Lane-change detection based on individual driving style

This paper presents a novel method to detect when a vehicle is changing lanes by considering individual driving styles. Some previous works have focused on the relation between lane changes and driving styles; however, the detection method of lane changes while considering driving styles is still not reported. The proposed method extracts a new feature to appropriately describe driving styles based on drivers' risk taking behavior when following the preceding vehicle. Based on the driving style of the target vehicle, performing a lane change is estimated at each time step. The determined driving style is used as input for the lane-change detection method based on the gap acceptance model, and it is integrated with the detection method based on vehicle movements. Experiments are used to demonstrate that the proposed method considerably improves detection performance.     

Adaptive driver model using a neural network

An adaptive driver model for longitudinal movements of a vehicle has been developed. It incorporates a conventional feedback brake controller, and both fixed and adaptive neural network controllers to produce the throttle demand. It has been interfaced with a vehicle model in a Simulink environment, and simulation studies indicate a high level of performance. Implementation of the adaptive driver model within a real-time environment has also been realized successfully. This work was presented in part at the 7th International Symposium on Artificial Life and Robotics, Oita, Japan, January 16–18, 2002     

Solving the minimum bisection problem using a biologically inspired computational model

The traditional trend of DNA computing aims at solving computationally intractable problems. The minimum bisection problem (MBP) is a well-known NP-hard problem, which is intended to partition the vertices of a given graph into two equal halves so as to minimize the number of those edges with exactly one end in each half. Based on a biologically inspired computational model, this paper describes a novel algorithm for the minimum bisection problem, which requires a time cost and a DNA strand length that are linearly proportional to the instance size.     

Characterizing human driver characteristics using an artificial neural network and a theoretical model

Human drivers seem to have different characteristics, so different drivers often yield different results from the same drivingmode tests with identical vehicles and same chassis dynamometer. However, drivers with different experiences often yieldsimilar results under the same driving conditions. If the features of human drivers are known, the control inputs to each driver,including warnings, will be customized to optimize each man–machine vehicle system. Therefore, it is crucial to determinehow to characterize human drivers quantitatively. This study proposes a method to estimate the parameters of a theoreticalmodel of human drivers. Themethod uses an artificial neural network (ANN) model and a numerical procedure to interpret theidentified ANN models theoretically. Our approach involves the following process. First, we specify each ANN driver modelthrough chassis dynamometer tests performed by each human driver and vehicle. Subsequently, we obtain the parameters ofa theoretical driver model using the ANN model for the corresponding driver. Specifically, we simulate the driver’s behaviorsusing the identified ANN models with controlled inputs. Finally, we estimate the theoretical driver model parameters usingthe numerical simulation results. A proportional-integral-differential (PID) control model is used as the theoretical model.The results of the parameter estimation indicate that the PID driver model parameter combination can characterize humandrivers. Moreover, the results suggest that vehicular factors influence the parameter combinations of human drivers.     

Resource allocation on computational grids using a utility model and the knapsack problem

This work introduces a utility model (UM) for resource allocation on computational grids and formulates the allocation problem as a variant of the 0–1 multichoice multidimensional knapsack problem. The notion of task-option utility is introduced, and it is used to effect allocation policies. We present a variety of allocation policies, which are expressed as functions of metrics that are both intrinsic and external to the task and resources. An external user-defined credit-value metric is shown to allow users to intervene in the allocation of urgent or low priority tasks. The strategies are evaluated in simulation against random workloads as well as those drawn from real systems. We measure the sensitivity of the UM-derived schedules to variations in the allocation policies and their corresponding utility functions. The UM allocation strategy is shown to optimally allocate resources congruent with the chosen policies.     

Biomechanical response of the musculoskeletal system to whole body vibration using a seated driver model

ObjectiveThis study aimed to assess the effects of backrest inclination and vibration frequency on muscle activity in a dynamic environment using a musculoskeletal model.MethodThe muscle activity modeling method was used to analyze a full body musculoskeletal system of a seated person with a public domain rigid body model in an adjustable car seat. This model was established using AnyBody Modeling System, based on the inverse dynamic approach. And the min/max criterion in dealing with the muscle redundancy problem. Ten healthy subjects were exposed to whole body vibration (WBV) with five frequencies (3, 4.5, 6, 7, and 8 Hz) in the vertical direction in a randomized order on three separate days. The displacement of the seat-pan and head was measured using a hybrid Polaris spectra system to obtain the seat-to-head (STH) transmissibility. Muscle oxygenation was measured using near-infrared spectroscopy. The validity of the model was tested using STH transmissibility and muscle oxygenation.ResultsIncreased vibration frequency caused high muscle activities of the abdomen and the right leg with a backrest forward inclination angle. The muscle activities of the left leg decreased at a backrest backward inclination except at inclination angles of 15° and 30°. Muscle activity of the lumbar suddenly increased at a backrest inclination angle of 5° and vibration frequency of 5 Hz. Muscle activities were higher under vibration than that without vibration.ConclusionVibration frequency significantly affected the muscle activity of the lumbar area. Likewise, the inclination degree of the backrest significantly affected the muscle activities of the right leg and the abdomen. The combination of vibration and forward inclination of the backrest can be used to maximize the muscle activity of the leg, similar to the abdomen and lumbar muscles.Relevance to the industryThe musculoskeletal model established in the present study provides a method that can be used to investigate the biomechanical response of seated drivers to WBV. This model helps protect drivers from occupational injury.     

Real-time traffic incident detection using a probabilistic topic model

Traffic congestion occurs frequently in urban settings, and is not always caused by traffic incidents. In this paper, we propose a simple method for detecting traffic incidents from probe-car data by identifying unusual events that distinguish incidents from spontaneous congestion. First, we introduce a traffic state model based on a probabilistic topic model to describe the traffic states for a variety of roads. Formulas for estimating the model parameters are derived, so that the model of usual traffic can be learned using an expectation–maximization algorithm. Next, we propose several divergence functions to evaluate differences between the current and usual traffic states and streaming algorithms that detect high-divergence segments in real time. We conducted an experiment with data collected for the entire Shuto Expressway system in Tokyo during 2010 and 2011. The results showed that our method discriminates successfully between anomalous car trajectories and the more usual, slowly moving traffic patterns.     

Topic change point detection using a mixed Bayesian model

Data Mining and Knowledge Discovery - Dynamic text documents, including news articles, user reviews, and blogs, are now commonly encountered in many fields. Accordingly, the topics underlying text...     

Construction of a parallel computational model by composition of computational facilities

We consider a general algorithm for constructing a parallel numerical model by composition of several computational objects. The only requirement for the applicability of the proposed algorithm is for the disturbance propagation speed to be finite. We present one possible approach to map a numerical model onto an object-oriented program model. Some results of numerical experiments on solving the heat conduction equation in linear and nonlinear cases are discussed.     

基于KLT算法的驾驶员疲劳检测方法

为了有效解决驾驶员过度疲劳引发交通事故的问题,提出了一种基于KLT(kanade-lucas-tomasi)算法的驾驶员视觉疲劳检测算法.首先在驾驶员眼睛周围选取若干个特征点,用KIT算法对这些特征点进行跟踪,从而来定位人眼,然后将定位到的人眼与模板进行比较,采用阈值的方法,计算二者的相似度,从而判断眼睛的状态.针对该方法受光照影响较大的问题,又提出背景模型对该算法作了较大改进.通过对采集到的视频流的处理,结果表明该方法有较好的实用性.     

驾驶员疲劳检测系统的研究

为了减少由于驾驶员疲劳驾驶引起的交通事故,提出驾驶员疲劳状态检测系统的方案。使用3×3中值滤波去除噪声和光照对图像的影响,通过对AdaBoost算法的强分类器训练算法改进、级联分类器优化实现人脸的快速检测,在检测到的人脸区域,通过积分灰度投影和从粗到细改进的模板匹配方法对人眼进行准确定位;通过PERCLOS、眼睛闭合时间、眼睛眨眼频率、嘴巴张开程度、头部运动的计算,进行驾驶员疲劳程度的综合判定。实验结果表明,该方法准确率高,兼具了良好的实时性和鲁棒性。     

Dynamic set point model for driver alert state using digital image processing

Multimedia Tools and Applications - The driver fatigue and lose of attention while driving are the most important causes of traffic accidents. Each year more than one million of deaths occur due to...     

Modeling and performance analysis of a hybrid driver model

For an effective simulation of vehicle motion a realistic driver model is as necessary as the dynamic vehicle model. In this paper, the human perception process is described by discrete event techniques that also allow human deficiencies to be addressed. When a human driver reaches his personal physical limits, his control performance rapidly deteriorates.     

Salient and non-salient fiducial detection using a probabilistic graphical model

Deformable shape detection is an important problem in computer vision and pattern recognition. However, standard detectors are typically limited to locating only a few salient landmarks such as landmarks near edges or areas of high contrast, often conveying insufficient shape information. This paper presents a novel statistical pattern recognition approach to locate a dense set of salient and non-salient landmarks in images of a deformable object. We explore the fact that several object classes exhibit a homogeneous structure such that each landmark position provides some information about the position of the other landmarks. In our model, the relationship between all pairs of landmarks is naturally encoded as a probabilistic graph. Dense landmark detections are then obtained with a new sampling algorithm that, given a set of candidate detections, selects the most likely positions as to maximize the probability of the graph. Our experimental results demonstrate accurate, dense landmark detections within and across different databases.     

Credit card fraud detection using a deep learning multistage model

The Journal of Supercomputing - The banking sector is on the eve of a serious transformation and the thrust behind it is artificial intelligence (AI). Novel AI applications have been already...     

A model of driver behaviour

The driver-vehicle-environment system is characterized by the driver's ability to receive information from the environment and to react upon it by activating the controls of the vehicle. Modelling this system one has to take into account that it is complex. After introducing the model the required operations for the driver's eye and hand movements are demonstrated by means of selected examples drawn from driving-simulator runs. First analyses indicate the principal applicabilities of the fuzzy driver model for further research.     

An automated binary change detection model using a calibration approach

An automated binary change detection model using a threshold-based calibration approach was introduced in the study. The burdensome processes required in binary change detection, including calibration, calculation of accuracy, extraction of optimum threshold(s), generation of a binary change mask, and removal of “salt-and-pepper” noise were integrated and automated in the model. For practical purpose, the model was implemented as a dynamic linked library in ESRI ArcMap 9.1 using Visual Basic. This study demonstrated the model with a variety of single and multiple variables (layers) extracted from multiple-date QuickBird imagery for three study sites in Las Vegas, NV and two study sites in Tucson, AZ. The use of multiple variables in binary change detection resulted in significantly better performance than single variables.     

基于模糊参数的高斯边缘检测模型方法研究

对图像边缘提取的问题进行了研究,针对以往边缘提取算法容易造成边缘断裂,提取较多噪声的缺点,提出一种利用模糊逻辑函数在全局上对图像进行增强,在局部进行边缘提取的算法。定义一个模糊逻辑函数,利用类似直方图均衡化的方法,对变换后的模糊逻辑图像进行对比度增强操作;在增强的图像上,对模糊逻辑函数进行局部非线性投影变换,并在变换基础上进行边缘图像的阈值检测,从而得到最终的边缘图像。在进行非线性变换的同时,利用梯度迭代法对非线性参数进行优化,从而保证获得最优的高斯模型,并提取出边缘信息。该方法在自动获得最优的系统参数同时,能准确有效地提取出图像的边缘,克服噪声对系统的干扰;并能广泛使用在基于边缘信息的图像处理系统和模式识别系统中。