Accurate motion prediction is a cornerstone of safe autonomous driving and a key enabler for building efficient intelligent transportation systems in Internet of Things environments. Mainstream methods typically follow a divided paradigm: first modeling scene dependencies and then generating trajectories in a one-shot manner. Despite their promising performance, persistent challenges still exist, especially regarding insufficient future representation learning due to constrained information and suboptimal motion decoding from overlooking varying temporal correlations. In this paper, we propose FLAMD, a novel trajectory prediction framework that integrates multi-level feature learning with multi-scale motion decoding. Different from previous methods with decoupled design, FLAMD introduces a joint optimization scheme for multimodal motion representation learning and scene context modeling. Equipped with the carefully designed motion-aware feature interaction module and cross-consistency enhancement mechanism, FLAMD promotes long-range interactions and feature co-evolution between historical observations and anticipated scenarios, guided by diverse future-oriented queries. This mutual interaction enables deeper reasoning about scene dynamics, leading to context-aware and future-guided representations. In the decoding phase, FLAMD proposes to extend traditional single-stage decoding into a multi-scale framework. Multiple horizon-specific decoders operate at different temporal resolutions, each custom-made for the complexity of its assigned prediction window. This structure helps to capture temporal correlations and allocates appropriate feature granularity across varying future steps, thereby improving the capacity of trajectory generation. Extensive experiments on two real-world benchmarks demonstrate the superiority of our approach in generating accurate and reliable predictions in diverse traffic scenarios.

Predicting the future trajectories of moving agents serves as a pivotal endeavor in advancing safe autonomous driving forward within the context of the evolving Internet of Things technology. Despite significant progress driven by deep learning methods, a gap in predictive capability remains when compared to experienced human drivers, particularly in scenarios that demand heightened perception and broader situational understanding. In this study, we propose Brimory, a novel trajectory prediction approach inspired by human driving, designed to equip autonomous vehicles with humanoid memory systems. The core concept of Brimory is to emulate human driving processes by enabling the model to deeply comprehend traffic scenes, establish working memory, and iteratively accumulate to form driving experience. Brimory first introduces the working memory generator, which processes multiple interactions between the scene elements in the driving environment. It is noted that unlike traditional models confined to the time domain, Brimory also extracts underlying dependencies in the frequency domain. The long-term memory builder module is then developed to adaptively retrieve information from working memory and gradually accumulate driving experience with the help of the designed iterative updating mechanism. To mitigate the impact of irrelevant components on long-term memory effectiveness, we devise a co-modulation strategy that retains meaningful representations in a learnable manner. Finally, the combined humanoid memories are leveraged by the multimodal trajectory predictor to forecast future motions. Extensive experiments on benchmark datasets demonstrate the superiority, feasibility, and efficiency of Brimory. The results underscore the potential of humanoid memory frameworks in trajectory prediction, offering a promising path toward the realization of safer autonomous vehicles.

Predicting future motions of target agents is crucial to ensuring the safety of autonomous vehicles in Internet of Things environments. Although significant progress has been made in this field, most mainstream approaches rely heavily on high-definition (HD) maps, which may not always be available or accurate owing to the high costs of map construction and the potential localization errors. Without the explicit guidance of HD maps, trajectory prediction would become more challenging. To address this challenge, we present MLB-Traj, an innovative framework for map-free motion prediction based on local behavior queries. MLB-Traj leverages the observation that agents often follow local behavior patterns in specific traffic scenarios, where these local behaviors reveal the potential trajectories of the targets and contain scenario-consistent information. It starts with a hierarchical dynamic modal query paradigm that first captures the scene’s general modal characteristics and then models target-specific properties. A dual Transformer query mechanism aggregates multiscale relationships to facilitate this process. To tackle potential inconsistency in map-free forecasting, we introduce a trajectory consistency module. It ensures the continuity of inferred trajectories by utilizing patch-wise interaction representations to capture local temporal dependencies, while also learning more robust representations by simulating the model’s response to spatial inconsistency in its predictions. Extensive experiments conducted on real-world datasets validate the effectiveness of MLB-Traj. The results indicate that our framework outperforms existing methods, highlighting its superiority in generating accurate predictions in map-free settings.

Predicting the future trajectories of dynamic traffic actors is the Gordian knot for autonomous vehicles to achieve collision-free driving. Most existing works suffer from a gap in characterizing the evolving interactions of scenario components and ensuring the physical feasibility of predictions, particularly in highly heterogeneous scenarios. Therefore, we propose an Enhanced Multi-Stream Interaction Network (EMSIN), which is devoted to providing accurate trajectory predictions. EMSIN highlights several threads of high-level time-varying interactions, including agent-traffic semantic, self-trend, and agentagent dependencies. A novelly-designed trend-aware mechanism is developed to capture the self-trend interactions from different representation subspaces sufficiently. To model the spatial information of traffic agents and extract their evolutions, we present a dynamic adaptive graph convolutional network that extends previously predefined graph paradigms. The adaptive and dynamic graphs in EMSIN are created using learnable node embeddings, allowing the model to discern interaction strengths without additional attention modules. Finally, all highlevel feature spaces elaborating multi-stream interactions are fused to generate possible agent actions with corresponding confidence values. Comprehensive experiments conducted on both L5kit and nuScenes datasets demonstrate that EMSIN surpasses its counterparts, boasting smaller prediction errors and faster inference times. This study also introduces a fuzzy-based metric to probe the physical feasibility of predicted trajectories, providing valuable insights into appraising the performance of various prediction models from the perspective of fuzziness.

This article studies the unmanned aerial vehicle (UAV)-assisted wireless powered network, where a UAV is dispatched to wirelessly charge multiple ground nodes (GNs) by using radio frequency (RF) energy transfer and then the GNs use their harvested energy to upload the sensed information to the UAV. At each moment, the UAV is scheduled to charge the GNs or only one GN is scheduled to upload its data. An optimization problem is formulated to minimize the average Age of Information (AoI) of the GNs by jointly optimizing the trajectory of the UAV and the scheduling of information transmission and energy harvesting of GNs. As the problem is a combinational optimization problem with a set of binary variables, it is difficult to be solved. Thus, it is modeled as a Markov problem with large state spaces and a deep${Q}$network (DQN)-based scheme is proposed to find its near-optimal solution on the basis of the deep reinforcement learning (DRL) framework. Two nets are structured with artificial neural network (ANN), where one is for evaluating the reward of the action performed in current state, and the other is for predicting realistic action. The corresponding state spaces, the efficient action spaces, and reward function are designed. Simulation results demonstrate the convergence of the proposed DQN scheme, which also show that the proposed DQN scheme gets much smaller average AoI than the three other known schemes. Moreover, by involving the energy punishment in the reward, the UAV may save its energy but yield higher AoI. Additionally, the effects of the packet size, the transmit power, and the distribution area of GNs on the GNs’ average AoI are also discussed, which are expected to provide some useful insights.