We will see smart cars on the street in future that has got ability to recognize the car, and has the ability to estimate the direction and distance from other vehicles or pedestrians and they can implement operations corresponding to the track for the navigation. In this paper by using stereo vision with the optical axis intersecting, by using two cameras, a camera has got rotation around on the Y-axis and using modern methods of image processing and focusing on the area of 1/5 m and using MATLAB software to estimate the distance between the vehicles. In this paper the whole process of stereo vision from image acquisition distortion compensation, image smoothing, stereo correspondence, and finally measure the distance is handled. The method used in this paper for calibration has got flexible more than other methods. The main advantage of the used method for calibration is its easy setup so that just by creating a calibration page anyone can use it. The results shows at day and in the laboratory conditions that has got acceptable accuracy %89/9.

The most important subjects in the memory‐based collaborative filtering recommender system (RS) are to accurately calculate the similarities between users and finally finding interesting recommendations for active users. The main purpose of this research is to provide a list of the best items for recommending in less time. The fuzzy‐genetic collaborative filtering (FGCF) approach recommends items by optimizing fuzzy similarities in the continuous genetic algorithm (CGA). In this method, first, the crisp values of user ratings are converted to fuzzy ratings, and then the fuzzy similarities are calculated. Similarity values are placed into the genes of the genetic algorithm, optimized, and finally, they are used in fuzzy prediction. Therefore, the fuzzy system is used twice in this process. Experimental results on RecSys, Movielens 100 K, and Movielens 1 M datasets show that FGCF improves the collaborative filtering RS performance in terms of quality and accuracy of recommendations, time and space complexities. The FGCF method is robust against the sparsity of data due to the correct choice of neighbours and avoids the users' different rating scales problem but it not able to solve the cold‐start challenge.

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