This paper introduces an adaptive method for detecting front vehicles under complex weather conditions. In the field of vehicle detection from images extracted by cameras installed in vehicles, backgrounds with complicated weather, such as rainy and snowy days, increase the difficulty of target detection. In order to improve the accuracy and robustness of vehicle detection in front of driverless cars, a cascade vehicle detection method combining multifeature fusion and convolutional neural network (CNN) is proposed in this paper. Firstly, local binary patterns, Haar-like and orientation gradient histogram features from the front vehicle are extracted, then principal-component-analysis dimension reduction and serial-fusion processing are performed on the input image. Furthermore, a preliminary screening is conducted as the input of a support vector machine classifier based on the acquired fusion features, and the CNN model is employed to validate cascade detection of the filtered results. Finally, an integrated data set extracted from BDD, Udacity, and other data sets is utilized to test the method proposed. The recall rate is 98.69%, which is better than the traditional feature algorithm, and the recall rate of 97.32% in a complex driving environment indicates that the algorithm possesses good robustness.

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A novel lane detection approach, based on the dynamic region of interest (DROI) selection in the horizontal and vertical safety vision, is proposed to improve the accuracy of lane detection in this paper. The curvature of each point on the edge of the road and the maximum safe distance, which are solved by the lane line equation and vehicle speed data of the previous frame, are used to accurately select the DROI at the current moment. Next, the global search of DROI is applied to identify the lane line feature points. Subsequently, the discontinuous points are processed by interpolation. To fulfill fast and accurate matching of lane feature points and mathematical equations, the lane line is fitted in the polar coordinate equation. The proposed approach was verified by the Caltech database, under the premise of ensuring real-time performance. The accuracy rate was 99.21% which is superior to other mainstream methods described in the literature. Furthermore, to test the robustness of the proposed method, it was tested in 5683 frames of complicated real road pictures, and the positive detection rate was 99.07%.

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The artificial bee colony (ABC) algorithm is a biomimetic optimization algorithm based on the intelligent foraging behavior of a bee colony. It has obvious advantages in dealing with complex nonlinear optimization problems. However, the random neighborhood search leads to the ABC algorithm being good at exploration but neglected in exploitation. Therefore, a modified artificial bee colony algorithm (MABC) is proposed in this paper. The modified artificial bee colony algorithm is applied to the thinned optimization of large multiple concentric circular antenna arrays. The aim is to make the thinned array obtain the narrow beam pattern with the best peak sidelobe level (PSLL) in the vertical plane. The elements in the concentric circular antenna arrays are uniformly excited and isotropic. Two different cases have been considered in this study for thinning of concentric circular antenna arrays using MABC, one with fixed uniform interelement spacing and another with optimum uniform interelement spacing. In both the cases, the thinning percentage of the array is kept equal to or more than 50%. Simulation results of the proposed thinned arrays are compared with a fully populated array to illustrate the effectiveness of our proposed method.