This paper presents a learning-based model predictive trajectory planning controller for automated driving in unstructured, dynamic environments with obstacle avoidance. We first address the problem of lacking prior knowledge in unstructured environments by introducing a risk map that maps the density and motion of obstacles and the road to an occupancy risk. Model predictive control is then used to integrate trajectory planning and tracking control into one framework to bridge the gap between planning and control. Meanwhile, we use Gaussian Process (GP) regression to learn the residual model uncertainty for improving the model accuracy. An objective function considering both risks within the feasible region and vehicle dynamics is carefully formulated to obtain collision-free and kinematically-feasible local trajectories. Field experiments are performed on real unstructured environments with our automated vehicle. Experimental results demonstrate the effectiveness of the proposed algorithm for successful obstacle avoidance in various complex unstructured scenarios.

Simultaneous localization and mapping (SLAM) plays an important role in the state estimation of mobile robots. Most popular LiDAR SLAM (L-SLAM) methods extract feature points only from the geometric structure of the environment, which can result in inaccurate localization in degenerated scenarios. In this article, we present a novel framework for LiDAR intensity gradient enhanced tightly coupled LiDAR-inertial odometry (IGE-LIO). The framework proposes a novel LiDAR intensity gradient-based feature extraction approach for accurate pose estimation, overcoming the challenges faced by L-SLAM in degenerated environments. After computing the intensity gradient of each LiDAR point, we dynamically extract intensity edge points (IEPs) from texture information. In addition, we extract geometric planar points (GPPs) and geometric edge points (GEPs) based on geometric information. Then, the error analysis is performed on each type of feature points, and the weighting functions are designed to correct measurement noise and mitigate biases introduced by the additional uncertainty in feature extraction. Subsequently, an iterative extended Kalman filter (IEKF) framework is constructed by combining residuals from point-to-plane and point-to-edge associations. Finally, extensive experiments are conducted in indoor, outdoor, and LiDAR degenerated scenarios. The results demonstrate the significantly improved robustness and accuracy of our proposed method compared with the existing geometric-only methods, especially in LiDAR degenerated scenarios.

Currently, high-fidelity simulation techniques, such as discrete element methods, are widely employed for predicting the off-road mobility of unmanned ground vehicles, ensuring the successful completion of field missions. However, the traversal simulation process based on discrete element terrain models is highly time-consuming. To address this challenge, we propose a double neural networks enhanced global mobility prediction model. Firstly, acceleration of the vehicle-terrain contact simulation is achieved by developing an artificial neural network surrogate model. This model predicts stress and strain relationships based on a multiscale representative volume element terrain model, effectively replacing complex mechanical calculations. Secondly, the generation of the global mobility map is expedited by introducing a boundary data enhanced artificial neural network model. This model generates a training dataset by simulating Gaussian sampled terrain data and additional enhanced terrain data at the mobility classification boundary. The trained artificial neural network is subsequently utilized to predict the mobility map, thus circumventing the time-consuming process of traversing simulations. The simulation tests validate that our proposed method not only significantly reduces computation time but also ensures high prediction accuracy.

To avoid unmanned ground vehicles being obstructed by deformed terrain in off-road, effective vehicle mobility analysis is required. However, the computational complexity of existing mobility analysis methods, such as discrete element analysis, poses significant challenges when applied to large-scale terrains. To address this problem, we propose an efficient and high-fidelity vehicle mobiliy prediction method for a large-scale terrain. Initially, precise terrain models are constructed employing Gaussian sampling, thereby serving as optimal inputs for the mobility simulation. Subsequently, we introduce a co-simulation method based on a multi-body dynamics model and discrete element analysis to obtain high-fidelity vehicle mobility data on sampled terrains. Following that, the mobility data is utilized to train a PSO-kriging neural network, enabling accurate predictions of the global mobility map. Through rigorous simulation experiments, the proposed method demonstrates its remarkable effectiveness.

With the rapid advancement of technologies related to unmanned ground systems, ground vehicles are being widely deployed across various domains. However, when operating in complex, soft terrain environments, the low bearing capacity of such terrains poses a significant challenge to vehicle mobility. This paper presents a comprehensive review of mobility prediction methods for ground vehicles in off-road environments. We begin by discussing the concept of vehicle mobility, followed by a systematic and thorough summary of the primary prediction methods, including empirical, semi-empirical, numerical simulation, and machine learning approaches. The strengths and weaknesses of these methods are compared and analyzed in detail. Subsequently, we explore the application scenarios of mobility prediction in military operations, subsea work, planetary exploration, and agricultural activities. Finally, we address several existing challenges in current mobility prediction methods and propose exploratory research directions focusing on key technologies and applications, such as real-time mobility prediction, terrain perception, path planning on deformable terrain, and autonomous mobility prediction for unmanned systems. These insights aim to provide valuable reference points for the future development of vehicle mobility prediction methods.

Three-dimensional (3D) object detection is crucial for autonomous driving, yet current PointPillar feature-based methods face challenges like under-segmentation, overlapping, and false detection, particularly in occluded scenarios. This paper presents a novel dual-stage improved PointPillar feature-based 3D object detection method (S2*-ODM) specifically designed to address these issues. The first innovation is the introduction of a dual-stage pillar feature encoding (S2-PFE) module, which effectively integrates both inter-pillar and intra-pillar relational features. This enhancement significantly improves the recognition of local structures and global distributions, enabling better differentiation of objects in occluded or overlapping environments. As a result, it reduces problems such as under-segmentation and false positives. The second key improvement is the incorporation of an attention mechanism within the backbone network, which refines feature extraction by emphasizing critical features in pseudo-images and suppressing irrelevant ones. This mechanism strengthens the network’s ability to focus on essential object details. Experimental results on the KITTI dataset show that the proposed method outperforms the baseline, achieving notable improvements in detection accuracy, with average precision for 3D detection of cars, pedestrians, and cyclists increasing by 1.04%, 2.17%, and 3.72%, respectively. These innovations make S2*-ODM a significant advancement in enhancing the accuracy and reliability of 3D object detection for autonomous driving.