Trajectory Prediction Research Articles

Rapid Vehicle Trajectory Prediction Based on Multi-Attention Mechanism for Fusing Multimodal Information

Trajectory prediction plays a crucial role in autonomous driving tasks, as accurately and rapidly predicting the future trajectories of traffic participants can significantly enhance the safety and robustness of autonomous driving systems. This paper presents a novel trajectory prediction model that follows the encoder–decoder paradigm, achieving precise and rapid predictions of future vehicle trajectories by efficiently aggregating the spatiotemporal and interaction information of agents in traffic scenarios. We propose an agent–agent interaction information extraction module based on a sparse graph attention mechanism, which enables the efficient aggregation of interaction information between agents. Additionally, we introduce a non-autoregressive query generation method that accelerates the model inference speed by generating the decoding queries in parallel. Comparative experiments with the existing advanced algorithms show that our method improves the multimodal trajectory prediction metrics for the Minimum Average Displacement Error (minADE), the Minimum Final Displacement Error (minFDE), and the Miss Rate (MR) by an average of 9.1%, 11.8%, and 14.6%, respectively, while the inference time is only 33.7% of the average time taken by the other algorithms. Finally, we demonstrate the effectiveness of the various modules proposed in this paper through ablation studies.

Ship Trajectory Prediction in Complex Waterways Based on Transformer and Social Variational Autoencoder (SocialVAE)

Ship trajectory prediction plays a key role in the early warning and safety of maritime traffic. It is a necessary assistant tool that can forecast a ship’s trajectory in a certain period to prevent ship collision. However, highly precise prediction of long-term ship trajectories is still a challenge. This study proposes a ship trajectory prediction model called ShipTrack-TVAE, which is based on a Variational Autoencoder (SocialVAE) and Transformer architecture. It aims to address ship trajectory prediction tasks in complex waterways. To enable the model to avoid potential collision risks, this study designs a collision avoidance mechanism, which comprehensively incorporates safety constraints related to the distance between ships into the loss function. The experimental results show that on the Qiongzhou Strait ship AIS dataset, the Average Displacement Error (ADE) of ShipTrack-TVAE improved by 21.85% compared to the current state-of-the-art trajectory prediction model, SocialVAE, while the Final Displacement Error (FDE) improved by 17.83%. The experimental results demonstrate that the ShipTrack-TVAE model can effectively improve the prediction accuracy of short-term, medium-term, and long-term ship trajectories. It has excellent performance and provides a certain reference value for advancing unmanned ship collision avoidance.

Trajectory prediction based on the dynamic characteristics and coupling relationships among vehicles in highway scenarios

In dynamic and highly interactive traffic scenarios, vehicle trajectory prediction has become a critical and complex problem in vehicle assistance systems due to the uncertainty of traffic participant behaviour. This study proposes a novel vehicle trajectory prediction method called the Dynamic Coupling-based Long and Short-Term Memory Network (DSKM-LSTM). The method innovatively combines a vehicle dynamics model with the dynamic coupling between vehicles by integrating the intrinsic motion laws of the vehicles with the correlation of the motion parameters (position, velocity, and acceleration) between the vehicles via Kalman filtering. Subsequently, this information is embedded into a Long Short-Term Memory (LSTM)-based encoder-decoder framework to encode and decode historical trajectories with the fused information in order to predict future driving trajectories. Additionally, this paper introduces a dynamic loss function that optimizes the generalization capability and iteration time of DSKM-LSTM by adjusting the threshold value in real time and dynamically updating the loss value. This approach effectively addresses the problem of insufficient objectivity and accuracy in existing vehicle trajectory prediction models and provides a theoretical foundation for generating objective and interpretable trajectories in the field of trajectory prediction. Validation results on three publicly available datasets show that DSKM-LSTM achieves efficient and accurate long-term (5-s) trajectory prediction in complex traffic scenarios (e.g., motorways).

Training improvement methods of ANN trajectory predictors in power systems

AbstractThis paper proposes training improvement methods of artificial neural networks (ANN) trajectory predictors. First, a dynamic power system time‐series trajectory is split into several different segments to simplify the original ANN training problem. Moreover, the time‐derivative of the trajectory is included to obtain an augmented loss function. Compared to previous studies which mainly focused on increasing the prediction accuracy, the aim of these novel techniques is to reduce the computational burden where the ANN output performance is still acceptable. The effectiveness of the developed methods is validated based on the WSCC three‐machine nine‐bus and IEEE 39‐bus system models. The mean absolute error (MAE) and trajectory prediction results are analysed, in which the numbers of neurons, hidden layers, and training epochs are constrained during the ANN training process. Rotor‐angle difference between generators and the system frequency are investigated as the dynamic trajectories of the power system models. The approaches are revealed to be effective when the ANN architecture and epochs are constrained. The MAE results can be reduced by up to 65% in the power system models depending on the ANN hyperparameters and training epochs. The ANN training results can better reflect the original trajectory as well.

Research on flight intention recognition of UAVs-swarm

Abstract Most existing threat level assessments for aerial targets rely on numerical analysis and typically address only single or a few incoming targets without incorporating intention recognition. Such methods are inadequate for anti-UAV(Unmanned aerial vehicle) tasks involving large numbers of flight targets. To accurately and efficiently identify the flight intentions of UAVs-swarm, this paper proposes a method based on formation recognition and trajectory prediction. Initially, the trajectories of UAVs are obtained and clustered using the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm. For each cluster, a Bayesian classifier is employed to recognize its flight formation. Subsequently, a minimum enclosing sphere is established for each cluster, and trajectory fusion and prediction are conducted based on the sphere’s center. Finally, a flight intention recognition model is constructed. The results demonstrate that this method effectively infers the flight intentions of each UAVs-cluster, providing a robust basis for the threat level assessment in anti-UAVs-swarm tasks.

Enhancing stability and safety: A novel multi‐constraint model predictive control approach for forklift trajectory

AbstractThe advancements in intelligent manufacturing have made high‐precision trajectory tracking technology crucial for improving the efficiency and safety of in‐factory cargo transportation. This study addresses the limitations of current forklift navigation systems in trajectory control accuracy and stability by proposing the Enhanced Stability and Safety Model Predictive Control (ESS‐MPC) method. This approach includes a multi‐constraint strategy for improved stability and safety. The kinematic model for a single front steering‐wheel forklift vehicle is constructed with all known state quantities, including the steering angle, resulting in a more accurate model description and trajectory prediction. To ensure vehicle safety, the spatial safety boundary obtained from the trajectory planning module is established as a hard constraint for ESS‐MPC tracking. The optimisation constraints are also updated with the key kinematic and dynamic parameters of the forklift. The ESS‐MPC method improved the position and pose accuracy and stability by 57.93%, 37.83%, and 57.51%, respectively, as demonstrated through experimental validation using simulation and real‐world environments. This study provides significant support for the development of autonomous navigation systems for industrial forklifts.

Trajectory Prediction and Risk Assessment in Car-Following Scenarios Using a Noise-Enhanced Generative Adversarial Network

Trajectory Prediction and Risk Assessment in Car-Following Scenarios Using a Noise-Enhanced Generative Adversarial Network

A Single-Stage SOC Reference Trajectory Prediction Method for Series PHEV Based on GRNN

In this paper, a single-stage SOC reference trajectory (SOC-RT) prediction method is proposed. Different from the two-stage prediction method mostly used in previous studies, the proposed strategy predicts the SOC-RT slope directly based on the operating condition, instead of predicting the vehicle speed first and then solving it based on a global optimization algorithm. Therefore, the proposed method can significantly reduce the computational load and has high application value. A generalized regression neural network (GRNN) is employed as the prediction model for its strong nonlinear fitting ability. Meanwhile, to solve the problem of error accumulation due to the slope prediction, the prediction trajectory is corrected by the linear trajectory, and the correction factor is determined based on particle swarm optimization algorithm (PSO). Finally, the trajectory is followed by equivalent consumption minimization strategy (ECMS) and the performance of the proposed strategy is verified. The results show that the prediction accuracy is improved by 52.10% and 3.73% compared to the linear trajectory under the two tested cycles, respectively. Meanwhile, the proposed strategy achieves the best fuel economy, with fuel consumption decreasing by 2.61% and 6.40%, respectively, compared to the rule-based control strategy. Finally, its real-time control performance in a real hardware environment is verified through the hardware-in-the-loop test.

Partitioned polygenic risk scores identify distinct types of metabolic dysfunction-associated steatotic liver disease

Metabolic dysfunction-associated steatotic liver disease (MASLD) is characterized by an excess of lipids, mainly triglycerides, in the liver and components of the metabolic syndrome, which can lead to cirrhosis and liver cancer. While there is solid epidemiological evidence that MASLD clusters with cardiometabolic disease, several leading genetic risk factors for MASLD do not increase the risk of cardiovascular disease, suggesting no causal relationship between MASLD and cardiometabolic derangement. In this work, we leveraged measurements of visceral adiposity identifying 27 previously unknown genetic loci associated with MASLD (n = 36,394), six replicated in four independent cohorts (n = 3,903). Next, we generated two partitioned polygenic risk scores based on the presence of lipoprotein retention in the liver. The two polygenic risk scores suggest the presence of at least two distinct types of MASLD, one confined to the liver resulting in a more aggressive liver disease and one that is systemic and results in a higher risk of cardiometabolic disease. These findings shed light on the heterogeneity of MASLD and have the potential to improve the prediction of clinical trajectories and inform precision medicine approaches.

A two-stage method with twin autoencoders for the degradation trajectories prediction of lithium-ion batteries

A two-stage method with twin autoencoders for the degradation trajectories prediction of lithium-ion batteries

Multi-modal intelligent situation awareness in real-time air traffic control: Control intent understanding and flight trajectory prediction

Multi-modal intelligent situation awareness in real-time air traffic control: Control intent understanding and flight trajectory prediction

Stable pedestrian trajectory prediction method with dynamic fusion of multiple features in dense crowds

Abstract Predicting pedestrian trajectories is an essential task within the realms of computer vision and artificial intelligence, aiming to forecast the future motion paths of pedestrians based on historical data. Current research primarily focuses on extracting factors that influence pedestrian movement and independently modeling these factors to predict future pedestrian trajectories. However, existing methods overlook the varying degrees of influence that different factors may have on pedestrian movement at different time steps. To address this issue, we, after extracting the influencing factors, employ a model-adaptive approach to model the importance of different factors at each time step and further predict pedestrian trajectories. Specifically, we utilize a simple multi-factor feature extraction method to extract various influencing factors. Subsequently, we employ a Transformer-based dynamic weight fusion mechanism to dynamically adjust the weights of different factors on pedestrian movement at different time steps, thereby achieving pedestrian trajectory prediction. Our method demonstrates significant superiority in dense crowds, with experimental results showing an improvement of 6% and 31% over state-of-the-art approaches.

Kernel-Based Metrics Learning for Uncertain Opponent Vehicle Trajectory Prediction in Autonomous Racing

Kernel-Based Metrics Learning for Uncertain Opponent Vehicle Trajectory Prediction in Autonomous Racing

Improving generative trajectory prediction via collision-free modeling and goal scene reconstruction

Improving generative trajectory prediction via collision-free modeling and goal scene reconstruction

Needle Trajectory Prediction for Percutaneous Kidney Biopsy in 5G-Powered Teleultrasound Navigation System

Needle Trajectory Prediction for Percutaneous Kidney Biopsy in 5G-Powered Teleultrasound Navigation System

AIS Data-Based Hybrid Predictor for Short-Term Ship Trajectory Prediction Considering Uncertainties

AIS Data-Based Hybrid Predictor for Short-Term Ship Trajectory Prediction Considering Uncertainties

An Empirical-Informed Model for the Early Degradation Trajectory Prediction of Lithium-ion Battery

An Empirical-Informed Model for the Early Degradation Trajectory Prediction of Lithium-ion Battery

Efficient Vehicle Trajectory Prediction With Goal Lane Segments and Dual-Stream Cross Attention

Efficient Vehicle Trajectory Prediction With Goal Lane Segments and Dual-Stream Cross Attention

A Novel Multimodal Long-Term Trajectory Prediction Scheme for Heterogeneous User Behavior Patterns

A Novel Multimodal Long-Term Trajectory Prediction Scheme for Heterogeneous User Behavior Patterns

Conditional Goal-Oriented Trajectory Prediction for Interacting Vehicles.

Predicting future trajectories of pairwise traffic agents in highly interactive scenarios, such as cut-in, yielding, and merging, is challenging for autonomous driving. The existing works either treat such a problem as a marginal prediction task or perform single-axis factorized joint prediction, where the former strategy produces individual predictions without considering future interaction, while the latter strategy conducts conditional trajectory-oriented prediction via agentwise interaction or achieves conditional rollout-oriented prediction via timewise interaction. In this article, we propose a novel double-axis factorized joint prediction pipeline, namely, conditional goal-oriented trajectory prediction (CGTP) framework, which models future interaction both along the agent and time axes to achieve goal and trajectory interactive prediction. First, a goals-of-interest network (GoINet) is designed to extract fine-grained features of goal candidates via hierarchical vectorized representation. Furthermore, we propose a conditional goal prediction network (CGPNet) to produce multimodal goal pairs in an agentwise conditional manner, along with a newly designed goal interactive loss to better learn the joint distribution of the intermediate interpretable modes. Explicitly guided by the goal-pair predictions, we propose a goal-oriented trajectory rollout network (GTRNet) to predict scene-compliant trajectory pairs via timewise interactive rollouts. Extensive experimental results confirm that the proposed CGTP outperforms the state-of-the-art (SOTA) prediction models on the Waymo open motion dataset (WOMD), Argoverse motion forecasting dataset, and In-house cut-in dataset. Code is available at https://github.com/LiDinga/CGTP/.