dc.description.abstract | A driver wants to change lane when driving on a road, he must glance the rearview and outside mirrors of his vehicle and turn his head to scan the possible approaching vehicles on the side lanes. However, the field of view by the above behavior is limited; there is a blind spot area invisible. To avoid the possible traffic accident during lane change, we here propose a lane change assistance system to assist drivers changing lane. In this system, two cameras are mounted under outside mirrors of the host vehicle to capture rear-side-view images for detecting approaching side vehicles. In this application of visual detection, there are two main problems influencing the detection results, they are various light conditions and perspective projection effect. In this study, we present two adaptive methods to overcome the two problems. Drivers have to face various weather and environment conditions when driving. The different weather conditions cause various light conditions, and the various light conditions may influence the detection results. The proposed method uses a 2-D intensity/gradient histogram to adaptively judge the edge, shadow, lane marks, and other scene components in various light conditions. On the other hand, when a side vehicle runs toward to the host vehicle with constant speed, the size and speed of the side vehicle appear increasing in images due to perspective projection effect. In this study, we propose a distance-adaptive method to compensate the horizontal optical-flow vectors in images. After the compensation, the horizontal optical-flow vectors are invariant to distance. Besides the above light and distance variant problems, the blind-spot vehicle detection also encounters the problems of false detection on lane marks and tree shadow on ground, and loss detection on similar-speed side vehicles. Thus, both static and motion features are adopted and sophisticatedly judged to detect side vehicles in this study.
The proposed system consists of four stages: estimation of light-adaptive threshold values with a 2-D intensity/gradient histogram, multiresolution optical flow estimation with distance-adaptive compensation, static feature detection, and detection decision based on the static and motion features. In experiments 6842 images in 14 side-vehicle videos were tested to evaluate the performance of the proposed systems. These videos were captured from six kinds of light conditions. Without light-adaptive function, we set 27 groups given values to three kinds of threshold values: underneath shadow, lane marking, and edge points. And also, we apply the 27 groups given value to five detection methods: only static (S), only motion (M), static and motion (S&M), static or motion (SorM), and mutually combing static and motion features without light-adaptive method (SM). Comparing the results of five detection methods, we find that we need the light-adaptive method to adjust the three kinds of threshold dynamically. After applying light-adaptive detection method, the accuracy of mutually combining static and motion features with light-adaptive method achieves 91.84% detection rate, 7.12% false alarm rate, and 1.04% missing rate. The accuracy of mutually combining static and motion features with light-adaptive method improve 16.68% than without light-adaptive method. The missing rate with light-adaptive is lower 19.82% than without light-adaptive method. With distance-adaptive function, first, we prove that the horizontal optical-flow vectors are invariant to distance after compensation. Second, we develop a distance-adaptive detection method to detect side vehicle. The accuracy of distance-adaptive method achieves 93.88% detection rate, 5.36% false alarm rate, and 0.76% missing rate. | en_US |