Browsing by Subject "roads"

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    Data Collection and Processing Methods for the Evaluation of Vehicle Road Departure Detection Systems
    (IEEE, 2018) Shen, Dan; Yi, Qiang; Li, Lingxi; Chien, Stanley; Chen, Yaobin; Sherony, Rini; Mechanical and Energy Engineering, School of Engineering and Technology
    Road departure detection systems (RDDSs) for avoiding/mitigating road departure crashes have been developed and included on some production vehicles in recent years. In order to support and provide a standardized and objective performance evaluation of RDDSs, this paper describes the development of the data acquisition and data post-processing systems for testing RDDSs. Seven parameters are used to describe road departure test scenarios. The overall structure and specific components of data collection system and data post-processing system for evaluating vehicle RDDSs is devised and presented. Experimental results showed sensing system and data post-processing system could capture all needed signals and display vehicle motion profile from the testing vehicle accurately. Test track testing under different scenarios demonstrates the effective operations of the proposed data collection system.
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    Predict Vehicle Collision by TTC From Motion Using a Single Video Camera
    (IEEE, 2018-05) Kilicarslan, Mehmet; Zheng, Jiang Yu; Computer and Information Science, School of Science
    The objective of this paper is the instantaneous computation of time-to-collision (TTC) for potential collision only from the motion information captured with a vehicle borne camera. The contribution is the detection of dangerous events and degree directly from motion divergence in the driving video, which is also a clue used by human drivers. Both horizontal and vertical motion divergence are analyzed simultaneously in several collision sensitive zones. The video data are condensed to the motion profiles both horizontally and vertically in the lower half of the video to show motion trajectories directly as edge traces. Stable motion traces of linear feature components are obtained through filtering in the motion profiles. As a result, this avoids object recognition and sophisticated depth sensing in prior. The fine velocity computation yields reasonable TTC accuracy so that a video camera can achieve collision avoidance alone from the size changes of visual patterns. We have tested the algorithm for various roads, environments, and traffic, and shown results by visualization in the motion profiles for overall evaluation.