The convolutional neural network (CNN) has been widely used in the field of self-driving cars. To satisfy the increasing demand, the deeper and wider neural network has become a general trend. However, this leads to the main problem that the deep neural network is computationally expensive and consumes a considerable amount of memory. To compress and accelerate the deep neural network, this paper proposes a filter pruning method based on feature maps clustering. The basic idea is that by clustering, one can know how many features the input images have and how many filters are enough to extract all features. This paper chooses Retinanet and WIDER FACE datasets to experiment with the proposed method. Experiments demonstrate that the hierarchical clustering algorithm is an effective method for filtering pruning, and the silhouette coefficient method can be used to determine the number of pruned filters. This work evaluates the performance change by increasing the pruning ratio. The main results are as follows: Firstly, it is effective to select pruned filters based on feature maps clustering, and its precision is higher than that of a random selection of pruned filters. Secondly, the silhouette coefficient method is a feasible method for finding the best clustering number. Thirdly, the detection speed of the pruned model improves greatly. Lastly, the method we propose can be used not only for Retinanet, but also for other CNN models. Its effect will be verified in future work.

Multi-Target tracking is a central aspect of modeling the environment of autonomous vehicles. A mono camera is a necessary component in the autonomous driving system. One of the biggest advantages of the mono camera is it can give out the type of vehicle and cameras are the only sensors able to interpret 2D information such as road signs or lane markings. Besides this, it has the advantage of estimating the lateral velocity of the moving object. The mono camera is now being used by companies all over the world to build autonomous vehicles. In the expressway scenario, the forward-looking camera can generate a raw picture to extract information from and finally achieve tracking multiple vehicles at the same time. A multi-object tracking system, which is composed of a convolution neural network module, depth estimation module, kinematic state estimation module, data association module, and track management module, is needed. This paper applies the YOLO detection algorithm combined with the depth estimation algorithm, Extend Kalman Filter, and Nearest Neighbor algorithm with a gating trick to build the tracking system. Finally, the tracking system is tested on the vehicle equipped with a forward mono camera, and the results show that the lateral and longitudinal position and velocity can satisfy the need for Adaptive Cruise Control (ACC), Navigation On Pilot (NOP), Auto Emergency Braking (AEB), and other applications.