Graph-Based Interaction-Aware Multimodal 2D Vehicle Trajectory Prediction using Diffusion Graph Convolutional Networks

Predicting vehicle trajectories is crucial for ensuring automated vehicle operation efficiency and safety, particularly on congested multi-lane highways. In such dynamic environments, a vehicle's motion is determined by its historical behaviors as well as interactions with surrounding vehicles. These intricate interactions arise from unpredictable motion patterns, leading to a wide range of driving behaviors that warrant in-depth investigation. This study presents the Graph-based Interaction-aware Multi-modal Trajectory Prediction (GIMTP) framework, designed to probabilistically predict future vehicle trajectories by effectively capturing these interactions. Within this framework, vehicles' motions are conceptualized as nodes in a time-varying graph, and the traffic interactions are represented by a dynamic adjacency matrix. To holistically capture both spatial and temporal dependencies embedded in this dynamic adjacency matrix, the methodology incorporates the Diffusion Graph Convolutional Network (DGCN), thereby providing a graph embedding of both historical states and future states. Furthermore, we employ a driving intention-specific feature fusion, enabling the adaptive integration of historical and future embeddings for enhanced intention recognition and trajectory prediction. This model gives two-dimensional predictions for each mode of longitudinal and lateral driving behaviors and offers probabilistic future paths with corresponding probabilities, addressing the challenges of complex vehicle interactions and multi-modality of driving behaviors. Validation using real-world trajectory datasets demonstrates the efficiency and potential

Fabrication Of Surface-Stabilized Ferroelectric Liquid Crystal Display With Stripe-Shaped Domain Structure

A uniform stripe-shaped domain (SSD) structure was obtained in the initial ferroelectric liquid crystal (FLC) alignment by rubbing the polyimide films doped with silicon naphthalocyanine. Such a surface-stabilized FLC cell exhibits a high contrast ratio and excellent bistability. Atomic force microscope images and pretilt angle measurement results demonstrated that the rubbing-induced polishing and flattening on the doped polyimide alignment films and the ordered arrangement of polymer aggregations are the main factors determining the formation of SSD structures. Using such alignment technique, a 64 × 80 FLC display device was assembled. The display panel shows 80 μs response time and \u3e90% bistable memory capability. The device stability is also improved due to the existence of the SSD structure

Predictive Analysis of Vehicular Lane Changes: An Integrated LSTM Approach

In the rapidly advancing domain of vehicular traffic management and autonomous driving, accurate lane change predictions are paramount for ensuring safety and optimizing traffic flow. This study introduces a comprehensive two-stage prediction model that harnesses the capabilities of long short-term memory (LSTM) for anticipating vehicular lane changes. Initially, we employed a variety of models, such as regression methods, SVMs, and a multilayer perceptron, to categorize lane change behaviors. The dataset was then segmented based on vehicle trajectories and lane change patterns. In the subsequent phase, we utilized the superior classification outcomes from LinearSVC to curate our training data. We developed two dedicated LSTM networks tailored to specific datasets: the lane-keeping LSTM (LK-LSTM) and the lane-changing LSTM (LC-LSTM). By integrating insights from both models, we achieved a comprehensive prediction of vehicular lane changes. Our results indicate that the unified prediction model markedly enhances prediction precision. Accurate lane change predictions offer valuable contributions to advanced driver-assistance systems (ADAS), with the potential to minimize traffic mishaps and enhance traffic fluidity. As we transition to a more autonomous automotive era, refining these predictions becomes essential in seamlessly merging human and automated driving experiences

Study of Wide Swath Synthetic Aperture Ladar Imaging Techology

Combining synthetic-aperture imaging and coherent-light detection technology, the weak signal identification capacity of Synthetic Aperture Ladar (SAL) reaches the photo level, and the image resolution exceeds the diffraction limit of the telescope to obtain high-resolution images irrespective to ranges. This paper introduces SAL, including the development path, technology characteristics, and the restriction of imaging swath. On the basis of this, we propose to integrate the SAL technology for extending its swath. By analyzing the scanning-operation mode and the signal model, the paper explicitly proposes that the former mode will be the developmental trend of the SAL technology. This paper also introduces the flight demonstrations of the SAL and the imaging results of remote targets, showing the potential of the SAL in long-range, high-resolution, and scanning-imaging applications. The technology and the theory of the scanning mode of SAL compensates for the defects related to the swath and operation efficiency of the current SAL. It provides scientific foundation for the SAL system applied in wide swath, high resolution earth observation, and the ISAL system applied in space-targets imaging