Abstract--Trajectory forecasting for autonomous driving must deliver accurate, calibrated multi-modal futures under tight compute and latency constraints. We propose a compact hybrid quantum architecture that aligns quantum inductive bias with road-scene structure by operating in an ego-centric, lane-aligned frame and predicting residual corrections to a kinematic baseline instead of absolute poses. The model combines a transformer-inspired quantum attention encoder (9 qubits), a parameter-lean quantum feedforward stack (64 layers, 1200 trainable angles), and a Fourier-based decoder that uses shallow entanglement and phase superposition to generate 16 trajectory hypotheses in a single pass, with mode confidences derived from the latent spectrum. All circuit parameters are trained with Simultaneous Perturbation Stochastic Approximation (SPSA), avoiding back-propagation through non-analytic components. In the Waymo Open Motion Dataset, the model achieves minADE (minimum A verage Displacement Error) of 1.94 m and minFDE (minimum Final Displacement Error) of 3.56m in the 16 models predicted over the horizon of 2.0 s, consistently outperforming a kinematic baseline with reduced miss rates and strong recall. Ablations confirm that residual learning in the lane frame, truncated Fourier decoding, shallow entanglement, and spectrum-based ranking focus capacity where it matters, yielding stable optimization and reliable multi-modal forecasts from small, shallow quantum circuits on a modern autonomous-driving benchmark. CCURA TE short-horizon trajectory forecasting under uncertainty is a central requirement for autonomous driving. An effective forecaster must reason about multiple plausible futures (e.g., straight vs. turn), remain well-calibrated, and operate under strict latency and compute budgets.

Multi-agent trajectory prediction is a key task in computer vision for autonomous systems, particularly in dense and interactive environments. Existing methods often struggle to jointly model goal-driven behavior and complex social dynamics, which leads to unrealistic predictions. In this paper, we introduce VISTA, a recursive goal-conditioned transformer architecture that features (1) a cross-attention fusion mechanism to integrate long-term goals with past trajectories, (2) a social-token attention module enabling fine-grained interaction modeling across agents, and (3) pairwise attention maps to show social influence patterns during inference. Our model enhances the single-agent goal-conditioned approach into a cohesive multi-agent forecasting framework. In addition to the standard evaluation metrics, we also consider trajectory collision rates, which capture the realism of the joint predictions. Evaluated on the high-density MADRAS benchmark and on SDD, VISTA achieves state-of-the-art accuracy with improved interaction modeling. On MADRAS, our approach reduces the average collision rate of strong baselines from 2.14% to 0.03%, and on SDD, it achieves a 0% collision rate while outperforming SOTA models in terms of ADE/FDE and minFDE. These results highlight the model's ability to generate socially compliant, goal-aware, and interpretable trajectory predictions, making it well-suited for deployment in safety-critical autonomous systems.

Uncertainty modeling is critical in trajectory forecasting systems for both interpretation and safety reasons.

--Accurate vessel trajectory prediction is essential for enhancing situational awareness and preventing collisions. Still, existing data-driven models are constrained mainly to single-vessel forecasting, overlooking vessel interactions, navigation rules, and explicit collision risk assessment. We present a transformer-based framework for multi-vessel trajectory prediction with integrated collision risk analysis. For a given target vessel, the framework identifies nearby vessels. It jointly predicts their future trajectories through parallel streams encoding kinematic and derived physical features, causal convolutions for temporal locality, spatial transformations for positional encoding, and hybrid positional embeddings that capture both local motion patterns and long-range dependencies. Evaluated on large-scale real-world AIS data using joint multi-vessel metrics, the model demonstrates superior forecasting capabilities beyond traditional single-vessel displacement errors. By simulating interactions among predicted trajectories, the framework further quantifies potential collision risks, offering actionable insights to strengthen maritime safety and decision support. Maritime shipping is critical not only for global trade and economy but also for various socio-economic activities, including fishing, passenger transportation, and recreational sailing [1]. To enhance navigational safety, the International Maritime Organization (IMO) mandated the use of the Automatic Identification System (AIS) in 2003, with satellite AIS integration in 2008, further expanding monitoring coverage [2], [3]. Consequently, the widespread adoption of AIS generates a vast volume of vessel movement data, which has spurred research to address maritime challenges.

Accurate prediction of road user movement is increasingly required by many applications ranging from advanced driver assistance systems to autonomous driving, and especially crucial for road safety. Even though most traffic accident fatalities account to bicycles, they have received little attention, as previous work focused mainly on pedestrians and motorized vehicles. In this work, we present the Great GATsBi, a domain-knowledge-based, hybrid, multimodal trajectory prediction framework for bicycles. The model incorporates both physics-based modeling (inspired by motorized vehicles) and social-based modeling (inspired by pedestrian movements) to explicitly account for the dual nature of bicycle movement. The social interactions are modeled with a graph attention network, and include decayed historical, but also anticipated, future trajectory data of a bicycles neighborhood, following recent insights from psychological and social studies. The results indicate that the proposed ensemble of physics models -- performing well in the short-term predictions -- and social models -- performing well in the long-term predictions -- exceeds state-of-the-art performance. We also conducted a controlled mass-cycling experiment to demonstrate the framework's performance when forecasting bicycle trajectories and modeling social interactions with road users.

The details of datasets used in this work are summarised in Table 3. In these datasets, there is no personally identifiable information. In the case of the Gowalla dataset, the included POIs are across different cities, whereas FS-NYC/FS-TKY are only about a single city. As for the Education, there is only one morning peak. POIs with different semantic meanings have different visiting patterns.

This paper's source code and experimental data are available at