Development of a Vision-Based Driver Assistance System with Lane Departure Warning and Forward Collision Warning Functions

Liu, Jingfu; Su, Yi‐Feng; Ko, Ming-Kuan; Yu, Pen-Ning

doi:10.1109/dicta.2008.78

Cited by 44 publications

(15 citation statements)

References 6 publications

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“…The results are shown in Table 7 and compared with recent vehicle detection methods, such as Klette and Ahn's DAS, 30 Liu and Su's DAS systems 31 and Le Meur et al spatio-temporal model. 36 In Table 6, we give our comparisons by a quantitative way.…”

Section: Performance Evaluation With Other State-of-the-art Models Onmentioning

confidence: 97%

Building up a Bio-Inspired Visual Attention Model by Integrating Top-Down Shape Bias and Improved Mean Shift Adaptive Segmentation

Yue

2015

Int. J. Patt. Recogn. Artif. Intell.

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The driver-assistance system (DAS) becomes quite necessary in-vehicle equipment nowadays due to the large number of road tra±c accidents worldwide. An e±cient DAS detecting hazardous situations robustly is key to reduce road accidents. The core of a DAS is to identify salient regions or regions of interest relevant to visual attended objects in real visual scenes for further process. In order to achieve this goal, we present a method to locate regions of interest automatically based on a novel adaptive mean shift segmentation algorithm to obtain saliency objects. In the proposed mean shift algorithm, we use adaptive Bayesian bandwidth to¯nd the convergence of all data points by iterations and the k-nearest neighborhood queries. Experiments showed that the proposed algorithm is e±cient, and yields better visual salient regions comparing with ground-truth benchmark. The proposed algorithm continuously outperformed other known visual saliency methods, generated higher precision and better recall rates, when challenged with natural scenes collected locally and one of the largest publicly available data sets. The proposed algorithm can also be extended naturally to detect moving vehicles in dynamic scenes once integrated with top-down shape biased cues, as demonstrated in our experiments.

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Section: Performance Evaluation With Other State-of-the-art Models Onmentioning

confidence: 97%

Building up a Bio-Inspired Visual Attention Model by Integrating Top-Down Shape Bias and Improved Mean Shift Adaptive Segmentation

Yue

2015

Int. J. Patt. Recogn. Artif. Intell.

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“…The lane boundary filter was used to identify the lane boundary pixels in the images first and then the Hough transform was used to find lane boundary lines. A vision-based system capable of detecting the lane departure using image processing and the road geometry model was developed (Liu et al, 2008). A gradient method was applied to detect the both lane marks.…”

Section: Introductionmentioning

confidence: 99%

A two-stage-training support vector machine approach to predicting unintentional vehicle lane departure

Albousefi

Ying

Filev

et al. 2016

Journal of Intelligent Transportation Systems

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Advanced driver assistance systems, such as unintentional lane departure warning systems, have recently drawn much attention and efforts. In this study, we explored utilizing the nonlinear binary support vector machine (SVM) technique to predict unintentional lane departure, which is innovative as the SVM methodology has not previously been attempted for this purpose in the literature. Furthermore, we developed a two-stage training scheme to improve SVM's prediction performance in terms of minimization of the number of false positive prediction errors. Experiment data generated by VIRTTEX, a hydraulically powered 6-degrees-of-freedom moving base driving simulator at Ford Motor Company were used. All the vehicle variables were sampled at 50 Hz and there were 16 drowsy drivers (about three-hour driving per subject) and six control drivers (approximately 20 minutes driving each). A total of 3,508 unintentional lane departures occurred for the drowsy drivers and 23 for the control drivers. Our study involving these 22 drivers with a total of over 7.5 million prediction decisions demonstrates that: (1) Downloaded by [La Trobe University] at 03:56 16 June 2016 ACCEPTED MANUSCRIPT ACCEPTED MANUSCRIPT 2 excellent SVM prediction performance, measured by numbers of false positives (i.e., falsely predicted lane departures) and false negatives (i.e., lane departures failed to be predicted), wereachieved when the prediction horizon was 0.6 s or less, (2) lateral position and lateral velocity worked the best as SVM input variables among the nine variable sets that we explored, and (3) the radial basis function performed the best as the SVM kernel function.

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“…Passive optical sensors have been used in the automotive industry for tasks such as management and off-board traffic observation [1]. The CV technique is often used in driver assistance and traffic sign detection in onboard systems [2]. Also, the augmented reality (AR) resulting from CV functions, improves the real environment by using virtual elements and allows diversified functions such as guided order picking or upkeep efforts [3,4].…”

Section: Introductionmentioning

confidence: 99%

A Real-Time Marker-Based Visual Sensor Based on a FPGA and a Soft Core Processor

Tayara

Ham

Chong

2016

Sensors

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This paper introduces a real-time marker-based visual sensor architecture for mobile robot localization and navigation. A hardware acceleration architecture for post video processing system was implemented on a field-programmable gate array (FPGA). The pose calculation algorithm was implemented in a System on Chip (SoC) with an Altera Nios II soft-core processor. For every frame, single pass image segmentation and Feature Accelerated Segment Test (FAST) corner detection were used for extracting the predefined markers with known geometries in FPGA. Coplanar PosIT algorithm was implemented on the Nios II soft-core processor supplied with floating point hardware for accelerating floating point operations. Trigonometric functions have been approximated using Taylor series and cubic approximation using Lagrange polynomials. Inverse square root method has been implemented for approximating square root computations. Real time results have been achieved and pixel streams have been processed on the fly without any need to buffer the input frame for further implementation.

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Development of a Vision-Based Driver Assistance System with Lane Departure Warning and Forward Collision Warning Functions

Cited by 44 publications

References 6 publications

Building up a Bio-Inspired Visual Attention Model by Integrating Top-Down Shape Bias and Improved Mean Shift Adaptive Segmentation

Building up a Bio-Inspired Visual Attention Model by Integrating Top-Down Shape Bias and Improved Mean Shift Adaptive Segmentation

A two-stage-training support vector machine approach to predicting unintentional vehicle lane departure

A Real-Time Marker-Based Visual Sensor Based on a FPGA and a Soft Core Processor

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