An all-transputer visual autobahn-autopilot/copilot

Dickmanns, Ernst D.; Behringer, Reinhold; Brudigam, C.; Dickmanns, Dirk; Thomanek, F.; Holt, V. van

doi:10.1109/iccv.1993.378155

“…The first ones are using 2D image models for modelling road edge [1,5,7]. The second approach takes into account the vehicle motion on the road and use 3D road models, those approaches lead to a search space reduction of features [2,3]. Our approach comes from the second category.…”

Section: Introductionmentioning

confidence: 99%

Road Markings Detection and Tracking Using Hough Transform and Kalman Filter

Voisin¹,

Avila²,

Emile³

et al. 2005

Lecture Notes in Computer Science

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Abstract.A lane marking tracking method using Hough Transform and Kalman Filtering is presented. Since the HT is a global feature extraction algorithm, it leads to a robust detection relative to noise or partial occlusion. The Kalman filter is used to track the roadsides which are detected in the image by this HT. The Kalman prediction step leads to predict the road marking parameters in the next frame, so we can apply the detection algorithm in smaller regions of interest, the computional cost is being consequently reduced.

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“…Some of the best known and fully operational systems include the CMU NavLab project (Thorpe, Hebert, Kanade, & Shafer, 1988), the INRIA autonomous car project (Baber, Kolodko, Noel, Parent, & Vlacic, 2005) in France, the VaMoRs (Dickmanns et al, 1993) and VITA projects (Ulmer, 1992) in Germany, and the Personal Vehicle System (PVS) project (Hattori, Hosaka, Taniguchi, & Nakano, 1992) in Japan. Approaches to road following in these and other systems vary drastically.…”

Section: On-road Planningmentioning

confidence: 99%

Motion planning in urban environments

Ferguson

¹

,

Howard

²

,

Likhachev

³

2008

Journal of Field Robotics

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We present the motion planning framework for an autonomous vehicle navigating through urban environments. Such environments present a number of motion planning challenges, including ultrareliability, high-speed operation, complex intervehicle interaction, parking in large unstructured lots, and constrained maneuvers. Our approach combines a model-predictive trajectory generation algorithm for computing dynamically feasible actions with two higher level planners for generating long-range plans in both on-road and unstructured areas of the environment. In the first part of this article, we describe the underlying trajectory generator and the on-road planning component of this system. We then describe the unstructured planning component of this system used for navigating through parking lots and recovering from anomalous on-road scenarios. Throughout, we provide examples and results from "Boss," an autonomous sport utility vehicle that has driven itself over 3,000 km and competed in, and won, the DARPA Urban Challenge. C 2008 Wiley Periodicals, Inc.

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“…Some of the best known and fullyoperational systems include the CMU NavLab project [7], the INRIA autonomous car project [8] in France, the VaMoRs [9] and VITA projects [10] in Germany, and the Personal Vehicle System (PVS) project [11] in Japan. Approaches to road following in these and other systems vary drastically.…”

Section: A On-road Planningmentioning

confidence: 99%

Motion planning in urban environments: Part II

Ferguson

¹

,

Howard

²

,

Likhachev

³

2008

2008 IEEE/RSJ International Conference on Intelligent Robots and Systems

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We present the motion planning framework for an autonomous vehicle navigating through urban environments. Such environments present a number of motion planning challenges, including ultra-reliability, high-speed operation, complex inter-vehicle interaction, parking in large unstructured lots, and constrained maneuvers. Our approach combines a model-predictive trajectory generation algorithm for computing dynamically-feasible actions with two higher-level planners for generating long range plans in both on-road and unstructured areas of the environment. In this Part II of a two-part paper, we describe the unstructured planning component of this system used for navigating through parking lots and recovering from anomalous on-road scenarios. We provide examples and results from ldquoBossrdquo, an autonomous SUV that has driven itself over 3000 kilometers and competed in, and won, the Urban Challenge. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Pennsylvania's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it. Abstract-We present the motion planning framework for an autonomous vehicle navigating through urban environments. Such environments present a number of motion planning challenges, including ultra-reliability, high-speed operation, complex inter-vehicle interaction, parking in large unstructured lots, and constrained maneuvers. Our approach combines a model-predictive trajectory generation algorithm for computing dynamically-feasible actions with two higher-level planners for generating long range plans in both on-road and unstructured areas of the environment. In this Part II of a two-part paper, we describe the unstructured planning component of this system used for navigating through parking lots and recovering from anomalous on-road scenarios. We provide examples and results from "Boss", an autonomous SUV that has driven itself over 3000 kilometers and competed in, and won, the Urban Challenge.

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An all-transputer visual autobahn-autopilot/copilot

Cited by 36 publications

References 6 publications

Road Markings Detection and Tracking Using Hough Transform and Kalman Filter

Road Markings Detection and Tracking Using Hough Transform and Kalman Filter

Motion planning in urban environments

Motion planning in urban environments: Part II

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