirectional view image analysis and a set of adaptive backpropagation networks to understand the outdoor road scene by a mobile robot. Both the road orientations used for robot heading and the road categories used for robot localization are determined by the integrated system, the road understanding neural networks (RUNN). Classification is performed before orientation estimation so that the system can deal with road images with different types effectively and efficiently. An omni-view image (OVI) sensor captures images with 360 degree view around the robot in real-time. The rotation-invariant image features are extracted by a series of image transformations, and serve as the inputs of a road classification network (RCN). Each road category has its own road orientation network (RON), and the classification result (the road category) activates the corresponding RON to estimate the road orientation of the input image. Several design issues, including the network model, the selection of input data, the number of the hidden units, and learning problems are studied. The internal representations of the networks are carefully analyzed. Experimental results with real scene images show that the method is fast and robust.

Feed-forward multilayer neural network model for vehicle lateral guidance control

An advanced vehicle lateral guidance control technology is necessary in order to develop intelligent transportation and manufacturing systems with flexibility and immediate adaptability. PID control, optimal control, and fuzzy control have often been used for designing a vehicle lateral guidance controller; in addition automatic guidance methods by spline curve and inverse dynamics are also used in mobile robots (e.g. differential drive), but they are not sufficient to develop a highly intelligent vehicle lateral guidance controller which can adapt to varying environments, because they lack some behavior like learning ability and adaptability. In this paper, the possibility to apply neural networks for developing a vehicle lateral guidance controller is exposed. A new neuron activation function suitable for vehicle lateral guidance control is suggested, a feed-forward multilayer neural network (FMNN) with the suggested neuron activation function is proposed and a vehicle lateral guidance controller (VLGC) is developed by use of the FMNN. The VLGC can be applied to automobiles of different parameters and roads of various widths. It can be also applied to mobile robots. Its input variables are proposed to be defined as kind of relative quantities by using the road width, automotive parameter, automotive position, and orientation on the corner course as 90°. Its output variable is the automotive steering angle. Its teaching data are collected by automobile driving simulation, and its connection weights and threshold values are tuned through the error back-propagation algorithm. The training process and the result of neural network by different learning rate coefficients and momentum parameters are compared. Four VLGCs are generated through training by using different learning rate coefficients, momentum parameters, and repeat training times. Automated guided automobile simulations and mobile robot experiments for each VLGC are carried out. Good training result as well as automated guided simulation and experimental results are obtained.