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    <title>Transport Research International Documentation (TRID)</title>
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    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
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    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
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      <title>A computational model of the car driver interfaced with a simulation platform for future Virtual Human Centred Design applications: COSMO-SIVIC</title>
      <link>https://trid.trb.org/View/1681688</link>
      <description><![CDATA[This paper presents the first step of a research programme implemented by IFSTTAR in order to develop an integrative simulation platform able to support a Human Centred Design (HCD) method for virtual design of driving assistances. This virtual platform, named COSMO-SiVIC, implements a COgnitive Simulation MOdel of the DRIVEr (i.e. COSMODRIVE) into a Vehicle-Environment-Sensors platform (named SiVIC, for Simulateur Ve  hicule-Infrastructure-Capteur). From this simulation tool based on a computational driver model, the design costs of driving assistances is expected to reduce in the future, and the end-users needs during the design process are also better taken into account. This article is mainly focussed on the description of the driver model developed and implemented on the SiVIC virtual platform, which is only the first step towards a future Virtual HCD integrated tool. The first section will discuss the research context and objective, and the second one will present the theoretical background in cognitive sciences supporting our driver modelling approach. Then, the SiVIC tool is used in this research as a methodological and technical support for both empirical data collection among human drivers and as a virtual road environment to be interfaced with the COSMODRIVE model. In the result section, the functional architecture of COSMO-SIVIC (based on three complementary modules of Perception , Decision and Action ) will be described, and an example of virtual simulation of human driver's errors due to visual distraction while driving will be presented. The perspectives concerning future use of COSMO-SIVIC for virtual HCD will be then discussed in the conclusion section.]]></description>
      <pubDate>Tue, 28 Jan 2020 16:16:53 GMT</pubDate>
      <guid>https://trid.trb.org/View/1681688</guid>
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      <title>PARALLEL ATTENTIVE VISUAL TRACKING</title>
      <link>https://trid.trb.org/View/409308</link>
      <description><![CDATA[The research reported here addresses the problem of detecting and tracking independently moving objects from a moving observer in real- time, using corners as object tokens.  Corners are detected using the Harris corner detector, and local image-plane constraints are employed to solve the correspondence problem.  The approach relaxes the restrictive static-world assumption conventionally made, and is therefore capable of tracking independently moving and deformable objects.  Tracking is performed without the use of any 3-dimensional motion model.  The technique is novel in that, unlike traditional feature-tracking algorithms where feature detection and tracking is carried out over the entire image-plane, here it is restricted to those areas most likely to contain meaningful image structure.  Two distinct types of instantiation regions are identified, these being the "focus- of-expansion" region and "border" regions of the image- plane.  The size and location of these regions are defined from a combination of odometry information and a limited knowledge of the operating scenario.  The algorithms developed have been tested on real image sequences taken from typical driving scenarios. Implementation of the algorithm using T800 Transputers has shown that near-linear speedups are achievable, and that real-time operation is possible (half-video rate has been achieved using 30 processing elements).  (A)]]></description>
      <pubDate>Fri, 09 Sep 1994 00:00:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/409308</guid>
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      <title>A SCENE SEGMENTER: VISUAL TRACKING OF MOVING VEHICLES</title>
      <link>https://trid.trb.org/View/409309</link>
      <description><![CDATA[In this paper the image processing system ASSET (A Scene Segmenter Establishing Tracking) is described.  ASSET receives a sequence of video images taken by a possibly moving camera and segments each image into separately moving objects using image motion.  The moving objects are tracked, and their outlines are accurately estimated.  The ASSET system provides a useful source of world information, for example, in the area of autonomous vehicle guidance.  (A)]]></description>
      <pubDate>Fri, 09 Sep 1994 00:00:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/409309</guid>
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    <item>
      <title>AUTONOMOUS ROAD VEHICLE NAVIGATION</title>
      <link>https://trid.trb.org/View/409310</link>
      <description><![CDATA[This paper describes a new modular hardware design for an autonomous land vehicle, together with techniques for navigation and obstacle avoidance.  A "bootstrap" algorithm is described which aligns the vehicle from an arbitrary starting point using the road's vanishing point.  An algorithm to perform navigation along a structured road is then described.  This is achieved by modelling the white lane-boundary marking using six parameters which are updated iteratively from frame to frame using a non-linear least-squares technique.  The obstacle- detection method is based on frame differencing.  Using data from the vehicle odometry sensors, a motion-corrected version of the difference between frames is produced, in which objects projecting up from the ground plane are easily identified. (A)]]></description>
      <pubDate>Fri, 09 Sep 1994 00:00:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/409310</guid>
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    <item>
      <title>ROAD RECOGNITION WITH A NEURAL NETWORK</title>
      <link>https://trid.trb.org/View/409311</link>
      <description><![CDATA[This paper presents a neural-network model that is capable of recognising roads in colour images of urban and rural road scenes. The network is trained to classify the visible objects in an image, given a set of hand-labelled examples.  The two problems of segmentation and recognition are separated through the use of "ideal" segmentations, allowing the performance of the recognition method to be assessed independently of the effects of using an imperfect real segmentation process.  The generalisation performance of the method is measured using unseen test data.  Results for both on-road and off-road viewpoints are presented. The system is found to recognise correctly approx.  70% of the regions, and approx.  90% of the area of the images. (A)]]></description>
      <pubDate>Fri, 09 Sep 1994 00:00:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/409311</guid>
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    <item>
      <title>PROGRAM FOR TIMETABLE COMPILATION BY A LOOK-AHEAD METHOD</title>
      <link>https://trid.trb.org/View/19839</link>
      <description><![CDATA[The problem of timetable compilation for a single-track railway is a job-shop scheduling problem but with differences that handicap the generation of feasible solutions.  The paper states the problem and describes the algorithm and the experimental results.  The idea of the algorithm is that a feasible solution is obtained by successive resolving of conflicts between trains, this process being interpreted as the generation of some tree T. The way to resolve a conflict is selected by a lookahead method which enables one to obtain good enough solutions by using a very rough estimate function.  One specific feature of the algorithm is that the lookahead tree T is not a subtree of T; the other is the culs-de-sac on trees T and T.  When it reaches a cul-de-sac, the algorithm augments the tree with additional nodes.]]></description>
      <pubDate>Sat, 31 Jul 1976 00:00:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/19839</guid>
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