The aim of this article attempts to propose an advanced design of driver assistance system which can provide the driver advisable information about the adjacent lanes and approaching lateral vehicles. The experimental vehicle has a camera mounted at the left side rear view mirror which captures the images of adjacent lane. The detection of lane lines is implemented with methods based on image processing techniques. The candidates for lateral vehicle are explored with lane-based transformation, and each one is verified with the characteristics of its length, width, time duration, and height. Finally, the distances of lateral vehicles are estimated with the well-trained recurrent functional neuro-fuzzy network. The system is tested with nine video sequences captured when the vehicle is driving on Taiwan's highway, and the experimental results show it works well for different road conditions and for multiple vehicles.
Most traffic accidents resulted from distraction, inattention to surrounding cars, and driving fatigue. In order to protect drivers, a real-time lane-detection and front-vehicle distance measurement system that uses a mounted camera inside a vehicle has been designed for safe driving. For lane detection, the laneboundary information is derived from the fan-scanning-detection method. The system calculates the departure degree according to the angular relationship of the boundaries and sends a suitable warning signal to drivers. For front-vehicle distance measurement, we use the front vehicle's shadow underneath it to identify the position of the front vehicle. The real distance is estimated by the use of the functional neurofuzzy network. The experimental results show that the system works successfully in real-time environment.Index Terms-Distance measurement, intelligent transportation system (ITS), lane detection, neural fuzzy networks (NFN).
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