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With the integration of EVs into the power grid, smart metering using machine-to-machine (M2M) communication is likely to play an important role in real-time energy management and control. Smart devices embedded with advanced metering infrastructure (AMI) can forecast the energy demand as well as perform energy pricing in real time. In this paper, an artificial neural network (ANN) based intelligent...
Vehicle detection is an essential task in an intelligent vehicle. Despite being a well-studied vision problem, it is unclear how well vehicle detectors generalize to new settings. Specifically, this paper studies the generalization capability of vehicle detectors on a U.S. highway dataset. Two types of models are employed in the experimental analysis, a subcategory aggregate channel features model...
In the past decade, improvements in the production of in-expensive PC equipment and software has permitted more refined real-time signal processing in BCI systems. In the literature, Deep learning concepts have not been applied to EEG data analysis in a systematic manner. This paper applies various existing Deep learning architectures and algorithms for the classification of EEG data applied to eye...
In China, a variety of non-motor vehicles (NM-Vehicle) exist on the road in recent years, some are similar to pedestrian, others are similar to motor vehicle (M-vehicle) in monitor video, which brings a great number of trouble to policeman when case tracking. In this paper, we proposed a traffic object classification pipeline based on state-of-the-art deep convolutional neural networks (CNNs).Unfortunately,...
Light detection and ranging (LIDAR) scanners are essential components of intelligent vehicles capable of autonomous travel. Obstacle detection functions of autonomous vehicles require very low failure rates. With the increasing number of autonomous vehicles equipped with LIDAR scanners to detect and avoid obstacles and navigate safely through the environment, the probability of mutual interference...
Logo identification and classification have received considerable attention from both the machine learning and computer vision communities. Vehicle logo recognition (VLR) is used to recognise accurately the manufacturer of a vehicle by using its iconic logo. A VLR system in addition to license plate recognition aims to increase the confidence of vehicle monitoring systems in private environments such...
This paper presents the improvement of vehicle classification in forward scattering radar (FSR) using a new classification technique. The technique is a combination between two methods which are Z-score and neural network (NN). The Zscore is used to extract the features of target signature while neural network is used as a classifier to classify the size of vehicles. The results of vehicle classification...
Advanced driver assistance systems, such as unintentional lane departure warning systems, have recently drawn much attention and R & D efforts. Such a system may assist the driver by monitoring the driver or vehicle behaviors to predict/detect driving situations (e.g., lane departure) and alert the driver to take corrective action. In this paper, we show how the support vector machine (SVM) methodology...
We present a motion planning algorithm for dynamic vehicles navigating through unknown environments. We focus on the scenario in which a fast-moving car attempts to navigate from a start location to a set of goal coordinates in minimum time with no prior information about the environment, building a map in real time from onboard sensor data. Whereas existing planners for exploration confine themselves...
Classification of vehicle logo is an important step towards the vehicle recognition that is required in many applications in intelligent transportation systems and automatic surveillance. A fast and reliable vehicle logo classification approach is proposed by first accurate logo detection, followed by an improved local-mean based classification algorithm. The recently published integrative logo detection...
This paper presents a concept for testing camera based ADAS, in order to reduce time and cost in the development phase. By adapting an existing virtual environment for the camera system, identifying and enhancing the important features for the testing detection function, the testing ADAS shall record the virtual driving scenes and deliver the same results as in real environments. A method to generate...
The development and test of advanced driver assistance systems (ADAS)present a challenge due to their complexity and dependency on other vehicle systems, initial conditions and their environment. Testing ADAS under real conditions leads to significant efforts and costs. Therefore, virtual prototyping and simulation are widely used instrumentsfor developing such complex systems. One of these useful...
This study aims to construct the vehicle feature model and deal with the vehicle identification problem without depending on the license plate recognition (LPR). The proposed system is applicable for detecting the fare evasion in electronic road pricing system. In the experiments, the completed vehicle model can be successfully built even though the training dataset are suffering the partial occlusion.
Research in human action recognition has advanced along multiple fronts in recent years to address various types of actions including simple, isolated actions in staged data (e.g., KTH dataset), complex actions (e.g., Hollywood dataset) and naturally occurring actions in surveillance videos (e.g, VIRAT dataset). Several techniques including those based on gradient, flow and interest-points have been...
Vehicle detection in traffic scenes is a fundamental task for intelligent transportation system and has many practical applications as diverse as traffic monitoring, intelligent scheduling and autonomous navigation. In recent years, the number of detection approaches in monocular images has grown rapidly. However, most of them focus on detecting other objects (such as face, pedestrian, cat, dog, etc...
Preventing a traffic accident is a good way to solve many problems in the world for the current generation surrounding with many automotive technologies causing many people's death from the accident. The prevention makes an important impact to every society for making many people more safety and improving their lives' quality. In the fact, the primary cause is mostly drivers' carelessness and lacking...
A method of estimating a vehicle's attitude in relation to the road surface using only light detection and ranging (lidar) measurements is presented. Gaussian processes, a machine learning technique, is used to relate the measurements of the road surface to the pitch and roll of the vehicle. Testing was performed under normal driving conditions on a test track as well as under high dynamic maneuvers...
Videos usually consist of activities involving interactions between multiple actors, sometimes referred to as complex activities. Recognition of such activities requires modeling the spatio-temporal relationships between the actors and their individual variabilities. In this paper, we consider the problem of recognition of complex activities in a video given a query example. We propose a new feature...
We investigate a range of solutions in car ‘make and model’ recognition. Several different feature detection approaches are investigated and applied to the problem including a new approach based on Harris corner strengths. This approach recursively partitions the image into quadrants, the feature strengths in these quadrants are then summed and locally normalised in a recursive, hierarchical fashion...
Driver emergency braking was distinguished and predicted exactly difficult. In order to gain the test data of driver emergency braking action, 7 professional drivers whose age were 23 to 45 years old were chose and 3 scenes of driver braking behavior including leading vehicle braking deceleration, a vehicle or pedestrian suddenly forced into the road way were designed and simulated by means of road...
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