Abstract
In this paper a method for detecting and estimating the distance of a vehicle driving in front using a single black-box camera installed in a vehicle was proposed. In order to apply the proposed method to autonomous vehicles, it was required to reduce the throughput and speed-up the processing. To do this, the proposed method decomposed the input image into multiple-resolution images for real-time processing and then extracted the aggregated channel features (ACFs). The idea was to extract only the most important features from images at different resolutions symmetrically. A method of detecting an object and a method of estimating a vehicle’s distance from a bird’s eye view through inverse perspective mapping (IPM) were applied. In the proposed method, ACFs were used to generate the AdaBoost-based vehicle detector. The ACFs were extracted from the LUV color, edge gradient, and orientation (histograms of oriented gradients) of the input image. Subsequently, by applying IPM and transforming a 2D input image into 3D by generating an image projected in three dimensions, the distance between the detected vehicle and the autonomous vehicle was detected. The proposed method was applied in a real-world road environment and showed accurate results for vehicle detection and distance estimation in real-time processing. Thus, it was showed that our method is applicable to autonomous vehicles.
Highlights
In recent years, interest in autonomous vehicles has been increasing rapidly
To evaluate the performance of the proposed vehicle detection method, we compared the vehicle region selected by the ground-truth method and the vehicle region detected by the proposed method using Equation (3)
If more than half of the selected vehicle region was detected in the experiment, it was judged as correct vehicle detection
Summary
Interest in autonomous vehicles has been increasing rapidly. Traditional vehicles can be improved with advances in artificial intelligence and its applications in various fields. Sensors for the safe driving of vehicles remain expensive. Sensors that collect three-dimensional information (such as lidar) are not practical for common use, because the price of a sensor is comparable to the price of a vehicle [3,7]. Ultrasonic sensors are used to collect two-dimensional information to recognize nearby objects as an auxiliary device for safe driving. Most of these sensors are located on the outside of the vehicle (front and rear bumper and rearview mirror). Most vehicles have been equipped with black-box devices to record driving conditions [6,8,9,10]. Black-box devices for vehicles are used to recover information
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