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—In this paper, we introduce a synergistic approach to integrated lane and vehicle tracking for driver assistance. The approach presented in this paper results in a final system that improves on the performance of both lane tracking and vehicle tracking modules. Further, the presented approach introduces a novel approach to localizing and tracking other(More)
—In this paper, we introduce vehicle detection by independent parts (VDIP) for urban driver assistance. In urban environments, vehicles appear in a variety of orientations, i.e., oncoming, preceding, and sideview. Additionally, partial vehicle occlusions are common at intersections, during entry and exit from the camera's field of view, or due to scene(More)
Safe operation of a motor vehicle requires awareness of the current traffic situation as well as the ability to predict future maneuvers. In order to provide an intelligent vehicle the ability to make predictions, this work proposes a framework for understanding the driving situation based on vehicle mounted vision sensors. Vehicles are tracked using Kalman(More)
—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(More)
In recent years, active learning has emerged as a powerful tool in building robust systems for object detection using computer vision. Indeed, active learning approaches to on-road vehicle detection have achieved impressive results. While active learning approaches for object detection have been explored and presented in the literature, few studies have(More)
—In this paper, we introduce a novel stereo-monocular fusion approach to on-road localization and tracking of vehicles. Utilizing a calibrated stereo-vision rig, the proposed approach combines monocular detection with stereo-vision for on-road vehicle localization and tracking for driver assistance. The system initially acquires synchronized monocular(More)
— In this paper, we present improved lane tracking using vehicle localization. Lane markers are detected using a bank of steerable filters, and lanes are tracked using Kalman filtering. On-road vehicle detection has been achieved using an active learning approach, and vehicles are tracked using a Condensation particle filter. While most state-of-the art(More)
— This paper details the research, development, and demonstrations of real-world systems intended to assist the driver in urban environments, as part of the Urban Intelligent Assist (UIA) research initiative. A 3-year collaboration between the driver assistance portion of the UIA project focuses on two main use cases of vital importance in urban driving.(More)