Research on Lane-Changing Decision Making and Planning of Autonomous Vehicles Based on GCN and Multi-Segment Polynomial Curve Optimization.

This paper considers the interactive effects between the ego vehicle and other vehicles in a dynamic driving environment and proposes an autonomous vehicle lane-changing behavior decision-making and trajectory planning method based on graph convolutional networks (GCNs) and multi-segment polynomial curve optimization. Firstly, hierarchical modeling is applied to the dynamic driving environment, aggregating the dynamic interaction information of driving scenes in the form of graph-structured data. Graph convolutional neural networks are employed to process interaction information and generate ego vehicle's driving behavior decision commands.

DctViT: Discrete Cosine Transform meet vision transformers.

Vision transformers (ViTs) have become one of the dominant frameworks for vision tasks in recent years because of their ability to efficiently capture long-range dependencies in image recognition tasks using self-attention. In fact, both CNNs and ViTs have advantages and disadvantages in vision tasks, and some studies suggest that the use of both may be an effective way to balance performance and computational cost. In this paper, we propose a new hybrid network based on CNN and transformer, using CNN to extract local features and transformer to capture long-distance dependencies.

A Multi-Step CNN-Based Estimation of Aircraft Landing Gear Angles.

This paper presents a method for measuring aircraft landing gear angles based on a monocular camera and the CAD aircraft model. Condition monitoring of the aircraft landing gear is a prerequisite for the safe landing of the aircraft. Traditional manual observation has an intense subjectivity.

"Reading Pictures Instead of Looking": RGB-D Image-Based Action Recognition via Capsule Network and Kalman Filter.

This paper proposes an action recognition algorithm based on the capsule network and Kalman filter called "Reading Pictures Instead of Looking" (RPIL). This method resolves the convolutional neural network's over sensitivity to rotation and scaling and increases the interpretability of the model as per the spatial coordinates in graphics. The capsule network is first used to obtain the components of the target human body.