Transportation mode classification is a key task in trajectory data mining. It adds human behaviour semantics to raw trajectories for trip recommendation, traffic management and transport planning. Previous approaches require heavy pre-processing and feature extraction processes in order to build a classifier, which is complicated and time-consuming. Recurrent neural network has demonstrated its capacity in sequence modelling tasks ranging from machine translation, speech recognition to image captioning. In this paper, we pro-pose a trajectory transportation mode classification framework that is based on an end-to-end bidirectional LSTM classifier. The proposed classification process does not require any feature extraction process, but automatically learns features from trajectories, and use them for classification. We further improve this framework by feeding the time interval as an external feature by embedding. Our experiments on real GPS datasets demonstrate that our approach outperforms existing methods with regard to AUC.

Spatio-temporal trajectory classification is a fundamental problem for location-based services with many real-world applications such as travel mode classification, animal mobility detection, and location recommendation. In the literature, many approaches have been proposed to solve this classification task including deep learning models like LSTM recently for sequence classification. However, these approaches fail to consider both spatial and temporal interval information simultaneously, but share some common drawbacks: omitting either the spatial information or the temporal interval information out. Some models like Time-LSTM, have been proposed to handle the temporal interval information for spatio-temporal trajectories, but they do not take into account the spatial information. Note that, considering both spatial and temporal interval information is crucial for spatio-temporal data mining in order not to miss any spatio-temporal pattern. In this study, we propose a trajectory classifier called Spatio-Temporal GRU to better model the spatio-temporal correlations and irregular temporal intervals prevalently present in spatio-temporal trajectories. We introduce a novel segmented convolutional weight mechanism to capture short-term local spatial correlations in trajectories and propose an additional temporal gate to control the information flow related to the temporal interval information. Performance evaluation demonstrates that our proposed model outperforms popular deep learning approaches for the travel model classification problem.

Online Multi-Object Tracking (MOT) plays a pivotal role in autonomous systems. The state-of-the-art approaches usually employ a tracking-by-detection method, and data association plays a critical role. This paper proposes a learning and graph-optimized (LEGO) modular tracker to improve data association performance in the existing literature. The proposed LEGO tracker integrates graph optimization, which efficiently formulates the association score map, facilitating the accurate and efficient matching of objects across time frames. To further enhance the state update process, the Kalman filter is added to ensure consistent tracking by incorporating temporal coherence in the object states to further enhance the state update process. Our proposed method, utilising LiDAR alone, has shown exceptional performance compared to other online tracking approaches, including LiDAR-based and LiDAR-camera fusion-based methods. LEGO ranked 3^rd among all trackers (both online and offline) and 2^nd among all online trackers in the KITTI MOT benchmark for cars¹, at the time of submitting results to KITTI object tracking evaluation ranking board. Moreover, our method also achieves competitive performance on the Waymo open dataset benchmark.

Meteorological imagery prediction is an important and challenging problem for weather forecasting. It can also be seen as a video frame prediction problem that estimates future frames based on observed meteorological imageries. Despite it is a widely-investigated problem, it is still far from being solved. Current state-of-the-art deep learning based approaches mainly optimise the mean square error loss resulting in blurry predictions. We address this problem by introducing a Meteorological Predictive Learning GAN model (in short MPL-GAN) that utilises the conditional GAN along with the predictive learning module in order to handle the uncertainty in future frame prediction. Experiments on a real-world dataset demonstrate the superior performance of our proposed model. Our proposed model is able to map the blurry predictions produced by traditional mean square error loss based predictive learning methods back to their original data distributions, hence it is able to improve and sharpen the prediction. In particular, our MPL-GAN achieves an average sharpness of 52.82, which is 14% better than the baseline method. Furthermore, our model correctly detects the meteorological movement patterns that traditional unconditional GANs fail to do.

Cooperative perception (CP) is essential to enhance the efficiency and safety of future transportation systems, requiring extensive data sharing among vehicles on the road, which raises significant privacy concerns. Federated learning offers a promising solution by enabling data privacy-preserving collaborative enhancements in perception, decision-making, and planning among connected and autonomous vehicles (CAVs). However, federated learning is impeded by significant challenges arising from data heterogeneity across diverse clients, potentially diminishing model accuracy and prolonging convergence periods. This study introduces a specialized federated learning framework for CP, termed the federated dynamic weighted aggregation (FedDWA) algorithm, facilitated by dynamic adjusting loss (DALoss) function. This framework employs dynamic client weighting to direct model convergence and integrates a novel loss function that utilizes Kullback-Leibler divergence (KLD) to counteract the detrimental effects of non-independently and identically distributed (Non-IID) and unbalanced data. Utilizing the BEV transformer as the primary model, our rigorous testing on FedBEVT dataset which is expanded on OpenV2V dataset, demonstrates significant improvements in the average intersection over union (IoU). These results highlight the substantial potential of our federated learning framework to address data heterogeneity challenges in CP, thereby enhancing the accuracy of perception models and facilitating more robust and efficient collaborative learning solutions in the transportation sector.