Browsing by Author "Mo, Xiaoyu"
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Item Open Access Driver steering behaviour modelling based on neuromuscular dynamics and multi‑task time‑series transformer(Springer, 2024-01-11) Xing, Yang; Hu, Zhongxu; Mo, Xiaoyu; Hang, Peng; Li, Shujing; Liu, Yahui; Zhao, Yifan; Lv, ChenDriver steering intention prediction provides an augmented solution to the design of an onboard collaboration mechanism between human driver and intelligent vehicle. In this study, a multi-task sequential learning framework is developed to predict future steering torques and steering postures based on upper limb neuromuscular electromyography signals. The joint representation learning for driving postures and steering intention provides an in-depth understanding and accurate modelling of driving steering behaviours. Regarding different testing scenarios, two driving modes, namely, both-hand and single-right-hand modes, are studied. For each driving mode, three different driving postures are further evaluated. Next, a multi-task time-series transformer network (MTS-Trans) is developed to predict the future steering torques and driving postures based on the multi-variate sequential input and the self-attention mechanism. To evaluate the multi-task learning performance and information-sharing characteristics within the network, four distinct two-branch network architectures are evaluated. Empirical validation is conducted through a driving simulator-based experiment, encompassing 21 participants. The proposed model achieves accurate prediction results on future steering torque prediction as well as driving posture recognition for both two-hand and single-hand driving modes. These findings hold significant promise for the advancement of driver steering assistance systems, fostering mutual comprehension and synergy between human drivers and intelligent vehicles.Item Open Access Map-adaptive multimodal trajectory prediction using hierarchical graph neural networks(IEEE, 2023-04-26) Mo, Xiaoyu; Xing, Yang; Liu, Haochen; Lv, ChenPredicting the multimodal future motions of neighboring agents is essential for an autonomous vehicle to navigate complex scenarios. It is challenging as the motion of an agent is affected by the complex interaction among itself, other agents, and the local roads. Unlike most existing works, which predict a fixed number of possible future motions of an agent, we propose a map-adaptive predictor that can predict a variable number of future trajectories of an agent according to the number of lanes with candidate centerlines (CCLs). The predictor predicts not only future motions guided by single CCLs but also a scene-reasoning prediction and a motion-maintaining prediction. These three kinds of predictions are produced integrally via a single graph operation. We represent the driving scene with a heterogeneous hierarchical graph containing nodes of two types. An agent node contains its dynamics feature encoded from its historical states, and a CCL node contains the CCL's sequential feature. We propose a hierarchical graph operator (HGO) with an edge-masking technology to regulate the information flow in graph operations and obtain the encoded scene feature for the trajectory decoder. Experiments on two large-scale real-world driving datasets show that our method realizes map-adaptive prediction and outperforms strong baselines.Item Open Access Multi-agent trajectory prediction with heterogeneous edge-enhanced graph attention network(IEEE, 2022-02-01) Mo, Xiaoyu; Huang, Zhiyu; Xing, Yang; Lv, ChenSimultaneous trajectory prediction for multiple heterogeneous traffic participants is essential for safe and efficient operation of connected automated vehicles under complex driving situations. Two main challenges for this task are to handle the varying number of heterogeneous target agents and jointly consider multiple factors that would affect their future motions. This is because different kinds of agents have different motion patterns, and their behaviors are jointly affected by their individual dynamics, their interactions with surrounding agents, as well as the traffic infrastructures. A trajectory prediction method handling these challenges will benefit the downstream decision-making and planning modules of autonomous vehicles. To meet these challenges, we propose a three-channel framework together with a novel Heterogeneous Edge-enhanced graph ATtention network (HEAT). Our framework is able to deal with the heterogeneity of the target agents and traffic participants involved. Specifically, agents' dynamics are extracted from their historical states using type-specific encoders. The inter-agent interactions are represented with a directed edge-featured heterogeneous graph and processed by the designed HEAT network to extract interaction features. Besides, the map features are shared across all agents by introducing a selective gate-mechanism. And finally, the trajectories of multiple agents are predicted simultaneously. Validations using both urban and highway driving datasets show that the proposed model can realize simultaneous trajectory predictions for multiple agents under complex traffic situations, and achieve state-of-the-art performance with respect to prediction accuracy. The achieved final displacement error (FDE@3sec) is 0.66 meter under urban driving, demonstrating the feasibility and effectiveness of the proposed approach.