A Lane Departure Warning System Based on Virtual Lane Boundary

The development of tools for automated analysis of data collected for mine countermeasures (MCM) search, classify and map (SCM) has predominantly focused on automated target recognition for individual mines. As MCM target detection becomes more automated, detecting spatial patterns in the positioning of mines should follow. Natural restrictions on mine deployment such as depth and bottom contour as well as mine deployment methodology and protocol effectively create mine line patterns that are typical to coastal areas. We have developed a mine line detection algorithm that uses nonparametric spatial analysis to identify mine-like contacts (MLCs) that fit into mine line patterns dictated by standard mine laying protocol in coastal regions. In general, a set of MLCs reported from a mine detection sensor will consist of both actual mines and false alarms; the algorithm classifies every MLC in this set into one of three classes: fixed — the MLCψfits into a mine line pattern with fixed mine spacing; random -the MLCψfits into a mine line pattern with random mine spacing; or false alarm-ψ-the MLCψdoes not fit into a mine line pattern and is subsequently not considered a mine. The latest version of the algorithm utilizes nonparametric techniques and requires no knowledge of either the detection sensor's probability of detection (P d ) or probability of false alarm (P fa ). A nonparametric anomaly detector first identifies regions with unusually high MLC density for more detailed analysis. Suspected regions are subsequently analyzed for unusually regular patterns of MLCs. The probabilities that the observed target densities and target patterns could occur simply by chance are estimated by comparison with results from nearby areas using basic information about mine deployment. To ensure that a mine line's full extent is captured, the algorithm attempts to grow a line after it is detected. And a more detailed probability analysis distinguishes random mine lines from fixed-spaced mine lines. Extensive Matlab simulations characterize the algorithm's performance across a wide range of operating environments and simulated minefield configurations. In total, ten replications each of 1,367 different configurations have been evaluated. These configurations include three different water depths (10 ft., 6 ft. and 200 ft.), six false notional alarm rates (1, 2,5,10,15 and 20 false alarms per sq. NM), three false alarm distributions (uniform, sloped, and striped), three sensor probabilities of detections (0.4, 0.6 and 0.9), three target confidence scores (0.6,0.8 and 1.0) and four false alarm confidence scores (0.5, 0.6, 0.8 and 1.0). The range of simulation parameters was selected in part as an attempt to push the algorithm past its acceptable performance limits. However, the algorithm performed remarkably well even under the worst conditions. For MLC sets with false alarm rates as high as 20 per square nautical mile, the algorithm correctly classifies over 98% of the MLCs as mines and detects 97% of the mine lines. Furthermore, it reduces the false alarm rate from 20 to 0.2 per square nautical mile with a false mine line rate of 0.14 false lines per square nautical mile. The algorithm achieves even lower false alarm rates when the initial false alarm rate is less than 20. The automated mine line detection discovers many obscure lines that are virtually undetectable to the naked eye.

Lane departure warning system based on surrounding view is a human-made decision such as solution to avoid lane departure fatalities with low cost and high reliability. In this paper, the key parameters of time to lane crossing (TLC) and distance to lane crossing (DLC) are all calculated from image processing(the corner detect and the optic flow). In the warning module, two strategies are proposed to improve the reliability of the system. After adjusting the threshold values on abundance frames of consecutive images, the refined threshold of the two strategies are determined. Finally, the proposed strategies are incorporated into our lane departure warning system to be tested in off-line simulation. The results indicate the efficiency and feasibility of our system.

Because of the increasing number of population of vehicles, the road traffic accidents are becoming more and more serious in recent years. Hence, improving driver assistance systems for security has become an important area of research. This paper presents a robust lane detection and tracking system based on monocular vision. We use the Lane Departure Warning (LDW) systems to detect the position of the vehicle with respect to the lane boundary. An algorithm to detect the road lane marking and to control the direction of the vehicle is proposed. Our work consists in establishing a Region Of Interest (ROI) of the road images, data pre-processing using the Gaussian filter and then apply the Canny edge detector to enhance lane boundaries. A method to extract lane boundaries based on color information and image segmentation by using the histogram threshold, Hough transform is proposed, and the current vehicle position is obtained. In such a case, we can decide if the vehicle is doing lane departure used on the vehicle's position, the system sends a warning message to the driver. Our proposed algorithm works accurately with various lighting conditions as well as on different road types.

In human presence detection for Internet of Things (IoT) applications, Wi-Fi channel state information (CSI)-based methods offer significant advantages in cost-effectiveness and privacy preservation. Existing approaches typically rely on feature engineering or deep learning techniques to process CSI data. However, these methods often struggle to accurately model the complex, probabilistic nature of CSI measurements, particularly in environments with nonhuman moving elements. This leads to suboptimal detection probabilities under low false alarm rate (FAR) conditions. Therefore, this article introduces Wi-CCFAR, a controllable FAR human presence detector utilizing Wi-Fi CSI data. First, we employ a normalizing flow network with spline transformations for data modeling, capturing the complexity of CSI measurement distribution. Second, to address the inherent limitation of the same input and output dimensions in normalizing flow networks, we introduce an efficient feature extractor, which reduces input dimensions and computational requirements while maintaining high detection probabilities by preserving essential information. Last, we implement an enhanced information bottleneck loss function, which balances data fitness and class divergence, to achieve favorable detection probabilities under low FAR conditions. Experimental results demonstrate that Wi-CCFAR outperforms conventional methods, achieving higher detection probabilities with lower false alarm probabilities, even in challenging scenarios with various interferences such as moving objects and multiple occupants.

Many different demands can be made of intrusion detection systems. An important requirement is that it be effective i.e. that it should detect a substantial percentage of intrusions into the supervised system, while still keeping the false alarm rate at an acceptable level.This paper aims to demonstrate that, for a reasonable set of assumptions, the false alarm rate is the limiting factor for the performance of an intrusion detection system. This is due to the base-rate fallacy phenomenon, that in order to achieve substantial values of the Bayesian detection rate, P(Intrusion|Alarm), we have to achieve—a perhaps unattainably low—false alarm rate.A selection of reports of intrusion detection performance are reviewed, and the conclusion is reached that there are indications that at least some types of intrusion detection have far to go before they can attain such low false alarm rates.

A novel semantic Minimum Bayes ternary hypothesis detector based on the Rayleigh distribution is presented to decrease detection false alarm rate of synthetic aperture radar image. The method not only employs the strong scattering features of the target which is used in constant false alarm rate (CFAR) but employs the shadow features of the target which is used in the Power Ratio detector. Furthermore, target, shadow, and clutter regions are merged into the ternary image to estimate Rayleigh distribution parameters and weights. Meanwhile, the semantic relationship between the strong scattering features and the shadow features is established to partly reduce the false alarm targets. By the experiments on the Mini-SAR image of the 4-in resolution, it is shown that our method outperforms the semantic CFAR method and CFAR algorithm by a much lower false alarm rate.

This paper focuses on a night time driving performance evaluation of an image-based lane departure warning (LDW) system which was previously developed by the author [25, 26]. In the previous study, road testing was only conducted under the day time driving condition. So, the main focus here is to conduct night time driving evaluations. This system uses the Lucas-Kanada (L-K) optical flow and the Hough transform methods approach. Based on the status of the vehicle deviating from its heading lane, the method integrates both techniques to establish an operation algorithm to determine whether a warning signal should be issued. The L-K optical flow tracking is used when the lane boundaries cannot be detected, while the lane detection technique is used when they become available. Even though both techniques are used in the system, only one method is activated at any given time because each technique has its own advantages and also disadvantages. The image-based LDW system was road tested on rural highways and this paper briefly presents our night time driving test results.

Development of an algorithm to discriminate between valid and false alarms in a loose-parts monitoring system

Many different demands can be made of intrusion detection systems. An important requirement is that an intrusion detection system be effective ; that is, it should detect a substantial percentage of intrusions into the supervised system, while still keeping the false alarm rate at an acceptable level. This article demonstrates that, for a reasonable set of assumptions, the false alarm rate is the limiting factor for the performance of an intrusion detection system. This is due to the base-rate fallacy phenomenon, that in order to achieve substantial values of the Bayesian detection rate P(Intrusion***Alarm) , we have to achieve a (perhaps in some cases unattainably) low false alarm rate. A selection of reports of intrusion detection performance are reviewed, and the conclusion is reached that there are indications that at least some types of intrusion detection have far to go before they can attain such low false alarm rates.

Multi-pass search and reconnaissance missions provide unique opportunities for hyperspectral target detection systems to operate at drastically reduced false alarm rates. In the simplest example, confirmed false alarms generated by an anomaly detector on an initial pass can be archived for future reference. A more sophisticated approach captures false alarm signatures to update background clutter models that inform detection algorithms. Or if detections are confirmed as targets on one pass, then matched-filter maps based on their spectra can be compared over time to monitor changes. As a final example, when sub-pixel registration is feasible, multi-temporal spectral covariance relations can be estimated from the data and used to detect anomalous changes at low false alarm rates, using no target signature information. All but the simplest of such methods require that the spectral evolution of a terrestrial background -- its chromodynamics -- be modeled sufficiently that naturally occurring changes are not confused with unnatural ones. This paper describes several detection paradigms that rely on multi-pass missions. Optimal linear algorithms to predict scene and target evolution are discussed, as are more realistic methods with relaxed operational requirements. One of these, called Covariance Equalization, is shown to perform nearly as well as the minimum error solution based on the matrix Wiener filter, which requires subpixel registration accuracy.

Encouraging compliance with graduated driver licensing restrictions

The amount of data in the field of computer networking is growing rapidly and this urges new challenges in the field of an intrusion detection system (IDS). To handle such increasing volume of data, a new hybrid approach has to be developed to overcome the problems such as high detection rate and low false alarm rate. An intrusion detection system plays a vital role in the detection of malicious attacks. Data mining and machine learning techniques are important and play a vital role in the detection of attacks. This paper mainly focuses on detection rate and false alarm rate and so to resolve these problems a hybrid method, ensemble clustering, has been proposed. This method tries to increase detection rate with lowering false alarm rate. The method has been tested on KDDCup'99 network intrusion dataset and performs well as compared with other algorithms in terms of detection rate and false alarm rate.

The performance of a radar detector may become inaccurate when key parameters of the system are imprecise. Imprecise parameters are modeled here as fuzzy numbers. The fuzzy integrator detector is constructed for a Rician target in Rayleigh background. Results indicate that the precision of the detection performance decreases as parameter imprecision increases. The false alarm rate is particularly sensitive to parameter imprecision at low false alarm rate; 5% imprecision in the parameters can give rise to more than 300% imprecision in the false alarm rate at nominal false alarm rates below 10/sup -5/.

ERP components in Go/Nogo tasks and their relation to inhibition

IntroductionStatistical Process Control (SPC) charts are widely used to detect process shifts, yet classical Shewhart X̄ charts often fail in the presence of outliers and non-normal data distributions. To address these shortcomings, we propose a hybrid approach that integrates discrete wavelet decomposition with support vector machine (SVM) classification for enhanced outlier detection.MethodsThe proposed Dynamic Wavelet-SVM X̄ Chart (DWS-X̄) combines multi-resolution wavelet feature extraction with an SVM classifier to distinguish between in-control and out-of-control states. Its performance was assessed through extensive simulations varying subgroup size, number of subgroups, and contamination levels. Key metrics included Detection Rate (DR), False Alarm Rate (FAR), and Average Run Length (ARL).ResultsSimulation results consistently demonstrated the superiority of the DWS-X̄ chart compared to the traditional X̄ chart. The hybrid chart achieved higher detection rates (above 96%), lower false alarm rates (below 1-6% depending on variability), and shorter ARLs (1.6-5.1). Application to real neonatal heart rate data further confirmed its enhanced sensitivity in identifying abnormal cardiac patterns.DiscussionThe findings indicate that the proposed DWS-X̄ chart provides more reliable monitoring under contamination and high variability, offering earlier detection of abnormal signals with fewer false alarms. This hybrid method holds promise for both industrial quality control and healthcare process monitoring.