Aug 2026· ACM Transactions on Software Engineering and Methodology· 0 citations· 96 references
TL;DR
A comprehensive empirical study is conducted that confirms the limitations of MLPrior in trajectory prediction scenarios and demonstrates that TrajectoryTest outperforms all existing test input prioritization techniques, including the state-of-the-art approach MLPrior, multiple confidence-based methods, and the baseline random selection.
Abstract
Trajectory prediction models have become increasingly vital in various safety-critical domains, such as healthcare, maritime surveillance, and intelligent transportation systems. These applications utilize machine learning models to predict the operational state of a moving agent based on its motion trajectory. Despite their widespread deployment in these domains, testing such models remains a significant challenge due to the high cost of manual labeling, further exacerbated by the large scale and domain-specific characteristics of trajectory datasets. Test input prioritization has emerged as a promising solution to address the labeling cost issue, aiming to identify potentially misclassified inputs early to accelerate debugging and improve testing efficiency. The current state‑of‑the‑art test prioritization technique that can be applied to trajectory prediction tasks is MLPrior. However, MLPrior has the following limitations when applied to trajectory prediction scenarios: 1) MLPrior relies on rich input features, which are limited in trajectory data. Trajectory inputs typically contain only basic spatiotemporal coordinates (e.g., latitude, longitude, timestamps), offering insufficient attribute richness. 2) MLPrior inherits the limitations of learning‑based approaches. When the training data are imbalanced, their effectiveness can be significantly reduced. To overcome these limitations, we propose TrajectoryTest, a trajectory-specific test prioritization strategy that integrates trajectory-related information with MLPrior-derived representations to rank test inputs. Moreover, TrajectoryTest employs an adaptive strategy that switches between learning-based and uncertainty-based ranking depending on the model’s prediction error ratio. We conduct a comprehensive empirical study that confirms the limitations of MLPrior in trajectory prediction scenarios and demonstrates that TrajectoryTest outperforms all existing test input prioritization techniques, including the state-of-the-art approach MLPrior, multiple confidence-based methods, and the baseline random selection. The experimental results show that TrajectoryTest achieves improvements ranging from 7.03% to 9.56% over MLPrior (the state-of-the-art method) and confidence-based approaches on natural datasets, and from 6.71% to 9.94% on noisy datasets.
A comprehensive Explainable AI (XAI) evaluation framework is introduced, including temporal sensitivity analysis, interaction-aware perturbation studies, spatial influence analysis, and gradient-based feature attribution methods that provide insights into how the model captures temporal motion dependencies, neighboring vehicle interactions, and environmental context during trajectory prediction.
R. Megalingam, Naveen Prasaad Selvarajan, Pritty Vijay· Italian National Conference...· 0 citations
Deep learning-based vessel trajectory prediction using Automatic Identification System (AIS) has become a hot topic in the fields of maritime traffic monitoring, situational awareness, and navigational decision support. However, most previous studies have focused primarily on end-to-end model training using trajectory data from a single water area, which limits the resulting models’ ability to generalize to regions with different traffic patterns. To address this issue, this study proposes a method that constructs traffic context from historical AIS records at multiple geographic resolutions using H3, a hexagonal hierarchical spatial indexing system, and integrates this context with a Transformer-based trajectory predictor. A reliability-aware selector determines the contribution of the context to the final prediction, conditioning this decision on the vessel’s motion state and the retrieved historical patterns. Experiments on AIS data from three distinct water areas demonstrated that H3-indexed context improved cross-water prediction accuracy without requiring model retraining on the target area. These findings demonstrate that H3-indexed context, structured at multiple geographic resolutions and integrated through a selective mechanism, serves as transferable spatial context for vessel trajectory prediction.
Ramp merging areas present critical safety challenges due to complex vehicle interactions and significant speed differences, which frequently lead to elevated collision risks. With the rapid development of connected and automated vehicle (CAV) technologies, advanced trajectory planning has emerged as a crucial capability to ensure safe in complex traffic environments. However, traditional trajectory planning methods for CAVs often encounter bottlenecks in ramp merging scenarios. They primarily rely on deterministic prediction models, thereby lacking the necessary adaptability to highly dynamic and uncertain traffic flows. To address these limitations, we propose a novel trajectory planning framework specifically designed for ramp merging situations, with a primary focus on enhancing safety. Our work mainly includes two contributions. First, we construct a multimodal predictive risk field (MPRF) to better capture the uncertainties in vehicle behaviors. This involves two key steps: 1) developing a multimodal trajectory prediction model based on an modified interactive multiple model (IMM) Kalman filter, which integrates a priority mechanism and refined motion models to predict lateral and longitudinal movements more accurately; and 2) constructing and calibrating a dynamic risk field using real-world aerial trajectory data processed through YOLO-based computer vision methods, ensuring the risk field reflects diverse driving behaviors. Second, based on MPRF, we design two risk matrices: one for path planning and one for speed planning. This separation allows the trajectory planner to independently optimize path and speed, better handling multimodal prediction uncertainties and dynamic-static coupling. Simulation experiments show that our proposed method significantly improves safety performance while maintaining traffic efficiency.
Baofeng Sun, Hong-Chao Liang, Guodong Ma et al.· Accident Analysis and Preven...· 0 citations
Building accurate decision-support tools for next-generation air traffic control requires robust trajectory prediction models. We present a flow-matching architecture trained exclusively on historical aircraft trajectories, with no route labels or chart supervision. Trained on 1.15 million Automatic Dependent Surveillance-Broadcast trajectory windows collected over the San Francisco Bay Area, the model generates aircraft trajectory distributions that closely match historical traffic, reproducing known airspace structure around San Francisco Airport such as the shape of SFO's published NIITE FOUR departure procedure. Our model is trained directly on the native, irregular ADS-B sampling interval. Trajectory prediction is cast as sequence inpainting using a block-causal Transformer that denoises future state tokens conditioned on the observed history using Conditional Flow Matching or Denoising Diffusion Probabilistic Models. We compare our architecture against constant-velocity, deterministic-Long Short Term Memory, and Conditional Variational Autoencoders baselines. At matched parameter count, CFM outperforms DDPM by 11-26% in minADE@20, and both generative objectives surpass the CVAE baseline by 31-41%. We further show that the error degrades gracefully with prediction horizon, and the architecture remains effective when retrained on temporally decimated feeds. Lastly, we sample $K$ independent completions, yielding spatial probabilistic occupancy estimates that can serve as input to downstream conflict-risk estimation.
Mathurin Petit, Emir Torun, Louis Brusset et al.· 0 citations
Comparative analysis reveals that no single technical route can fully address the coupled challenges of uncertainty, accuracy, and real-time performance, underscoring that hybrid frameworks are essential for balancing these competing requirements.
Vessel trajectory prediction is a key basis for port traffic monitoring, collision-risk identification and navigation decision support. However, AIS data are often affected by irregular sampling, noise and complex manoeuvring behaviours in port waters, making it difficult for models to simultaneously capture global navigation trends and local motion variations. To address this issue, this study proposes a Transformer-based trajectory prediction model enhanced by multi-scale temporal motion encoding, termed MTME-Transformer. The model introduces temporal convolutional branches with different kernel sizes into the Transformer encoder to capture motion patterns over short, medium and wider temporal receptive fields, and is evaluated under a unified data preprocessing, resampling and multi-step autoregressive prediction framework. Experimental results show that, under a 2-min sampling interval and a 30-min prediction horizon, MTME outperforms the Transformer and RNN-based comparison models across multiple evaluation metrics. Ablation experiments further indicate that larger kernel scales are more important for trajectory extrapolation. These results suggest that multi-scale local motion modelling can stably improve the accuracy of AIS-based vessel trajectory prediction.
Qi Xu, Hua-Sheng Nong, Tianwei Ma et al.· International Conference on...· 0 citations
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