Hypersonic Vehicle Trajectory Prediction Algorithm Based on Hough Transform

In this paper, a system solution is designed to improve the service efficiency of intersection. For the purpose, a method of trajectory prediction for vehicle is put forward, which is based on the basis of realize multi-targets tracking. The characteristics of traffic video, structural of road profile and movement of vehicles have been analyzed. We designed the multi-targets tracking algorithm based on particle filter for traffic video sequence, in order to improve the accuracy and rapidity, the heuristic method for multi-targets data correlation is proposed. Based on the results of tracking, we analyze the rule of movement parameters of vehicle as the path has changed, according to the features of structured road the vehicle running status parameters is simplified, and a Hidden Markov Model (HMM) has trained, which is used for vehicle trajectory prediction. In the end, Analysis the basic reason of traffic conflict and the dynamic model of traffic conflict zone is designed. Experimental results demonstrate that, the method of tracking based on particle filter has good tracking performance in traffic video; the vehicle trajectory can be predicted rapidly in the required time, and traffic conflict zone is produced accurately.

Accurate detection of human motion states and prediction of intentions are crucial for the natural walking assistance provided by lower-limb exoskeletons. Several methods for gait task recognition, phase estimation, and motion trajectory prediction have been proposed, but few studies have considered all of these simultaneously. This article proposes a novel scheme for gait recognition and trajectory prediction, capable of detecting gait events, recognizing multiple gait tasks, estimating gait phases, and predicting trajectories. First, this scheme proposes a gait task recognition method based on gait events and a multilayer perceptron neural network (MLPNN), which reduces computational cost and improves recognition stability. Next, a gait phase estimation method based on resettable symmetric adaptive oscillators (RSAOs) is introduced, which offers faster convergence speed and smaller error compared to traditional adaptive oscillators (AOs). Subsequently, a trajectory prediction method with adaptive dynamic motion primitives (ADMP) and feedback regulation is proposed. Finally, gait task recognition, phase estimation, trajectory prediction, and comprehensive online experiments are carried out respectively. The online experimental results show that the recognition accuracy of gait tasks can reach 99.2%, the phase estimation error is less than 0.3652 rad, and the trajectory estimation error is less than 1.1814°.

Machine learning for autonomous vehicle's trajectory prediction: A comprehensive survey, challenges, and future research directions

Connected and Automated Vehicles (CAV) have been rapidly developed, which, inevitably, renders that human-driven and autonomous vehicles share the road. Thus, trajectory prediction is an important research topic, which helps each CAV to efficiently follow a Human-Driven Vehicle (HV). In a wider scope, trajectory prediction, also, helps to improve the throughput of traffic flow and enhance its stability. To realize the trajectory prediction of Connected and Automated Vehicles to Human-Driven Vehicles, a car-following model, which is based on trajectory data, was established. Adding deep neural networks and an Attention mechanism, this paper established a data-driven car-following model, based on CNN-BiLSTM-Attention for CAV, to predict trajectory, by referring to the modeling idea of the traditional car-following model. The trajectory data in the next-generation-simulation (NGSIM) datasets that match the car-following characteristics were selected. In addition, noise-reduction pre-processing of the trajectory data was performed, to make it match the actual car-following situation. Experiments, for selecting the optimal structure of the model and the method of trajectory prediction, were carried out. The data-driven car-following models, such as LSTM, GRU, and CNN-BiLSTM, were selected for comparative analysis of trajectory prediction. The results show that the CNN-BiLSTM-Attention model has the smallest MAE and MSE as well as the largest R2. The CNN-BiLSTM-Attention model has the highest accuracy in vehicle-trajectory prediction. The model can, effectively, realize vehicle-trajectory prediction and provide a theoretical basis for vehicle-trajectory-based velocity guidance of Human-Driven Vehicles. In the future, the model can, also, provide the theoretical basis for Connected and Automated Vehicles, to make car-following decisions in mixed traffic flow.

Pedestrian trajectory prediction is fundamental to a wide range of scientific research work and industrial applications. Most of the current advanced trajectory prediction methods incorporate context information such as pedestrian neighbourhood, labelled static obstacles, and the background scene into the trajectory prediction process. In contrast to these methods which require rich contexts, the method in our paper focuses on predicting a pedestrian's future trajectory using his/her observed part of the trajectory only. Our method, which we refer to as LVTA, is a Location-Velocity-Temporal Attention LSTM model where two temporal attention mechanisms are applied to the hidden state vectors from the location and velocity LSTM layers. In addition, a location-velocity attention layer embedded inside a tweak module is used to improve the predicted location and velocity coordinates before they are passed to the next time step. Extensive experiments conducted on three large benchmark datasets and comparison with eleven existing trajectory prediction methods demonstrate that LVTA achieves competitive prediction performance. Specifically, LVTA attains 9.19 pixels Average Displacement Error (ADE) and 17.28 pixels Final Displacement Error (FDE) for the Central Station dataset, and 0.46 metres ADE and 0.92 metres FDE for the ETH&UCY datasets. Furthermore, evaluation on using LVTA to generate trajectories of different prediction lengths and on new scenes without the need of retraining confirms that it has good generalizability.

Near space hypersonic vehicles present severe challenges for real-time trajectory prediction due to their high dynamics, expansive operating domains, and unpredictable maneuvering patterns. This article proposes an online trajectory prediction method based on Gaussian Process (GP) regression. Unlike existing approaches, the proposed method does not require complex pretraining or extensive prior knowledge, yet it achieves an effective balance between high accuracy and real-time performance. Specifically, the method first employs an extended Kalman filter (EKF) to fuse the vehicle's fast- and slow-time-varying kinematic models, yielding an efficient online estimate of acceleration. This acceleration estimate sequence is then used to train the GP model, enabling rolling prediction of future accelerations within a sequential iterative framework. Simulation results in two representative maneuvering scenarios demonstrate that the proposed method accurately captures the vehicle's motion trends from the online estimates and delivers superior real-time prediction accuracy.

Data-driven trajectory prediction with weather uncertainties: A Bayesian deep learning approach

This paper aims to propose a new guidance algorithm for a rocket with aerodynamics control for launch
\noperations, based on the concept of the instantaneous impact point (IIP). In this study, the rocket with aerodynamics
\ncontrol is considered with the purpose of reducing dispersion of the impact point after separation of the rocket for safety
\nreasons. Since a very limited aerodynamic maneuverability is typically allowed for the rocket due to the structural limit,
\na guidance algorithm producing a huge acceleration demand is not desirable. Based on this aspect, the proposed guidance
\nalgorithm is derived directly from the underlying principle of the guidance process: forming the collision geometry
\ntowards a target point. To be more specific, the collision-ballistic-trajectory where the instantaneous impact point becomes
\nthe target point, and the corresponding heading error are first determined using a rapid ballistic trajectory prediction
\ntechnique. Here, the trajectory prediction method is based on the partial closed-form solutions of the ballistic trajectory
\nequations considering aerodynamic drag and gravity. And then, the proposed guidance algorithm works to nullify the
\nheading error in a finite time, governed by the optimal error dynamics. The key feature of the proposed guidance algorithm
\nlies in its simple implementation and exact collision geometry nature. Hence, the proposed method allows achieving the
\ncollision course with minimal guidance command, and it is a desirable property for the guidance algorithm of the rocket
\nwith the aerodynamics control. Finally, numerical simulations are conducted to demonstrate the effectiveness of the
\nproposed guidance algorithms

This paper presents the results from a test of the performance of several general trajectory prediction methods and proposes a hybrid trajectory prediction model that aims to increase the safety of flights en route and improve airspace management capabilities by predicting the aircraft’s four-dimensional trajectory (4DT) more accurately. The automatic dependent surveillance-broadcast (ADS-B) data from 589 trajectories of cruising aircraft from the Guangzhou area were extracted for experiments. Numerous trajectory prediction methods, including velocity trend extrapolation, long short-term memory (LSTM), stateful-LSTM, back propagation (BP) neural network, a one-dimensional convolutional neural network (1D-ConvNet), Kalman filter, and flight plan interpolation were used for prediction experiments, and their performance at different time spans of prediction is obtained. By extracting the best methods using different time spans of prediction, a hybrid prediction model is proposed based on the reconstruction of these methods. For the data in this paper, the mean squared error (MSE) of the hybrid prediction model is significantly reduced compared to other methods in different time spans of prediction, which has great significance for future trajectory prediction in a structured airspace.

Predicting accurate future trajectories of agents is essential for autonomous navigation in complex scenarios. Although numerous work has made great progress on this goal, it is still challenging due to the uncertainty and continuity of behavioral intentions of agents, where uncertainty means the instantaneous multimodality of motion behavior, while the continuity refers to the consistency and stability of behavioral intention of an agent over a period of time constrained by its final destination. These factors easily affect the improvement of prediction accuracy. In this paper, we present a novel trajectory prediction method, Stacked Conditional VAE (S-CVAE) with Incremental Greedy Region (IGR). Specifically, the IGR is designed to enlarge the coverage of candidate waypoints/endpoints by reformulating the waypoints/endpoints prediction problem as candidate region generation, which can further encourage and model multimodality of behavioral intentions. Meanwhile, to exploit the inherent continuity between adjacent behavioral intentions of an agent, the S-CVAE architecture is constructed to transmit the behavioral intentions of one agent by inserting intermediate waypoints with IGR into the potential trajectories from the observed path to the final endpoint, and also enhances the reliability of the generated waypoints/endpoints in the next moment, further improve the accuracy of trajectory prediction. Our method is evaluated on several public datasets, including nuScenes, Apolloscape, SDD, INTERSECTION, Waymo, and VTPTL. The comprehensive experimental results demonstrate that our method achieves significant performance on these datasets. Especially in nuScenes and VTPTL, the accuracy is increased by at least 11.11% on average ADE and 2.40% on average FDE compared with state-of-the-arts.

The prediction of drag anchor trajectory is key for the anchor design. The yield envelope method based on anchor behavior under combined loadings provided an alternative method for the prediction of anchor trajectory. The traditional yield envelope method based on deep anchor behavior ignored the shallow anchor behavior that occurred during installation from shallow depth to deep depth and may lead to unreliable trajectory prediction. The present work examines how the shallow behavior of an inclined fluke influences the predicted trajectory of drag anchors. An example case was used to investigate the effects of soil non-homogeneity and anchor–soil interface friction on the predicted trajectory of drag anchors. The trajectory of another case of practical drag anchor is also predicted and compared to the existing centrifuge results. The results indicate that the prediction results are largely influenced by the selected interface friction condition and the bearing capacity factor. Incorporating both shallow and deep behaviors of inclined flukes leads to a more accurate description of anchor trajectory. Recommendations on the new method of drag anchor trajectory prediction, considering both shallow and deep anchor behavior, are proposed for a reliable trajectory prediction.

Recently, there have been several approaches to utilize a Brain-Computer Interface (BCI) for chronic stroke patients. The prediction of movement trajectory based on recorded brain activity could thereby help to improve BCI-guided stroke rehabilitation or could be used for control of an assistive device, like an orthosis or a robotic arm. One problem in predicting movement trajectory in stroke patients are compensatory movements, which make it difficult to link specific brain activity to movement intention. In this paper we compare different methods for trajectory prediction and show how Canonical Correlation Analysis (CCA) can be used to predict movement trajectories. Based on the results, we argue that the resulting trajectory prediction is closer to the actual movement intention. We further show how the transformation matrices obtained by CCA can be interpreted and discuss how this interpretation might be useful to get information regarding compensatory movements in stroke and the underlying patterns of brain activity.

Accurate trajectory prediction plays a crucial role in optimizing the performance of Internet of Vehicles (IoV) systems, reducing data transmission overhead, and enhancing communication network security. However, the expanding sensing range in IoV has led to increasingly complex spatial-temporal interactions, posing significant challenges for future trajectory prediction endeavors. Currently, predominant approaches involve constructing spatial-temporal interactions through various attention mechanisms. Nevertheless, these methods often yield numerous redundant interactions, potentially resulting in unstable predictions and diffuse interactions. Consequently, there is a pressing need to enhance the application of these methods in trajectory prediction within IoV contexts. Motivated by these challenges, our work introduces a sparse spatial-temporal Transformer (SSTT) to predict vehicle trajectories. SSTT consists of two main Transformer modules: the sparse spatial Transformer and the local-global temporal Transformer. We integrate a learnable sparse plugin into the former to minimize extraneous information in spatial interactions. This plugin enables SSTT to focus more effectively on critical interactive neighbor vehicles by optimizing attention weight distribution, thereby enhancing optimization convergence and prediction accuracy. For the latter, local time windows are employed to capture temporal local correlations and extend the attentional receptive field. Experimental results conducted on three real-world datasets demonstrate that SSTT achieves state-of-the-art performance, and even when only 15% of the training data is used, it can still outperform SOAT baselines. This study presents novel ideas and methodologies for advancing trajectory prediction techniques within the IoV paradigm. The code and our model will be available at GitHub.

Robots that navigate to approach free-standing conversational groups should do so in a safe and socially acceptable manner. This is challenging since it not only requires the robot to plot trajectories that avoid collisions with members of the group, but also to do so without making those in the group feel uncomfortable, for example, by moving too close to them or approaching them from behind. Previous trajectory prediction models focus primarily on formations of walking pedestrians, and those models that do consider approach behaviours into free-standing conversational groups typically have handcrafted features and are only evaluated via simulation methods, limiting their effectiveness. In this paper, we propose AppGAN, a novel trajectory prediction model capable of generating trajectories into free-standing conversational groups trained on a dataset of safe and socially acceptable paths. We evaluate the performance of our model with state-of-the-art trajectory prediction methods on a semi-synthetic dataset. We show that our model outperforms baselines by taking advantage of the GAN framework and our novel group interaction module.

Techniques for predicting the trajectory of vulnerable road users are important to the development of perception systems for autonomous vehicles to avoid accidents. The most effective trajectory prediction methods, such as Social-LSTM, are often used to predict pedestrian trajectories in normal passage scenarios. However, they can produce unsatisfactory prediction results and data redundancy, as well as difficulties in predicting trajectories using pixel-based coordinate systems in collision avoidance systems. There is also a lack of validations using real vehicle-to-pedestrian collisions. To address these issues, some insightful approaches to improve the trajectory prediction scheme of Social-LSTM were proposed, such methods included transforming pedestrian trajectory coordinates and converting image coordinates to world coordinates. The YOLOv5 detection model was introduced to reduce target loss and improve prediction accuracy. The DeepSORT algorithm was employed to reduce the number of target transformations in the tracking model. Image Perspective Transformation (IPT) and Direct Linear Transformation (DLT) theories were combined to transform the coordinates to world coordinates, identifying the collision location where the accident could occur. The performance of the proposed method was validated by training tests using MS COCO (Microsoft Common Objects in Context) and ETH/UCY datasets. The results showed that the target detection accuracy was more than 90% and the prediction loss tends to decrease with increasing training steps, with the final loss value less than 1%. The reliability and effectiveness of the improved method were demonstrated by benchmarking system performance to two video recordings of real pedestrian accidents with different lighting conditions.