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该论文提出一个基于回答集编程(ASP)的混合声明式框架，用于对交互式运动轨迹进行可解释的前瞻性推理，并在Argoverse 2基准上进行评估，每个预测轨迹都附带可检查的事件级假设。
该论文提出了一种自适应多判别器 Wasserstein GAN（MD-WGAN）框架，将强化学习与博弈论协调结合，用于资源受限的车联网环境，实现与最先进 GAN 相当的预测精度（RMSE 1.029），同时显著降低 CPU 与内存消耗。
提出上下文感知注意力高斯混合模型（CAA-GMM），用于自动驾驶中多模态、不确定性感知的车辆轨迹预测，在nuScenes和Argoverse 2上取得与最先进栅格基线相当或更优的精度，同时保持较低计算复杂度和对通信/感知不确定性的鲁棒性。
This paper proposes RRDrive, a risk-aware occupancy representation for register-based end-to-end autonomous driving, which improves trajectory prediction and selection in complex scenes, achieving notable performance gains.
The paper introduces a flow-matching model for predicting aircraft trajectories using ADS-B data, achieving superior performance over traditional methods in probabilistic trajectory prediction.
This paper proposes a two-stage physics-constrained autoregressive transformer model to predict driver stop-go decisions and longitudinal trajectories at signalized intersections, using real-world data to enhance traffic safety.
M3-Former is a multimodal transformer model that uses mixture-of-experts and LLM-enhanced semantic fusion to predict long-term vessel trajectories with improved accuracy and robustness.
This paper introduces Wave Function Backpropagation (WFB), a wave-parameterized learning formulation that explicitly models temporal intervals to improve neural network performance in trajectory prediction, achieving up to 20.4% reduction in average displacement error.
This paper proposes CRHT, a Continuous Regression Hybrid Transformer for vessel trajectory prediction using AIS data, featuring an online K-means cluster sampling strategy and a CNN-Transformer hybrid architecture to address geographic bias and improve short-term forecasting accuracy.
This paper introduces Hybrid Probabilistic Zonotopes (HProbZ), a neural network output head that jointly models discrete modes, bounded drift, and stochastic noise with closed-form likelihood, enabling identifiable uncertainty decomposition, observation-driven contraction, and multi-modal conformal coverage.
INTraJ is a unified framework for trajectory prediction that decomposes social influence into two stages: planning with future social information and local reaction from residuals, achieving state-of-the-art results on Argoverse 2, ETH/UCY, and SDD benchmarks.
This thesis introduces polynomial representations for long-term traffic scene prediction in autonomous driving, showing improved computational efficiency, generalization, and prediction plausibility over sequence-based baselines, validated on Argoverse 2 and Waymo Open datasets.
DreamTraj predicts 6-DoF object trajectories from a single RGB image and a language instruction by decoding internal video diffusion latents, eliminating the need for video, depth, or CAD models at inference. It introduces the MOVEdataset with fine-grained language-to-motion annotations and achieves state-of-the-art performance while running 4.6x faster than generate-then-extract pipelines.
This paper proposes the Conditional Informer, a Transformer-based encoder-decoder for ship trajectory prediction that conditions vessel dynamics on environmental contexts via a Conditional Attention mechanism, achieving 15.4% accuracy improvement and robust fallback via Modality Masking.
This paper introduces TrajRS, an extension of Randomized Smoothing that provides certified robust radii for pedestrian trajectory predictors, offering verifiable safety guarantees against adversarial perturbations.
This paper introduces a method called 'Learn to Cluster' to quantify and interpret social interactions among pedestrians for better trajectory prediction. It uses probabilistic latent variable generative learning to cluster social interactions without labels, improving robustness for autonomous driving and social robots.
This paper introduces M-CTX, an exact and scalable spatial context retrieval framework for trajectory analytics that reduces context construction time from 17 CPU-days to 1.8 hours on a 5.48M-anchor maritime corpus, by replacing brute-force stages with index-backed operators.
This paper compares 19 graph neural network layer types for modelling interactions in driving trajectory prediction, finding ARMA, Chebyshev, and topology-aware layers most effective and offering design principles for better prediction models.
Qwen-VLA is a unified vision-language-action model for embodied decision-making, integrating manipulation, navigation, and trajectory prediction across different robot platforms. It uses a DiT-based action decoder and embodiment-aware prompt conditioning, achieving strong performance and out-of-distribution generalization.
SleepWalk is a three-tier benchmark for evaluating vision-language models' ability to predict spatially coherent trajectories in 3D environments from textual instructions and visual observations, revealing systematic failures in grounded spatial reasoning under occlusions and multi-step instructions.