Building world models for mobile platforms using heterogeneous sensors fusion and temporal analysis

Beauvais, Michel; Kreucher, Chris; Lakshmanan, Sridhar

doi:10.1109/itsc.1997.660480

Cited by 12 publications

(10 citation statements)

References 6 publications

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“…The intelligent vehicles community has been very active in the image fusion area as well [14], [15], [26], [27]. The problem of co-registering vehicle navigation data (proximity of the vehicle ahead, map of the scene ahead, host vehicle position, speed and heading direction, etc.)…”

Section: Multisensor Image Fusionmentioning

confidence: 99%

“…• Define the Cauchy density function (14) where is a pre-determined constant to control the width of the Cauchy density function. It takes different values when evaluating the contribution of the magnitude and direction of the gradient at a pixel to the likelihood.…”

Section: Optical Imaging Likelihoodmentioning

confidence: 99%

“…The same framework can be extended to fuse data from other modalities such as from infra-red, ultrasound or single-beam radar sensors. We refer the reader to [14] and [15] for examples.…”

Section: Introductionmentioning

confidence: 99%

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Simultaneous detection of lane and pavement boundaries using model-based multisensor fusion

Lakshmanan

Hero

2000

IEEE Trans. Intell. Transport. Syst.

Self Cite

111

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Abstract-This paper treats a problem arising in the design of intelligent vehicles: automated detection of lane and pavement boundaries using forward-looking optical and radar imaging sensors mounted on an automobile. In previous work, lane and pavement boundaries have always been located separately. This separate detection strategy is problematic in situations when either the optical or the radar image is too noisy.In this paper, we propose a Bayesian multisensor image fusion method to solve our boundary detection problem. This method makes use of a deformable template model to globally describe the boundaries of interest. The optical and radar imaging processes are described with random field likelihoods. The multisensor fusion boundary detection problem is reformulated as a joint MAP estimation problem. However, the joint MAP estimate is intractable, as it involves the computation of a notoriously difficult normalization constant, also known as the partition function. Therefore, we settle for the so-called empirical MAP estimate, as an approximation to the true MAP estimate. Several experimental results are provided to demonstrate the efficacy of the empirical MAP estimation method in simultaneously detecting lane and pavement boundaries. Fusion of multi-modal images is not only of interest to the intelligent vehicles community, but to others as well, such as biomedicine, remote sensing, target recognition. The method presented in this paper is also applicable to image fusion problems in these other areas.

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Section: Multisensor Image Fusionmentioning

confidence: 99%

Section: Optical Imaging Likelihoodmentioning

confidence: 99%

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Simultaneous detection of lane and pavement boundaries using model-based multisensor fusion

Lakshmanan

Hero

2000

IEEE Trans. Intell. Transport. Syst.

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111

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“…Most research groups face this problem using highly sophisticated image filtering algorithms. For the most part, gray-level images are used, but in some cases color images are used: this is the case of the MOSFET (Michigan Off-road Sensor Fusing Experimental Testbed) autonomous vehicle which uses a color segmentation algorithm that maximizes the contrast between lane markings and road [22].…”

Section: Lane Detectionmentioning

confidence: 99%

“…In the MOSFET autonomous vehicle, for instance, lane markings are modeled as parabolas [22]. A simplified Hough transform is used to accomplish the fitting procedure.…”

Section: Lane Detectionmentioning

confidence: 99%

Vision-based intelligent vehicles: State of the art and perspectives

Bertozzi

Broggi

Fascioli

2000

Robotics and Autonomous Systems

383

179

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Recently, a large emphasis has been devoted to Automatic Vehicle Guidance since the automation of driving tasks carries a large number of benefits, such as the optimization of the use of transport infrastructures, the improvement of mobility, the minimization of risks, travel time, and energy consumption. This paper surveys the most common approaches to the challenging task of Autonomous Road Following reviewing the most promising experimental solutions and prototypes developed worldwide using AI techniques to perceive the environmental situation by means of artificial vision.The most interesting results and trends in this field as well as the perspectives on the evolution of intelligent vehicles in the next decades are also sketched out.

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Lane marking detection via deep convolutional neural network

et al. 2018

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Research on Faster R-CNN has recently witnessed the progress in both accuracy and execution efficiency in detecting objects such as faces, hands or pedestrians in photograph or video. However, constrained by the size of its convolution feature map output, it is unable to clearly detect small or tiny objects. Therefore, we presented a fast, deep convolutional neural network based on a modified Faster R-CNN. Multiple strategies, such as fast multi-level combination, context cues, and a new anchor generating method were employed for small object detection in this paper. We demonstrated performance of our algorithm both on the KITTI-ROAD dataset and our own traffic scene lane markings dataset. Experiments demonstrated that our algorithm obtained better accuracy than Faster R-CNN in small object detection.

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Building world models for mobile platforms using heterogeneous sensors fusion and temporal analysis

Cited by 12 publications

References 6 publications

Simultaneous detection of lane and pavement boundaries using model-based multisensor fusion

Simultaneous detection of lane and pavement boundaries using model-based multisensor fusion

Vision-based intelligent vehicles: State of the art and perspectives

Lane marking detection via deep convolutional neural network

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