In order to make up for the high demand of supervised information in supervised learning， a self-supervised learning method based on minimal prior knowledge was proposed. Firstly， the unlabeled data were clustered on the basis of the prior knowledge of data， or the initial labels were generated for unlabeled data based on center distances of labeled data. Secondly， the data were selected randomly after labeling， and the machine learning method was selected to build sub-models. Thirdly， the weight and error of each data extraction were calculated to obtain average error of the data as the data label degree for each dataset， and set an iteration threshold based on the initial data label degree. Finally， the termination condition was determined on the basis of comparing the data-label degree and the threshold during the iteration process. Experimental results on 10 UCI public datasets show that compared with unsupervised learning algorithms such as K-means， supervised learning methods such as Support Vector Machine （SVM） and mainstream self-supervised learning methods such as TabNet （Tabular Network）， the proposed method achieves high classification accuracy on unbalanced datasets without using labels or on balanced datasets using limited labels.

Attackers can illegally open a vehicle by forgeing the Radio Frequency IDentification （RFID） signal sent by the vehicle remote key. Besides， when the vehicle remote key is lost or stolen， the attacker can obtain the secret data inside the vehicle remote key and clone a usable vehicle remote key， which will threaten the property and privacy security of the vehicle owner. Aiming at the above problems， a Vehicle RKE Two-Factor Authentication （VRTFA） protocol for vehicle Remote Keyless Entry （RKE） that resists physical cloning attack was proposed. The protocol is based on Physical Uncloneable Function （PUF） and biological fingerprint feature extraction and recovery functions， so that the specific hardware physical structure of the legal vehicle remote key cannot be forged. At the same time， the biological fingerprint factor was introduced to build a two-factor authentication protocol， thereby solving the security risk of vehicle remote key theft， and further guaranteeing the secure mutual authentication of vehicle RKE system. Security analysis results of the protocol using BAN logic show that VRTFA protocol can resist malicious attacks such as forgery attack， desynchronization attack， replay attack， man-in-the-middle attack， physical cloning attack， and full key leakage attack， and satisfy the security attributes such as forward security， mutual authentication， data integrity， and untraceability. Performance analysis results show that VRTFA protocol has stronger security and privacy and better practicality than the existing RFID authentication protocols.

Compared with 2D RGB-based images， 3D point clouds retain the real and rich geometric information of objects in space to deal with vision challenge with scale variation in the single object tracking problem. However， the precision of 3D object tracking is affected by the loss of information brought by the sparsity of point cloud data and the deformation caused by the object position changing. To solve the above two problems， a proposal-based aggregation network composed of three modules was proposed in an end-to-end learning pattern. In this network， the 3D bounding box was determined by locating object center in the best proposal to realize the single object tracking in 3D point cloud. Firstly， the point cloud data of both templates and search areas was transferred into bird’s-eye view pseudo images. In the first module， the feature information was enriched through spatial and cross-channel attention mechanisms. Then， in the second module， the best proposal was given by the anchor-based deep cross-correlation Siamese region proposal subnetwork. Finally， in the third module， the object features were extracted through region of interest pooling operation by the best proposal at first， and then， the object and template features were aggregated， the sparse modulated deformable convolution layer was used to deal with the problems of point cloud sparsity and deformation， and the final 3D bounding box was determined. Experimental results of the comparison between the proposed method and the state-of-the-art 3D point cloud single object tracking methods on KITTI dataset show that： in comprehensive experiment of car， the proposed method has improved 1.7 percentage points on success rate and 0.2 percentage points on precision in real scenes； in multi-category extensive experiment of car， van， cyclist and pedestrian， the proposed method has improved the average success rate by 0.8 percentage points， and the average precision by 2.8 percentage points， indicating that the proposed method can solve the single object tracking problem in 3D point cloud and make the 3D object tracking results more accurate.

Concerning the special requirements of collision detection in the virtual assembly environment, a virtual assembly-oriented two-layer exact collision detection algorithm named HSDHBB was proposed based on bounding volume boxes and space division method. The algorithm firstly usd space decomposition method to identify potential regional intersection and then used bounding volume boxes to locate the intersection triangles and the exact points. Methods of constructing the bounding volume boxes tree and space division were given, and the data structure of Hash table was used to accelerate the collision detection in space division. Finally, the algorithm was applied in CATIA, the results show that the algorithm can effectively meet the real-time and accuracy requirements of the virtual assembly environment.