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 autonomous vehicle is widely applied in various ground operations, in which motion planning and tracking control are becoming the key technologies to achieve autonomous driving. In order to further improve the performance of motion planning and tracking control, an efficient hierarchical framework containing motion planning and tracking control for the autonomous vehicles is constructed in this paper. Firstly, the problems of planning and control are modeled and formulated for the autonomous vehicle. Then, the logical structure of the hierarchical framework is described in detail, which contains several algorithmic improvements and logical associations. The global heuristic planning based artificial potential field method is developed to generate the real-time optimal motion sequence, and the prioritized Q-learning based forward predictive control method is proposed to further optimize the effectiveness of tracking control. The hierarchical framework is evaluated and validated by the numerical simulation, virtual driving environment simulation and real-world scenario. The results show that both the motion planning layer and the tracking control layer of the hierarchical framework perform better than other previous methods. Finally, the adaptability of the proposed framework is verified by applying another driving scenario. Furthermore, the hierarchical framework also has the ability for the real-time application.

Unmanned ground vehicles (UGVs) have great potential in the application of both civilian and military fields, and have become the focus of research in many countries. Environmental perception technology is the foundation of UGVs, which is of great significance to achieve a safer and more efficient performance. This article firstly introduces commonly used sensors for vehicle detection, lists their application scenarios and compares the strengths and weakness of different sensors. Secondly, related works about one of the most important aspects of environmental perception technology—vehicle detection—are reviewed and compared in detail in terms of different sensors. Thirdly, several simulation platforms related to UGVs are presented for facilitating simulation testing of vehicle detection algorithms. In addition, some datasets about UGVs are summarized to achieve the verification of vehicle detection algorithms in practical application. Finally, promising research topics in the future study of vehicle detection technology for UGVs are discussed in detail.

Pedestrian detection is an important branch of computer vision, and it has important applications in the fields of autonomous driving, artificial intelligence and video surveillance.With the rapid development of deep learning and the proposal of large-scale datasets, pedestrian detection has reached a new stage and achieves better performance. However, the performance of state-of-the-art methods is far behind the expectation, especially when occlusion and scale variance exist. Therefore, a lot of works focused on occlusion and scale variance have been proposed in the past few years. The purpose of this article is to make a detailed review of recent progress in pedestrian detection. Firstly, brief progress of pedestrian detection in the past two decades is summarized. Secondly, recent deep learning methods focusing on occlusion and scale variance are analyzed. Moreover, the popular datasets and evaluation methods for pedestrian detection are introduced. Finally, the development trend of pedestrian detection is prospected.

In this article, we propose a novel personalized planning and control approach for lane change assistance system which can efficiently learn a model prediction control (MPC)-based driver-specific lane-changing policy via end-to-end imitation learning from a few driver demonstrations. Specifically, we build a novel learnable predictive model of the vehicle-driver system and design an adaptable cost function for the MPC-based lane change controller. We then calculate the gradient of the imitation loss with respect to the personalization parameters of the model and cost function via differentiating the optimality conditions, and update those parameters to minimize the imitation loss in an end-to-end fashion. A semi-physical simulation on a driving simulator and a closed-loop test on a real vehicle are conducted to validate the learning ability and personalized control performance. The results show that 1) the proposed method can automatically implement both the generalized and the personalized lane change planning and control by learning from demonstration data; 2) the proposed controller can adapt to different driver-specific behaviors; and 3) the proposed approach outperforms the model-free learning approach in terms of imitation accuracy, interpretability, data efficiency, and generalized performance.