Circle structure of online brand communities allows companies to conduct cross-marketing activities by the influence of friends in different circles and build strong and lasting relationships with customers. However, existing works on the friend recommendation in social network do not consider establishing friendships between users in different circles, which has the problems of network sparsity, neither do they study the adaptive generation of appropriate link prediction algorithms for different circle features. In order to fill the gaps in previous works, the intelligent attention allocation link prediction algorithm is proposed to adaptively build attention allocation index (AAI) according to the sparseness of the network and predict the possible friendships between users in different circles. The AAI reflects the amount of attention allocated to the user pair by their common friend in the triadic closure structure, which is decided by the friend count of the common friend. Specifically, for the purpose of overcoming the problem of network sparsity, the AAIs of both the direct common friends and indirect ones are developed. Next, the decision tree (DT) method is constructed to adaptively select the suitable AAIs for the circle structure based on the density of common friends and the dispersion level of common friends' attention. In addition, for the sake of further improving the accuracy of the selected AAI, its complementary AAIs are identified with support vector machine model according to their similarity in value, direction, and ranking. Finally, the mutually complementary indices are combined into a composite one to comprehensively portray the attention distribution of common friends of users in different circles and predict their possible friendships for cross-marketing activities. Experimental results on Twitter and Google + show that the model has highly reliable prediction performance.

To ensure that autonomous vehicles satisfy the requirements of the traffic environment, vehicle driving ability, and desired driver experience during obstacle avoidance, this paper proposes a trajectory planner that considers three aspects: driving passability, regional safety, and driving acceptance. Multiresolution state lattices and Bézier curve fitters are applied to a state lattice framework to generate candidate obstacle avoidance trajectory clusters. Trajectory evaluation is then carried out in the above three aspects by using trajectory passability, safety and driver behavior proximity, and a trajectory evaluation function is designed to evaluate and screen trajectory clusters. The trajectory passability is checked by the vehicle motion capability set, which is established based on the vehicle dynamics model. The trajectory safety is evaluated by the potential field function between the fitted trajectory and the vehicle driving environment boundary with consideration of the inevitable collision state. The parameters of the vehicle motion state for the fitted trajectory are matched with the driving data of real drivers with different driving styles to evaluate the proximity between the trajectory and driver behavior. The rationality and effectiveness of different driving styles of trajectory planners are verified by vehicle tests under different vehicle velocities and different obstacle disturbances.

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A global reference path generated by a path search algorithm based on a road-level driving map cannot be directly used to complete the efficient autonomous path-following motion of autonomous vehicles due to the large computational load and insufficient path accuracy. To solve this problem, this paper proposes a lane-level bidirectional hybrid path planning method based on a high-definition map (HD map), which effectively completes the high-precision reference path planning task. First, the global driving environment information is extracted from the HD map, and the lane-level driving map is constructed. Real value mapping from the road network map to the driving cost is realized based on the road network information, road markings, and driving behavior data. Then, a hybrid path search method is carried out for the search space in a bidirectional search mode, where the stopping conditions of the search method are determined by the relaxation region in the two search processes. As the search process continues, the dimension of the relaxation region is updated to dynamically adjust the search scope to maintain the desired search efficiency and search effect. After the completion of the bidirectional search, the search results are evaluated and optimized to obtain the reference path with the optimal traffic cost. Finally, in an HD map based on a real scene, the path search performance of the proposed algorithm is compared with that of the simple bidirectional Dijkstra algorithm and the bidirectional BFS search algorithm. The results show that the proposed path search algorithm not only has a good optimization effect, but also has a high path search efficiency.