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Using Multivariate Statistics

The purpose of this paper is to provide a comprehensive presentation and interpretation of the Ensemble Kalman Filter (EnKF) and its numerical implementation. The EnKF has a large user group, and numerous publications have discussed applications and theoretical aspects of it. This paper reviews the important results from these studies and also presents new ideas and alternative interpretations which further explain the success of the EnKF. In addition to providing the theoretical framework needed for using the EnKF, there is also a focus on the algorithmic formulation and optimal numerical implementation. A program listing is given for some of the key subroutines. The paper also touches upon specific issues such as the use of nonlinear measurements, in situ profiles of temperature and salinity, and data which are available with high frequency in time. An ensemble based optimal interpolation (EnOI) scheme is presented as a cost-effective approach which may serve as an alternative to the EnKF in some applications. A fairly extensive discussion is devoted to the use of time correlated model errors and the estimation of model bias.

The Ensemble Kalman Filter: Theoretical formulation and practical implementation

For the last two decades, intelligent transportation systems (ITS) have emerged as an efficient way of improving the performance of transportation systems, enhancing travel security, and providing more choices to travelers. A significant change in ITS in recent years is that much more data are collected from a variety of sources and can be processed into various forms for different stakeholders. The availability of a large amount of data can potentially lead to a revolution in ITS development, changing an ITS from a conventional technology-driven system into a more powerful multifunctional data-driven intelligent transportation system (D2ITS) : a system that is vision, multisource, and learning algorithm driven to optimize its performance. Furthermore, D2ITS is trending to become a privacy-aware people-centric more intelligent system. In this paper, we provide a survey on the development of D2ITS, discussing the functionality of its key components and some deployment issues associated with D2ITS Future research directions for the development of D2ITS is also presented.

Data-Driven Intelligent Transportation Systems: A Survey

A comprehensive review of background subtraction algorithms evaluated with synthetic and real videos

A forward-facing vision system for a vehicle includes a forward-facing camera disposed in a windshield electronics module attached at a windshield of the vehicle and viewing through the windshield. A control includes a processor that, responsive to processing of captured image data, detects taillights of leading vehicles during nighttime conditions and, responsive to processing of captured image data, detects lane markers on a road being traveled by the vehicle. The control, responsive to lane marker detection and a determination that the vehicle is drifting out of a traffic lane, may control a steering system of the vehicle to mitigate such drifting, with the steering system manually controllable by a driver of the vehicle irrespective of control by the control. The processor, based at least in part on detection of lane markers via processing of captured image data, determines curvature of the road being traveled by the vehicle.

Vision system for vehicle

Segmentation and classification of urban range data into different object classes have several challenges due to certain properties of the data, such as density variation, inconsistencies due to missing data and the large data size that require heavy computation and large memory. A method to classify urban scenes based on a super-voxel segmentation of sparse 3D data obtained from LiDAR sensors is presented. The 3D point cloud is first segmented into voxels, which are then characterized by several attributes transforming them into super-voxels. These are joined together by using a link-chain method rather than the usual region growing algorithm to create objects. These objects are then classified using geometrical models and local descriptors. In order to evaluate the results, a new metric that combines both segmentation and classification results simultaneously is presented. The effects of voxel size and incorporation of RGB color and laser reflectance intensity on the classification results are also discussed. The method is evaluated on standard data sets using different metrics to demonstrate its efficacy.

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Segmentation Based Classification of 3D Urban Point Clouds: A Super-Voxel Based Approach with Evaluation

Pedestrians are the most vulnerable participants in urban traffic. The first step toward protecting pedestrians is to reliably detect them in a real-time framework. In this paper, a new approach is presented for pedestrian detection in urban traffic conditions using a multilayer laser sensor mounted onboard a vehicle. This sensor, which is placed on the front of a vehicle, collects information about the distance distributed according to four planes. Like a vehicle, a pedestrian constitutes, in the vehicle environment, an obstacle that must be detected, and located and then identified and tracked if necessary. To improve the robustness of pedestrian detection using a single laser sensor, a detection system based on the fusion of information located in the four laser planes is proposed. The method uses a nonparametric kernel-density-based estimation of pedestrian position of each laser plane. The resulting pedestrian estimations are then sent to a decentralized fusion according to the four planes. Temporal filtering of each object is finally achieved within a stochastic recursive Bayesian framework (particle filter), allowing a closer observation of pedestrian random movement dynamics. Many experimental results are given and validate the relevance of our pedestrian-detection algorithm with regard to a method using only a single-row laser-range scanner.

Pedestrian Detection and Tracking in an Urban Environment Using a Multilayer Laser Scanner

This paper is concerned with the Simultaneous Localization And Mapping (SLAM) problem using data obtained from a microwave radar sensor. The radar scanner is based on Frequency Modulated Continuous Wave (FMCW) technology. In order to meet the needs of radar image analysis complexity, a trajectoryoriented EKF-SLAM technique using data from a 360. field of view radar sensor has been developed. This process makes no landmark assumptions and avoids the data association problem. The method of egomotion estimation makes use of the Fourier-Mellin Transform for registering radar images in a sequence, from which the rotation and translation of the sensor motion can be estimated. In the context of the scan-matching SLAM, the use of the Fourier-Mellin Transform is original and provides an accurate and efficient way of computing the rigid transformation between consecutive scans. Experimental results on real-world data are presented.

Radar Scan Matching SLAM Using the Fourier-Mellin Transform

Metrology of vehicle trajectories has several applications in the field of road safety, particularly in dangerous curves. Actually, it is of great interest to observe trajectories of vehicles with the aim of designing a real time driver warning device in dangerous areas. This paper addresses the first step of a work with a video system placed along the road with the objective of vehicle's position and speed estimation. This system has been totally developed for this project and can record simultaneously three cameras with 640 times 480 pixels up to 30 frames per second (fps) and rangefinder informations. The best contribution of this paper is an original probabilistic background subtraction algorithm, first step of a global method (calibration, tracking, ...) implemented to be able to measure vehicle trajectories. Kinematic GPS (in post-processing) has been extensively used to get ground truth

Laurent Trassoudaine

Papers

Segmentation Based Classification of 3D Urban Point Clouds: A Super-Voxel Based Approach with Evaluation

Pedestrian Detection and Tracking in an Urban Environment Using a Multilayer Laser Scanner

Radar Scan Matching SLAM Using the Fourier-Mellin Transform

Vehicle trajectories evaluation by static video sensors

Automatic guidance of agricultural vehicles using a laser sensor.