Autonomous driving (AD), including single-vehicle intelligent AD and vehicle–infrastructure cooperative AD, has become a current research hot spot in academia and industry, and multi-sensor fusion is a fundamental task for AD system perception. However, the multi-sensor fusion process faces the problem of differences in the type and dimensionality of sensory data acquired using different sensors (cameras, lidar, millimeter-wave radar, and so on) as well as differences in the performance of environmental perception caused by using different fusion strategies. In this article, we study multiple papers on multi-sensor fusion in the field of AD and address the problem that the category division in current multi-sensor fusion perception is not detailed and clear enough and is more subjective, which makes the classification strategies differ significantly among similar algorithms. We innovatively propose a multi-sensor fusion taxonomy, which divides the fusion perception classification strategies into two categories—symmetric fusion and asymmetric fusion—and seven subcategories of strategy combinations, such as data, features, and results. In addition, the reliability of current AD perception is limited by its insufficient environment perception capability and the robustness of data-driven methods in dealing with extreme situations (e.g., blind areas). This article also summarizes the innovative applications of multi-sensor fusion classification strategies in AD cooperative perception.

In e-learning recommender systems, interpersonal information between learners is very scarce, which makes it difficult to apply collaborative filtering (CF) techniques. In this study, we propose a hybrid filtering (HF) recommendation approach (SI-IFL) combining learner influence model (LIM), self-organization based (SOB) recommendation strategy and sequential pattern mining (SPM) together for recommending learning objects (LOs) to learners. The method works as follows: (1) LIM is applied to acquire the interpersonal information by computing the influence that a learner exerts on others. LIM consists of learner similarity, knowledge credibility, and learner aggregation. LIM is independent of ratings. (2) a SOB recommendation strategy is applied to recommend the optimal learner cliques for active learners by simulating the influence propagation among learners. Influence propagation means that a learner can move toward active learners, and such behaviors can stimulate the moving behaviors of his neighbors. This SOB recommendation approach achieves a stable structure based on distributed and bottom-up behaviors of individuals. (3) SPM is applied to decide the final learning objects (LOs) and navigational paths based on the recommended learner cliques. The experimental results demonstrate that SI-IFL can provide personalized and diversified recommendations, and it shows promising efficiency and adaptability in e-learning scenarios.