Vehicle trajectory prediction is a keystone for the application of the internet of vehicles (IoV). With the help of deep learning and big data, it is possible to understand the between-vehicle interaction pattern hidden in the complex traffic environment. In this paper, we propose a novel spatial-temporal dynamic attention network for vehicle trajectory prediction, which can comprehensively capture temporal and social patterns in a hierarchical manner. The social relation between vehicles is captured at each timestamp and thus retains the dynamic variation of interaction. The temporal correlation in terms of individual motion state as well as social interaction is captured by different sequential models. Furthermore, a driving intention-specific feature fusion mechanism is proposed such that the extracted temporal and social features can be integrated adaptively for the maneuver-based multi-modal trajectory prediction. Experimental results on two real-world datasets show that compared with the state-of-the-art algorithms, our proposal achieves comparable prediction performance for short-term prediction, however, works much better for long-term prediction. Additionally, various ablation analysis is provided to evaluate the effectiveness of our proposed network components. The code will be available at https://xbchen82.github.io/resource/ .

Brain functional connectivity (FC) network, estimated with resting‐state functional magnetic resonance imaging (RS‐fMRI) technique, has emerged as a promising approach for accurate diagnosis of neurodegenerative diseases. However, the conventional FC network is essentially low‐order in the sense that only the correlations among brain regions (in terms of RS‐fMRI time series) are taken into account. The features derived from this type of brain network may fail to serve as an effective disease biomarker. To overcome this drawback, we propose extraction of novel high‐order FC correlations that characterize how the low‐order correlations between different pairs of brain regions interact with each other. Specifically, for each brain region, a sliding window approach is first performed over the entire RS‐fMRI time series to generate multiple short overlapping segments. For each segment, a low‐order FC network is constructed, measuring the short‐term correlation between brain regions. These low‐order networks (obtained from all segments) describe the dynamics of short‐term FC along the time, thus also forming the correlation time series for every pair of brain regions. To overcome the curse of dimensionality, we further group the correlation time series into a small number of different clusters according to their intrinsic common patterns. Then, the correlation between the respective mean correlation time series of different clusters is calculated to represent the high‐order correlation among different pairs of brain regions. Finally, we design a pattern classifier, by combining features of both low‐order and high‐order FC networks. Experimental results verify the effectiveness of the high‐order FC network on disease diagnosis.Hum Brain Mapp 37:3282–3296, 2016. ©2016 Wiley Periodicals, Inc.

Brain functional connectivity (FC) extracted from resting‐state fMRI (RS‐fMRI) has become a popular approach for diagnosing various neurodegenerative diseases, including Alzheimer's disease (AD) and its prodromal stage, mild cognitive impairment (MCI). Current studies mainly construct the FC networks between grey matter (GM) regions of the brain based on temporal co‐variations of the blood oxygenation level‐dependent (BOLD) signals, which reflects the synchronized neural activities. However, it was rarely investigated whether the FC detected within the white matter (WM) could provide useful information for diagnosis. Motivated by the recently proposed functional correlation tensors (FCT) computed from RS‐fMRI and used to characterize the structured pattern of local FC in the WM, we propose in this article a novel MCI classification method based on the information conveyed by both the FC between the GM regions and that within the WM regions. Specifically, in the WM, the tensor‐based metrics (e.g., fractional anisotropy [FA], similar to the metric calculated based on diffusion tensor imaging [DTI]) are first calculated based on the FCT and then summarized along each of the major WM fiber tracts connecting each pair of the brain GM regions. This could capture the functional information in the WM, in a similar network structure as the FC network constructed for the GM, based only on the same RS‐fMRI data. Moreover, a sliding window approach is further used to partition the voxel‐wise BOLD signal into multiple short overlapping segments. Then, both the FC and FCT between each pair of the brain regions can be calculated based on the BOLD signal segments in the GM and WM, respectively. In such a way, our method can generate dynamic FC and dynamic FCT to better capture functional information in both GM and WM and further integrate them together by using our developed feature extraction, selection, and ensemble learning algorithms. The experimental results verify that the dynamic FCT can provide valuable functional information in the WM; by combining it with the dynamic FC in the GM, the diagnosis accuracy for MCI subjects can be significantly improved even using RS‐fMRI data alone.Hum Brain Mapp 38:5019–5034, 2017. ©2017 Wiley Periodicals, Inc.

As a core function of autonomous driving (AD) and the Internet of Vehicles (IoV), accurately predicting the trajectory of vehicles can significantly improve traffic safety and reduce crash injuries. In this article, we propose an intention-aware non-autoregressive Transformer model with multi-attention learning for multimodal vehicle trajectory prediction. We first present social attention learning (SAL) where graph attention is properly integrated with the Transformer encoder so as to model the social interaction between vehicles. Then, the social and temporal dependency across consecutive frames is captured by temporal attention learning (TAL). The above social and temporal attention modules can be interleaved and stacked to achieve the coupled modeling and thus extract abundant features from trajectory data. To implement precise prediction as well as efficient inference, we further put forward an intention-aware decoder query generation approach to produce multiple possible trajectories concurrently. Finally, cross-attention learning (CAL) is devised to make full use of the encoded features, therefore, yielding future predictions. The proposed model is evaluated on two large-scale vehicle trajectory datasets and the experimental results verify that our algorithm achieves better performance compared with some state-of-the-art models. The root-mean-square error (RMSE) of the predicted trajectory over 5-s time horizon for the next generation simulation (NGSIM) and HighD datasets is 3.43 and 1.10 m, respectively.