Proceedings of the Intelligent Vehicles '93 Symposium最新文献

{"title":"Real-time Neural Vision For Vehicle Navigation And Safety","authors":"Lurng-Kuo Liu, P.A. Ligonienides","doi":"10.1109/IVS.1993.697337","DOIUrl":"https://doi.org/10.1109/IVS.1993.697337","url":null,"abstract":"This paper presents an investigation of the application of image analysis, processing and transmission techniques to vehicle navigation and safety. Orthogonalization neural network architectures are used for complex tasks of real time image processing for purposes of navigation and safety, in situations of vehicle maneuvering in unknown and hazardous environments. Hardware-implemented neural networks are used for image processing and compression/decompression on both ends of a communication link between a Traffic Information Center and vehicle.","PeriodicalId":283697,"journal":{"name":"Proceedings of the Intelligent Vehicles '93 Symposium","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1993-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123240937","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Vehicular Traffic GIS And Simulator For Route Guidance NY/NJ Highways","authors":"J. Holtzman, J. Hui, N. Moayeri, I. Seskar, H. Varma, J. Yip, S. Marie, T. Williams","doi":"10.1109/IVS.1993.697356","DOIUrl":"https://doi.org/10.1109/IVS.1993.697356","url":null,"abstract":"This paper describes the design of a macroscopic traffic simulator using Geographical Information Systems (GIS) and its implementation for performance evaluation of different Intelligent Vehicle Highway Systems ( IVHS) route guidance strategies. The GIS and the associated routing algorithms are intended for deployment in the NY/NY metropolitan area by TRANSCOM.","PeriodicalId":283697,"journal":{"name":"Proceedings of the Intelligent Vehicles '93 Symposium","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1993-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126501388","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Action Planning For The Collision Avoidance System Using Neural Networks","authors":"M. A. Arain, R. Tribe, E. An, C. Harris","doi":"10.1109/IVS.1993.697308","DOIUrl":"https://doi.org/10.1109/IVS.1993.697308","url":null,"abstract":"An understanding of the scenario in complex traffic situations is essential in order to give an early warning, or in an autonomous system, to intervene in the urban or motorway environment. A collision avoidance system needs both to predict possible collisions or hazards and to plan a less hazardous move in a critical situation. A crucial factor in the success of the system is the use of a priori knowledge. The classical problem with a knowledge-based decision making system is the acquisition and representation of the knowledge. It is diffcult to design and develop a system for real time auto-piloting in varied traffic environments. Neural networks are ideally suited for applications where a large training set is available because they can apply human decision making criteria in different situations. The learning processes encapsulate a wide variety of drivers' reactions to various scenarios. Neural networks' abilities to generalise their training to new scenarios in the light of driving experience and to make emotion-free decisions leads to a system that is adaptive and closely which resembles human action strategy. Recognition of a scenario is achieved by acquiring data about a scene from a variety of sensors. Visual data is preprocessed and features are extracted using a real-time image processing system, while microwave radar provides obstacle information and distances. This paper described an early warning system and suggests possible responses to various traffic situations. The paper focuses on various learning algorithms for decision making which is based on the current model and immediate history only. It would help if we could always recognise the dominant threat at every instant and avoid it by either slowing down or changing direction. In our analysis of situations using neural networks, the test cases show that reasonably such behaviour can be generated. In order to validate the auto pilot it is tested in parallel with expert drivers to assess the drivers' action in a number of scenarios. The network's intervention control is verified by independent observers. The intervention strategies are based on a number of rules by which an intervention controller is trained to generate various actions. These rules are fine tuned on-line to achieve reliable and repeatable actions.","PeriodicalId":283697,"journal":{"name":"Proceedings of the Intelligent Vehicles '93 Symposium","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1993-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131159787","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Vehicle Steering Algorithm Based On Bearing Measurements","authors":"R. Braithwaite","doi":"10.1109/IVS.1993.697338","DOIUrl":"https://doi.org/10.1109/IVS.1993.697338","url":null,"abstract":"This paper presents an algorithm for camera-based navigation of a conventionally steered vehicle to a visible marker. The algorithm whose primary measurement is the marker bearing, is robust to range errors, to awkward initial conditions, and to small temporal effects.","PeriodicalId":283697,"journal":{"name":"Proceedings of the Intelligent Vehicles '93 Symposium","volume":"2014 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1993-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127585806","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Visual Routines For Outdoor Navigation","authors":"M. Campani, M. Cappello, G. Piccioli, E. Reggi, M. Straforini, Vincent Torre","doi":"10.1109/IVS.1993.697306","DOIUrl":"https://doi.org/10.1109/IVS.1993.697306","url":null,"abstract":"This paper describes several visual routines which can be used to assist the navigation of a car in an outdoor environment. These routines are based on the grouping of edge chains in two classes of straight segments and on a suitable segmentation of the image in different regions. Such routines are able to provide a preliminary recovery of the 3D structure of the viewed scene, in which the sides of the road and the buildings are detected, and to recognize several landmarks, such as street signs, trees and light lamps. These results suggest that it is possible to construct drivers' assistant systems based on computer vision technologies.","PeriodicalId":283697,"journal":{"name":"Proceedings of the Intelligent Vehicles '93 Symposium","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1993-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125330824","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimal TriggeridngOf An Airbag","authors":"K. Watanabe, Y. Umezawa","doi":"10.1109/IVS.1993.697301","DOIUrl":"https://doi.org/10.1109/IVS.1993.697301","url":null,"abstract":"An airbag upon collision of automobiles must finish expanding at the time when the occupant arrives at the surface of tlie expanded airbag from the normal position and it must receive the body softly. Either an early or late expansion reduces the effect. Determination of the optimal timing for triggering the airbag is quite important. But i t is a difficult job, due to the time delay in the bag filling with gas after the collision. This paper will focuse on the airbag technology concemed with finding the optimal trigger timing and will present a new and straightforward algorithm to determine this timing by introducing the concept of the prediction. The algorithm first predicts how an occupant will move in a time corresponding to a delay time and then using this predicted information the airbag will trigger so as to compensate this delay in its operation. The algorithm determines the timing only from the acceleration measured by one sensor set i n the automobile and is composed of the following four basic processing blocks; Kalman filtering lo estimate the acceleration of the occupant and jerk from the noisy measured acceleration. Filtering to estimate the velocity and displacement of the occupant. Prediction of the velocity and displacement of the occupant after collision. Judgement to trigger the airbag from the above estimated and predicted variables by using neural networks. Simulations of the various conditions were carried out and the results demonstrated the validily of tlie algorithm.","PeriodicalId":283697,"journal":{"name":"Proceedings of the Intelligent Vehicles '93 Symposium","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1993-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115956252","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Vision For Intelligent Road Vehicles","authors":"V. Graefe","doi":"10.1109/IVS.1993.697311","DOIUrl":"https://doi.org/10.1109/IVS.1993.697311","url":null,"abstract":"As part of an effort to demonstrate a fully autonomous road vehicle able to participate in normal freeway traffic, a system of visual object recognition modules was developed within the EUREKA project PROMETHEUS over the last four years. Together, the modules constitute a basis for an intelligent road vehicle, able to recognize traffic situations in real time and to react accordingly. They were implemented on a specially developed robot vision system and extensively tested in real-world scenes on the German Autobahn and on other roads.","PeriodicalId":283697,"journal":{"name":"Proceedings of the Intelligent Vehicles '93 Symposium","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1993-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127112334","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Preview Lateral Control With Machine Vision For Intelligent Vehicle","authors":"K. Tomita, S. Murata, S. Tsugawa","doi":"10.1109/IVS.1993.697371","DOIUrl":"https://doi.org/10.1109/IVS.1993.697371","url":null,"abstract":"This paper describes a new preview lateral control algorithm for an autonomous vehicle with machine vision and experiments with an AGV conducted to demonstrate the feasibility of the algorithm. The algorithm uses the whole information in the two-dimensional field of view, which enables robust and smooth lateral control. Compensation of the delay due to the time for image processing is also considered.","PeriodicalId":283697,"journal":{"name":"Proceedings of the Intelligent Vehicles '93 Symposium","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1993-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127158173","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Real-time Visual Reflex For Autonomous Navigation","authors":"M. Romano, N. Ancona","doi":"10.1109/IVS.1993.697296","DOIUrl":"https://doi.org/10.1109/IVS.1993.697296","url":null,"abstract":"In this paper a well defined case of autonomous car driving in a typical traffic jam condition is addressed. Such a situation has been faced looking at the relation between the rate of change of the area projected on the image plane by a moving object and its time-to-crash (TTC). A visual sensor has been developed which is able to detect expansions or contractions of the area shape, without any explicit computation of the optical flow field. This sensor provides the visual input of an opto- motor reflex, working in real-time, which is able to keep constant in time the distance between the camera an a frontal obstacle. The methodology has been tested by using a mobile platform. The performances of the reflex on real image sequences are shown.","PeriodicalId":283697,"journal":{"name":"Proceedings of the Intelligent Vehicles '93 Symposium","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1993-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128757930","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Control Algorithms For An Autonomous Vehicle","authors":"P. Petrov","doi":"10.1109/IVS.1993.697330","DOIUrl":"https://doi.org/10.1109/IVS.1993.697330","url":null,"abstract":"This paper presents trajectory tracking algorithms for a two-degrees- of- freedom Wheeled Mobile Robot. It describes some results on a feedback control of an autonomous mobile robot.","PeriodicalId":283697,"journal":{"name":"Proceedings of the Intelligent Vehicles '93 Symposium","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1993-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129120357","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}