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

This paper provides a review of the literature in on-road vision-based vehicle detection, tracking, and behavior understanding. Over the past decade, vision-based surround perception has progressed from its infancy into maturity. We provide a survey of recent works in the literature, placing vision-based vehicle detection in the context of sensor-based on-road surround analysis. We detail advances in vehicle detection, discussing monocular, stereo vision, and active sensor-vision fusion for on-road vehicle detection. We discuss vision-based vehicle tracking in the monocular and stereo-vision domains, analyzing filtering, estimation, and dynamical models. We discuss the nascent branch of intelligent vehicles research concerned with utilizing spatiotemporal measurements, trajectories, and various features to characterize on-road behavior. We provide a discussion on the state of the art, detail common performance metrics and benchmarks, and provide perspective on future research directions in the field.

https://cvrr.ucsd.edu/publications/2013/SayananTrivedi_IEEETITS2013_VehicleSurvey.pdf

Looking at Vehicles on the Road: A Survey of Vision-Based Vehicle Detection, Tracking, and Behavior Analysis

http://clinique-autisme-asperger-mtl.ca/wp/wp-content/uploads/2015/03/Grossberg-2012.pdf

Adaptive Resonance Theory: How a brain learns to consciously attend, learn, and recognize a changing world

This paper presents a comprehensive literature review on environment perception for intelligent vehicles. The state-of-the-art algorithms and modeling methods for intelligent vehicles are given, with a summary of their pros and cons. A special attention is paid to methods for lane and road detection, traffic sign recognition, vehicle tracking, behavior analysis, and scene understanding. In addition, we provide information about datasets, common performance analysis, and perspectives on future research directions in this area.

/pdf/overview-of-environment-perception-for-intelligent-vehicles-7s8l9oth23.pdf

Overview of Environment Perception for Intelligent Vehicles

An algorithm for license plate recognition (LPR) applied to the intelligent transportation system is proposed on the basis of a novel shadow removal technique and character recognition algorithms. This paper has two major contributions. One contribution is a new binary method, i.e., the shadow removal method, which is based on the improved Bernsen algorithm combined with the Gaussian filter. Our second contribution is a character recognition algorithm known as support vector machine (SVM) integration. In SVM integration, character features are extracted from the elastic mesh, and the entire address character string is taken as the object of study, as opposed to a single character. This paper also presents improved techniques for image tilt correction and image gray enhancement. Our algorithm is robust to the variance of illumination, view angle, position, size, and color of the license plates when working in a complex environment. The algorithm was tested with 9026 images, such as natural-scene vehicle images using different backgrounds and ambient illumination particularly for low-resolution images. The license plates were properly located and segmented as 97.16% and 98.34%, respectively. The optical character recognition system is the SVM integration with different character features, whose performance for numerals, Kana, and address recognition reached 99.5%, 98.6%, and 97.8%, respectively. Combining the preceding tests, the overall performance of success for the license plate achieves 93.54% when the system is used for LPR in various complex conditions.

An Algorithm for License Plate Recognition Applied to Intelligent Transportation System

Driving always involves risk. Various means have been proposed to reduce the risk. Critical motion detection of nearby moving vehicles is one of the important means of preventing accidents. In this paper, a computational model, which is referred to as the dynamic visual model (DVM), is proposed to detect critical motions of nearby vehicles while driving on a highway. The DVM is motivated by the human visual system and consists of three analyzers: 1) sensory analyzers, 2) perceptual analyzers, and 3) conceptual analyzers. In addition, a memory, which is called the episodic memory, is incorporated, through which a number of features of the system, including hierarchical processing, configurability, adaptive response, and selective attention, are realized. A series of experimental results with both single and multiple critical motions are demonstrated and show the feasibility of the proposed system.

/pdf/critical-motion-detection-of-nearby-moving-vehicles-in-a-4enaoav7c9.pdf

Critical Motion Detection of Nearby Moving Vehicles in a Vision-Based Driver-Assistance System

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Critical Motion Detection of Nearby Moving Vehicles in a Vision-Based Driver-Assistance System