Accurate prediction of vehicle trajectories is crucial to the safety and comfort of autonomous vehicles. Although several graph-based models have exhibited substantial progress in acquiring spatiotemporal dependencies among vehicles in the driving environment, the potential for additional exploration in this domain persists. The main reason is that they concentrated on independently capturing the spatial relations and temporal dependencies, neglecting to incorporate the temporal feature into the spatial feature for co-training, which limits their ability to yield satisfactory predictive accuracy. Typically, spatial and temporal correlations are coupled and should be modeled jointly. Inspired by this, a novel dynamic graph neural network with spatial–temporal synchronization (STS-DGNN) for vehicle trajectory prediction is proposed, which constructs the driving scene as dynamic graphs and can jointly extract spatial–temporal features. Specifically, low-order and high-order dynamics of vehicle trajectories are considered collaboratively in a one-stage framework rather than independently modeling the spatial relationship and temporal correlations of vehicles in two-stage models. The proposed model also considers the dynamic nature of graph sequence by utilizing gate recurrent unit (GRU) to update the graph neural network (GNN) parameters dynamically. The spatial–temporal features are subsequently conveyed to convolutional neural networks (CNNs) and processed by a multilayer perceptron (MLP) to generate the ultimate trajectories. Finally, to illustrate the effectiveness of the STS-DGNN model, the model is assessed on three well-known datasets, namely highD, EWAP, and UCY. The results confirm that our model performs better at making predictions than cutting-edge models. The visualization results intuitively explain that our method can extract sophisticated and subtle multivehicle interactions, resulting in accurate predictions.

STS-DGNN: Vehicle Trajectory Prediction via Dynamic Graph Neural Network With Spatial–Temporal Synchronization

https://export.arxiv.org/pdf/2211.14753v3.pdf

A Self-adaptive Neuroevolution Approach to Constructing Deep Neural Network Architectures Across Different Types

Pedestrian trajectory prediction holds significant research value in service robots, autonomous driving, and intelligent monitoring. Currently, most pedestrian trajectory prediction methods focus on data-driven models based on recurrent neural networks, but there is insufficient research on data-driven models based on convolutional neural networks. In this study, we first analyze the two problems in pedestrian trajectory prediction methods based on convolutional neural networks: 1. Previous trajectory prediction methods based on convolutional neural networks have spatial-temporal entanglement problems; 2. These methods are limited by their fixed convolution kernels and cannot accurately model social and temporal interactions. Furthermore, we propose a deformable spatial-temporal convolutional neural network (DSTCNN) to better adapt to the pedestrian trajectory prediction task. The deformable spatial-temporal convolutional neural network models spatial and temporal interactions separately, overcoming the shortcomings of spatial-temporal entanglement. The deformable spatial-temporal convolution also gets rid of the fixed convolution kernel, making the modeling of spatial-temporal interactions more accurate. On the ETH and UCY datasets, the average displacement error and final displacement error of our method are 0.29 and 0.53 meters, respectively. In kernel density estimation, average Mahalanobis distance, and average maximum eigenvalue metrics, our method still achieves better performance compared to baseline methods. Moreover, the deformable spatial-temporal convolutional neural network is a memory-efficient model with only 4.1K parameters. 

DSTCNN: Deformable spatial-temporal convolutional neural network for pedestrian trajectory prediction

With the fast development of AI-related techniques, the applications of trajectory prediction are no longer limited to easier scenes and trajectories. More and more heterogeneous trajectories with different representation forms, such as 2D or 3D coordinates, 2D or 3D bounding boxes, and even high-dimensional human skeletons, need to be analyzed and forecasted. Among these heterogeneous trajectories, interactions between different elements within a frame of trajectory, which we call the ``Dimension-Wise Interactions'', would be more complex and challenging. However, most previous approaches focus mainly on a specific form of trajectories, which means these methods could not be used to forecast heterogeneous trajectories, not to mention the dimension-wise interaction. Besides, previous methods mostly treat trajectory prediction as a normal time sequence generation task, indicating that these methods may require more work to directly analyze agents' behaviors and social interactions at different temporal scales. In this paper, we bring a new ``view'' for trajectory prediction to model and forecast trajectories hierarchically according to different frequency portions from the spectral domain to learn to forecast trajectories by considering their frequency responses. Moreover, we try to expand the current trajectory prediction task by introducing the dimension $M$ from ``another view'', thus extending its application scenarios to heterogeneous trajectories vertically. Finally, we adopt the bilinear structure to fuse two factors, including the frequency response and the dimension-wise interaction, to forecast heterogeneous trajectories via spectrums hierarchically in a generic way. Experiments show that the proposed model outperforms most state-of-the-art methods on ETH-UCY, Stanford Drone Dataset and nuScenes with heterogeneous trajectories, including 2D coordinates, 2D and 3D bounding boxes.

Another Vertical View: A Hierarchical Network for Heterogeneous Trajectory Prediction via Spectrums

Throat swab sampling is one of the efficient ways for nucleic acid test. However, the manual sampling work has a high risk of cross-infection, hence the development of autonomous throat swab sampling robot becomes necessary. The design of this kind of machine faces several challenging problems, for instance: (1) it requires coordination of multiple manipulators; (2) it requires an efficient pose calculation algorithm to precisely control the motion of manipulators; and (3) it requires an accurate visual segmentation algorithm to detect the region of interest in the oral cavity. Considering the problems mentioned above, we introduce the prototype of the robot in this paper, which combines multiple manipulators coordination with ma-chine vision technologies. Extensive experiments demonstrate the effectiveness and feasibility of the robot.

An Autonomous Throat Swab Sampling Robot for Nucleic Acid Test

Reliable obstacle avoidance, which is essential for safe autonomous robot interaction with the real world, raises various challenges such as difficulties with obstacle perception and latent factor cognition impacting multi-modal obstacle avoidance. In this paper, we propose a Depth visUal Ego-motion Learning (DUEL) model, consisting of a cognitive generation network, a policy decision network and a potential partition network, to learn autonomous obstacle avoidance from expert policies. The DUEL model takes advantage of binocular vision to perceive scene depth. This serves as the input to the cognitive generation network which generates obstacle avoidance policies by maximizing its causal entropy. The policy decision network then optimizes the generation of the policies referring to expert policies. The generated obstacle avoidance policies are simultaneously transferred to the potential partition network to capture the latent factors contained within expert policies and perform multi-modal obstacle avoidance. These three core networks iteratively optimize the multi-modal policies relying on causal entropy and mutual information theorems, which are proven theoretically. Experimental comparisons with state-of-the-art models on 7 metrics demonstrate the effectiveness of the DUEL model. It achieves the best performance with an average ADE (Average Displacement Error) of 0.29 and average FDE (Final Displacement Error) of 0.55 across five different scenarios. Results show that the DUEL model can maintain an average obstacle avoidance success rate of 97% for both simulated and real world scenarios with multiple obstacles, demonstrating its success at capturing latent factors from expert policies. Our source codes are available at https://github.com/ACoTAI/DUEL.

DUEL: Depth visUal Ego-motion Learning for autonomous robot obstacle avoidance

Predicting the future trajectories of pedestrians is of increasing importance for many applications such as autonomous driving and social robots. Nevertheless, current trajectory prediction models suffer from limitations such as lack of diversity in candidate trajectories, poor accuracy, and instability. In this paper, we propose a novel Sequence Entropy Energy-based Model named SEEM, which consists of a generator network and an energy network. Within SEEM we optimize the sequence entropy by taking advantage of the local variational inference of <inline-formula><tex-math notation="LaTeX">$f$</tex-math><alternatives><mml:math><mml:mi>f</mml:mi></mml:math><inline-graphic xlink:href="liu-ieq1-3147639.gif"/></alternatives></inline-formula>-divergence estimation to maximize the mutual information across the generator in order to cover all modes of the trajectory distribution, thereby ensuring SEEM achieves full diversity in candidate trajectory generation. Then, we introduce a probability distribution clipping mechanism to draw samples towards regions of high probability in the trajectory latent space, while our energy network determines which trajectory is most representative of the ground truth. This dual approach is our so-called all-then-one strategy. Finally, a zero-centered potential energy regularization is proposed to ensure stability and convergence of the training process. Through experiments on both synthetic and public benchmark datasets, SEEM is shown to substantially outperform the current state-of-the-art approaches in terms of diversity, accuracy and stability of pedestrian trajectory prediction.

SEEM: A Sequence Entropy Energy-Based Model for Pedestrian Trajectory All-Then-One Prediction

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DUEL: Depth visUal Ego-motion Learning for autonomous robot obstacle avoidance

SEEM: A Sequence Entropy Energy-Based Model for Pedestrian Trajectory All-Then-One Prediction