Zhangyun Wang

Ensuring the safe driving and decision-making of autonomous vehicles requires reliable modeling of the multimodal motion of vehicles. In this paper, we propose a novel scenario-guided diffusion probabilistic model called RoadDiff to address these issues. By modeling and interacting with scene-contextual heterogeneous data at the feature level, we achieve lightweight and high-quality representations of traffic dynamics. We interpolate these representations as guiding conditions into a stochastic iterative denoising process of reverse diffusion, progressively transforming a known prior distribution into a multimodal trajectory distribution that includes the ground truth mode. Our well-designed Transformer-based neural network approximates the denoising matching function of the reverse diffusion process, and our region of interest-based training strategy ensures that the sampled multimodal trajectories conform to road and physical constraints to overcome mode blur. We carefully design two loss functions to ensure multimodal prediction for regions of interest and efficient trajectory guidance during this process. Experimental results on real-world motion datasets demonstrate that RoadDiff outperforms state-of-the-art methods in terms of prediction multimodality and accuracy, indicating its potential as a novel paradigm for vehicular multimodal motion prediction.

A reliable driving trajectory prediction of surrounding vehicles is an essential reference for decision-making and safe driving of an autonomous vehicle. Although predicting short-term trajectories can be well achieved, it is still very challenging for long-term prediction of trajectories since the prediction space grows exponentially. In this paper, we propose a novel architecture for trajectory prediction from factored intention estimation (I2T), which decouples the trajectory prediction space into a high-level space for intention estimation and a low-level space for motion prediction. The long-term dependencies between intention cues and future motions during driving are naturally extended to the internal sharing mechanism of I2T, leading to improved performance. Furthermore, we design a Prioriformer model to serve as the backbone network for I2T so that it can accurately capture the long-term dependency couplings related to the task of intention estimation or motion prediction. Prioriformer model adopts a personalized normalization method, which facilitates learning latent representations of long-term features and avoids getting stuck on local optimum. A designed multi-scale fusion encoder extracts features from various receptive fields and then learns richer information from the representation subspaces. An efficient non-autoregressive decoder reduces the pressure in long-term prediction of trajectories while avoiding cumulative errors. Experiments on three real-world motion datasets show that I2T can significantly outperform the state-of-the-art.

Unmanned Aerial Vehicles (UAVs) can be employed as low-altitude aerial base stations (UAV-BSs) to provide communication services for ground users (GUs). However, most existing works mainly focus on optimizing coverage and maximizing throughput, without considering the fairness of the GUs in communication services. This may result in certain GUs being underserviced by UAV-BSs in pursuit of maximum throughput. In this paper, we study the problem of UAV-assisted communication with the consideration of user fairness. We first design a Ratio Fair (RF) metric by weighting fairness and throughput to evaluate the tradeoff between fairness and communication efficiency when UAV-BSs serve GUs. The problem is formulated as a mixed-integer non-convex optimization problem based on the RF metric and we propose a UAV-Assisted Fair Communication (UAFC) algorithm based on multi-agent deep reinforcement learning to maximize the fair throughput of the system. The UAFC algorithm comprehensively considers fair throughput, UAV-BSs coverage, and flight status to design a reasonable reward function. In addition, the UAFC algorithm establishes an information sharing mechanism based on gated functions by sharing neural networks, which effectively reduces the distributed decision-making uncertainty of UAV-BSs. To reduce the impact of state dimension imbalance on the convergence of the algorithm, we design a new state decomposing and coupling actor network architecture. Simulation results show that the proposed UAFC algorithm increases fair throughput by 5.62%, 26.57% and fair index by 1.99%, 13.82% compared to the MATD3 and MADDPG algorithms, respectively. Meanwhile, UAFC can also meet energy consumption limitation and network connectivity requirement.