Modelling, predicting and analysing driver behaviours are essential to advanced driver assistance systems (ADAS) and the comprehensive understanding of complex driving scenarios. Recently, with the development of deep learning (DL), numerous driver behaviour learning (DBL) methods have been proposed and applied in connected vehicles (CV) and intelligent transportation systems (ITS). This study provides a review of DBL, which mainly focuses on typical applications in CV and ITS. First, a comprehensive review of the state-of-the-art DBL is presented. Next, Given the constantly changing nature of real driving scenarios, most existing learning-based models may suffer from the so-called “catastrophic forgetting,” which refers to their inability to perform well in previously learned scenarios after acquiring new ones. As a solution to the aforementioned issue, this paper presents a framework for continual driver behaviour learning (CDBL) by leveraging continual learning technology. The proposed CDBL framework is demonstrated to outperform existing methods in behaviour prediction through a case study. Finally, future works, potential challenges and emerging trends in this area are highlighted.@en

Accurate recognition of driver behaviours is the basis for a reliable driver assistance system. This paper proposes a novel fusion framework for driver behaviour recognition that utilises the traffic scene and driver gaze information. The proposed framework is based on the graph neural network (GNN) and contains three modules, namely, the gaze analysing (GA) module, scene understanding (SU) module and the information fusion (IF) module. The GA module is used to obtain gaze images of drivers, and extract the gaze features from the images. The SU module provides trajectory predictions for surrounding vehicles, motorcycles, bicycles and other traffic participants. The GA and SU modules are parallel and the outputs of both modules are sent to the IF module that fuses the gaze and scene information using the attention mechanism and recognises the driving behaviours through a combined classifier. The proposed framework is verified on a naturalistic driving dataset. The comparative experiments with the state-of-the-art methods demonstrate that the proposed framework has superior performance for driving behaviour recognition in various situations. @en

The deep understanding of the behaviors of traffic participants is essential to guarantee the safety of automated vehicles (AV) in mixed traffic with vulnerable road users (VRUs). Precise trajectory prediction of traffic participants can provide reasonable solution space for motion planning of AV. Early works mainly focused on handcrafting the feature representation and designing complicated architectures in deep learning-based prediction models. However, these approaches overlooked the fact that different road users perceive the safety of the same interaction differently and also exhibit heterogeneous risk-taking styles. In this paper, we will develop a model for trajectory prediction based on risk-taking styles. The model accounts for the expected positions and occupancy of traffic participants in the surrounding environment. It consists of two sequential steps: risk-taking styles of multi-modal road users under interactive scenes are first clustered, and then reformulated in the heterogeneous graph model for trajectory prediction. The model is validated by the driving data collected on the urban road using a public dataset. Comparative experiments demonstrate that the proposed method can predict the trajectory of traffic participants much more accurately than the state-of-the-art methods.@en

Due to the complex and dynamic character of intersection scenarios, the autonomous driving strategy at intersections has been a difficult problem and a hot point in the research of intelligent transportation systems in recent years. This paper gives a brief summary of state-of-the-art autonomous driving strategies at intersections. Firstly, we enumerate and analyze common types of intersection scenarios, corresponding simulation platforms, as well as related datasets. Secondly, by reviewing previous studies, we have summarized characteristics of existing autonomous driving strategies and classified them into several categories. Finally, we point out problems of the existing autonomous driving strategies and put forward several valuable research outlooks.@en

To deal with the nonlinear interference caused by chassis movement and road surface undulations with the tracking and aiming of unmanned combat ground vehicles, a tracking and aiming adaptive control method for unmanned combat ground vehicles on the move based on reinforcement learning compensation is proposed. This method consists of a main controller and a compensation controller. The main controller uses the PID control algorithm combined with the current tracking error to obtain the main control quantity, and the compensation controller uses the Dueling DQN reinforcement learning network to process the current state of the combat vehicle as well as the road surface undulation information near the local planning path to obtain the compensation control quantity. Firstly, the integrated kinematics model of the unmanned combat ground vehicle is established. Then, the compensation control algorithm based on reinforcement learning is described. Finally, simulation and verification are performed in three-dimensional scenes based on the V-REP dynamic software. The experimental results show that the tracking and aiming control method based on reinforcement learning compensation has good adaptive ability for chassis movement and road surface undulations, which effectively improves the tracking/aiming accuracy and stability of unmanned combat vehicles.@en

Intelligent vehicles have achieved a considerable development in technologies and can fulfill the basic functions of autonomous driving in a limited closed environment. However, results of actual road tests show that the current technologies of intelligent vehicles still have many limitations and their large-scale application in complex urban and off-road environments still faces many challenges. As one of the key technologies, the motion planning and control technology has basically formed a complete theoretical system and has been widely applied in engineering. However, the traditional methods still have some defects in practical application, such as the inability of understanding dynamic and complex scenes, poor adaptability for different scenes, high complexity of the model, and difficulty in parameter tuning. Due to the strong ability in knowledge representation and model fitting, machine learning methods have been widely applied in perception and navigation technology for intelligent vehicles. In order to solve the problems of generalization and applicability in traditional motion planning and control techniques, many researchers have also devoted themselves to exploring the usage of deep learning, reinforcement learning, and so on machine learning methods in motion planning and control policy for intelligent vehicles. In this paper, machine learning-based methods were reviewed for motion planning and control in intelligent vehicles, analyzing the existing policy learning methods for motion planning and control from three aspects, including basic framework, basic learning paradigms, and different planning and control methods based on learning. Finally, the research status and future development directions were summarized and prospected.@en

Precisely modeling interactions and accurately predicting trajectories of surrounding vehicles are essential to the decision-making and path-planning of intelligent vehicles. This paper proposes a novel framework based on ensemble learning to improve the performance of trajectory predictions in interactive scenarios. The framework is termed Interactive Ensemble Trajectory Predictor (IETP). IETP assembles interaction-aware trajectory predictors as base learners to build an ensemble learner. Firstly, each base learner in IETP observes historical trajectories of vehicles in the scene. Then each base learner handles interactions between vehicles to predict trajectories. Finally, an ensemble learner is built to predict trajectories by applying two ensemble strategies on the predictions from all base learners. Predictions generated by the ensemble learner are final outputs of IETP. In this study, three experiments using different data are conducted based on the NGSIM dataset. Experimental results show that IETP improves the predicting accuracy and decreases the variance of errors compared to base learners. In addition, IETP exceeds baseline models with 50% of the training data, indicating that IETP is data-efficient. Moreover, the implementation of IETP is publicly available at https://github.com/BIT-Jack/IETP.@en

In urban environments, the complex and uncertain intersection scenarios are challenging for autonomous driving. To ensure safety, it is crucial to develop an adaptive decision making system that can handle the interaction with other vehicles. Manually designed model-based methods are reliable in common scenarios. But in uncertain environments, they are not reliable, so learning-based methods are proposed, especially reinforcement learning (RL) methods. However, current RL methods need retraining when the scenarios change. In other words, current RL methods cannot reuse accumulated knowledge. They forget learned knowledge when new scenarios are given. To solve this problem, we propose a hierarchical framework that can autonomously accumulate and reuse knowledge. The proposed method combines the idea of motion primitives (MPs) with hierarchical reinforcement learning (HRL). It decomposes complex problems into multiple basic subtasks to reduce the difficulty. The proposed method and other baseline methods are tested in a challenging intersection scenario based on the CARLA simulator. The intersection scenario contains three different subtasks that can reflect the complexity and uncertainty of real traffic flow. After offline learning and testing, the proposed method is proved to have the best performance among all methods.@en

This paper proposes a hierarchical path tracking control framework divided into the upper controller and the lower controller for double motors independently driven unmanned high-speed tracked vehicle. The upper layer generates rolling speed command of the dual-side tracks using model predictive control. The Euler method, the second-order Runge-Kutta method and the revised fourth-order Runge-Kutta method are applied to compare the control performance in this layer. Meanwhile, to surmount control command execution delay, two compensatory methods based on control law in time domain are proposed. The lower controller translates the tracks speed command from the upper layer into motors torque according to the tracked vehicle dynamic model. Experiments show that the root-mean-square lateral error is 0.1176m and the root-mean-square heading error is 0.7552° when applying the second-order Runge-Kutta method that has the best tracking accuracy in straight off-road roads at 10m/s. Experiments also demonstrate that the control framework has good tracking performance in high-speed tracking straight path and low-speed tracking curved path. Results show the steady condition lateral error of 70km/h pavement tracking is less than 0.2m and 10km/h right-angled soft off-road road is less than 0.6m.@en