Obstacle

About: Obstacle is a research topic. Over the lifetime, 9517 publications have been published within this topic receiving 94760 citations. The topic is also known as: impediment & barrier.

Obstacle detecting device for vehicle

Abstract: PROBLEM TO BE SOLVED: To provide an obstacle detecting device for a vehicle capable of finely and accurately deciding an obstacle being a trouble in the traveling of the vehicle. SOLUTION: This device is provided with a vehicle estimated traveling trajectory arithmetic unit 24 for calculating an estimated traveling trajectory on which a vehicle is presumed to travel from a steering angle at a current point of time, an image processor 32 for processing the image of a camera 34 which photographs a road conditions (white lines and objects) and a radar device 36 which detects an object in front of the vehicle. Whether or not the object on the estimated traveling trajectory is a dangerous obstacle, or whether or not the object on the traveling path is a dangerous obstacle is decided based on the estimated traveling trajectory, the traveling path, and the object grasped by using those devices 24, 32, and 36. Then, when an obstacle exists in the estimated traveling trajectory, and the object exists on the traveling path, it is decided that a significant danger is generated in the vehicle compared with the case in which an obstacle exists in the traveling path, while no obstacle exist in the estimated traveling trajectory. COPYRIGHT: (C)2004,JPO

Hierarchical Planning with 3D Local Multiresolution Obstacle Avoidance for Micro Aerial Vehicles

Abstract: Micro aerial vehicles (MAVs), such as multicopters, are particular well suited for the inspection of human-built structures, e. g., for maintenance or disaster management. Today, the operation of MAVs in the close vicinity of these structures requires a human operator to remotely control the vehicle. For fully autonomous operation, a detailed model of the environment is essential. Building such a model by means of autonomous exploration is time consuming and delays the execution of the main mission. In many real-world applications, a coarse model of the environment already exists and can be used for high-level planning. Nevertheless, detailed obstacle maps, needed for safe navigation, are often not available. We employ the coarse information for global mission and path planning and refine the path on the fly, whenever the vehicle can acquire information with its onboard sensors. To allow for fast replanning during the flight, we present a 3D local multiresolution path planning approach making online grid-based planning for our MAV platform tractable.

Embedded Controller for Vehicle In-Front Obstacle Detection and Cabin Safety Alert System

Abstract: In today’s world safety and security plays an important role, hence we tend to provide a good safety and security system while travelling. Vehicles are important in today’s fast-paced society. Hence, acquiring a vehicle nowadays is considered a necessity, compared to the past where it was considered a luxury. In this thriving society, more and more vehicles are produced to meet the increasing demands of people and businesses from all corners of the world. Here comes the necessity to provide more and more safety and security features to them. Hence this project aims to design an embedded system for vehicle cabin safety and security by modifying and integrating the existing modules. This monitors the level of the toxic gases such as CO, LPG and alcohol inside the vehicle and provides alert information in the form of alarm during the critical situations. And also send SMS to the authorised person through the GSM. An IR Sensor is used to detect the static obstacle in front of the vehicle and the vehicle gets stopped if any obstacle is detected. This may avoid accidents due to collision of vehicles with any static obstacles.

Obstacle detection procedure for motor vehicle

Abstract: An obstacle detection procedure and detection device within the area surrounding a motor vehicle featuring the following: to define at least one first region of interest by carrying out a first detection of an obstacle by shape recognition in an acquired image of this environment; to define at least one second region of interest by carrying out a second detection of an obstacle by movement detection in relation to the vehicle on a sequence of acquired images of this environment; to classify the detected obstacle with, respectively, a first and second index of confidence applied to the first and second regions of interest in relation to given characteristics; and to validate the classification of the object detected in relation to these indices of confidence and in relation to these regions of interest resulting in validated regions of interest.