In complex and dynamic urban traffic scenarios, the accurate prediction of trajectories of surrounding traffic participants (vehicles, pedestrians, etc) with interactive behaviours plays an important role in the navigation and the motion planning of the ego vehicle. In this paper, based on the graph neural network (GNN), we propose a hierarchical GNN framework to model interactions of heterogeneous traffic participants (vehicles, pedestrians and riders) combined with LSTM to predict their trajectories. The proposed framework consists of two modules with two GNNs for interactive events recognition (IER) and trajectory prediction (TP). The IER module is used to recognise interactive events between traffic participants and the ego vehicle. With the recognised results as the input, the TP module is built for interactive trajectory prediction. In addition, to realise the multi-step prediction, a long short-term memory network (LSTM) is combined with GNN in the TP module. The proposed hierarchical framework is verified by the naturalistic driving data collected from the urban traffic environment. Comparative results with state-of-the-art methods indicate that the hierarchical GNN framework obtains an outstanding performance in the recognition of interactive events and the prediction of interactive behaviours.

The recognition of driver's braking intensity is of great importance for advanced control and energy management for electric vehicles. In this paper, the braking intensity is classified into three levels based on novel hybrid unsupervised and supervised learning methods. First, instead of selecting threshold for each braking intensity level manually, an unsupervised Gaussian mixture model is used to cluster the braking events automatically with brake pressure. Then, a supervised Random Forest model is trained to classify the correct braking intensity levels with the state signals of vehicle and powertrain. To obtain a more efficient classifier, critical features are analyzed and selected. Moreover, beyond the acquisition of discrete braking intensity level, a novel continuous observation method is proposed based on artificial neural networks to quantitative analyze and recognize the brake intensity using the prior determined features of vehicle states. Experimental data are collected in an electric vehicle under real-world driving scenarios. Finally, the classification and regression results of the proposed methods are evaluated and discussed. The results demonstrate the feasibility and accuracy of the proposed hybrid learning methods for braking intensity classification and quantitative recognition with various deceleration scenarios.

Effectively predicting interactive behaviors of traffic participants in the urban road is the key to successful decision-making and motion planning of intelligent vehicles. In this article, based on the data collected from vehicle on-board sensors, a graph-neural-network-based multitask learning framework (GNN-MTLF) is proposed to accurately predict trajectories of traffic participants with interactive behaviors. The interactive behavior considered in this research includes interactive events and trajectories that are modeled as spatial-temporal graphs using the GNN. Under the GNN-MTLF, the prediction process contains two main parts: recognition of interactive events and prediction of interactive trajectories. An integrated loss function is designed for multitask learning with the purpose of prediction and recognition. The proposed framework is verified using naturalistic driving data in the urban road. Experimental results show a superior performance of the GNN-MTLF compared to baseline methods and the potential for improving the road mobility.

The path planning of unmanned differential steering vehicles (UDSVs) in the off-road environment not only needs to consider the non-complete constraints of vehicles but also faces the challenges of complex off-road terrains and obstacles. In this paper, an integrated path planning system is proposed to handle the influence of kinematic vehicle model, off-road terrains and obstacles systematically for the path planning of UDSVs. To improve the planning efficiency, a Pre-planning is designed and carried out using the Voronoi diagram established in the 3D environment with obstacles. By combining the potential field functions (PFF) related to passable obstacles and 3D terrains, an integrated PFF is defined to represent the movement cost of UDSV in the nonlinear optimal control (NOC) problem. Based on the NOC, a channel path planning (CPP) problem is formulated to avoid the untraceable path caused by the traditional line path planning (LPP). Simulation results show that the proposed system can plan a feasible path fast with the constraints from vehicle kinematics, obstacle avoidance and off-road terrains.