Monocular 3D object detection is attractive for autonomous driving due to its low cost, but its performance degrades severely under adverse visual conditions that corrupts appearance cues and destabilizes depth-related representations. We propose Robo-DETR, a robustness-aware depth-guided transformer framework that combines minimal visual-conditioned input preconditioning with detector-internal depth reliability correction. Specifically, a lightweight Condition Recognition and Enhancement Block stabilizes low-level visual cues, while a Condition-aware Depth-guided Block refines depth logits via foreground-aware supervision and structured dynamic/region-level adjustment to suppress visual-induced depth noise. A dual-branch transformer decoder further promotes depth-appearance consistency through modality-specific cross-attention and fusion. Experiments on KITTI-C and the real-world TJ4DRadSet demonstrate consistent improvements over competitive monocular detectors under diverse degradations, and ablations validate the contribution of each component.

Internet-of-Things (IoT) sensing and V2X connectivity are expanding the deployment of electrified unmanned ground vehicles (UGVs) in rugged off-road environments. However, safe and energy-efficient navigation on uneven terrain remains challenging because terrain understanding, vehicle attitude prediction, and trajectory refinement are often handled in isolation. To address this limitation, this paper presents UT-Planner, an integrated energy- and stability-aware planning framework for IoT-enabled UGVs that exploits high-fidelity point-cloud maps. First, a wheel-contact-based attitude prediction module simulates four-wheel interactions on local point-cloud patches to estimate future body attitude and terrain roughness before traversal, thereby forming a predictive traversability layer. Second, an Eco-A∗ search augments Hybrid-A∗ with a Dubins-based energy heuristic that combines path length with terrain-aware energy surrogates to generate short and energy-favorable coarse paths. Third, a multi-objective trajectory optimizer refines the coarse path by jointly minimizing smoothness, gravity-aligned energy expenditure, and attitude deviation. Experiments on four representative uneven-terrain maps show that, relative to strong baselines, UT-Planner shortens the final 3D path length by 9.4%, reduces real energy consumption by 12.4%, improves trajectory smoothness by 13.3%, lowers the maximum tracking error by 19.8%, and also reduces peak body tilt. These results demonstrate a practical route to safe and energy-efficient off-road autonomous navigation.

Roadside vision centric 3D object detection has received increasing attention in recent years. It expands the perception range of autonomous vehicles, enhances the road safety. Previous methods focused on predicting per-pixel height rather than depth, making significant gains in roadside visual perception. While it is limited by the perspective property of near-large and far-small on image features, making it difficult for network to understand real dimension of objects in the 3D world. Bird’s Eye View (BEV) features and voxel features present the real distribution of objects in 3D world compared to the image features. However, BEV features tend to lose details due to the lack of explicit height information, and voxel features are computationally expensive. Inspired by this insight, an efficient framework learning height prediction in voxel features via transformer is proposed, dubbed HeightFormer. It groups the voxel features into local height sequences, and utilize attention mechanism to obtain height distribution prediction. Subsequently, the local height sequences are reassembled to generate accurate voxel features. The proposed method is applied to two large-scale roadside benchmarks, DAIR-V2X-I and Rope3D. Extensive experiments are performed and the HeightFormer outperforms the state-of-the-art methods in roadside vision centric 3D object detection task. Code will be athttps://github.com/zhangzhang2024/HeightFormer

As autonomous vehicle (AV) technology advance, their ability to navigate uneven terrains, such as mountainous areas, becomes increasingly important. However, current trajectory tracking methods struggle with tracking accuracy and stability due to insufficient consideration of terrain slopes and vehicle kinematics. In this article, we propose a manifold-based model predictive control framework designed for Ackermann steering electrified vehicles on uneven terrains. This method models the trajectory tracking control on manifolds, utilizing acceleration and front-wheel steering angle as control inputs to enhance control stability. To mitigate model inaccuracies and enhance the controller’s adaptability, we dynamically adjust the controller’s objective function weights based on trajectory curvature and integrate a PID feedback mechanism to provide real-time compensation for vehicle speed and steering angle. When fitting the surface terrain equation, we select sparse key points along the reference trajectory, achieving lightweight computation while maintaining high-fitting accuracy. The experimental and simulation results demonstrate that the proposed UT-MPC controller improves tracking performance by 53.74% and 42.68% compared to four baseline methods when tracking a trajectory on different uneven terrain maps. Real-world experiments have also demonstrated the effectiveness of UT-MPC.

Pedestrian trajectory prediction is crucial in various fields, but remains challenging due to complex spatial interactions. While existing pedestrian trajectory prediction methods show promise, they often fail to capture these dynamics effectively. To address this limitation, a Hierarchical Spatial Interaction Graph Convolutional Network (HSIGCN) is proposed to handle both group interactions and spatial interactions. Although previous methods have attempted to model group behaviors, they lack a comprehensive consideration of group interactions and often oversimplify the complex social dynamics in groups. HSIGCN introduces a novel group interaction mechanism that encompasses four types of interactions: all-pedestrian, intra-group, out-group, and inter-group interactions, enhancing the expressiveness in group behavior prediction. Furthermore, current approaches to spatial interaction sparsification either rely solely on prior-based or on learning-based methods. HSIGCN innovatively combines both approaches to form a mixed sparsification mechanism, effectively filtering all-pedestrian and out-group interactions. Additionally, existing prior-based methods fail to consider social factors comprehensively. HSIGCN takes into account the field of view (FOV), collision awareness, and distance factors to establish a more robust prior-based sparse function. Experimental results on ETH and UCY datasets demonstrate that the proposed method significantly outperforms baseline models, showcasing its potential to accurately predict pedestrian trajectories by effectively handling complex spatial interactions.

The construction of local HD (High Definition) Map and Lane Network with onboard sensors is critical for autonomous vechicles and facilitates downstream tasks. In contrast to previous studies that treated building HD Map and Lane Network as two individual tasks, in this paper a unified BEV (Bird’s-Eye-View) perception framework is proposed with seperate decoders to realize two tasks simultaneously. In this paper, the gap between object detection and curve regression when using a DETR-like decoder is discussed and a curve region aware method is proposed to make up for the above gap. Specifically, a mechanism called Curve Region Aware Deformable Attention is designed with a bezier grid sampling module to guide the attention learning in bev features and structual loss regarding shapes of lanelines is also included. Moreover, a BEV spatial-temporal fusion method is introduced to better utilize historical features with minimal loss of spatial information. The results on NuScenes dataset show that our work has been close to or exceeded SOTAs (state-of-the-art) on both two tasks simultaneously.

Pedestrian trajectory prediction is crucial across various domains, but remains challenging due to complex spatial interactions. Existing Graph Convolutional Network (GCN) methods show promise but often fail to capture these dynamics effectively. To address this limitation, a Sparse Directed Attention Graph Convolutional Network (SDAGCN) is proposed to handle both social interactions among pedestrians and self-interactions within individuals. Traditional GCN-based methods often model social interactions as undirected or dense graphs. However, due to the field of view and awareness of collision avoidance of pedestrians, they tend to focus unilaterally on specific neighbors. To reflect this, SDAGCN constructs a sparse and directed spatial graph that considers these attributes innovatively. Furthermore, the attention weights of pedestrians towards their neighbors are closely tied to spatial conflicts. The conflicts are deeply influenced by relative velocity and distance. Therefore, these attributes are leveraged to calculate the attention weights. These two components form the Sparse Directed Attention (SDA) mechanism, which effectively discerns the influence of neighbors on a target pedestrian in various situations. Additionally, the self-interaction of each pedestrian is significantly influenced by their speed. To capture variations in self-interaction across different states, SDAGCN employs a single-layer perceptron with the square of pedestrian speed as input. Experiments conducted on the ETH and UCY datasets demonstrate that our method outperforms other GCN-based spatial interaction methods, showcasing its potential in accurately predicting pedestrian trajectories by effectively handling complex social and self-interactions.

This paper presents a novel interactive motion planning approach for left-turning of autonomous vehicles at uncontrolled intersections. A typical left-turning scenario that consists of agent vehicles and an autonomous ego vehicle is established. The autonomous ego vehicle aims to cross the uncontrolled intersection quickly and safely when meeting with the agent vehicles. A modified obstacle reciprocal collision avoidance (MORCA) prediction model is proposed to predict the trajectory of the agent vehicles with the considerations of longitudinal and lateral interaction-aware behaviors. The parameters of the MORCA prediction model are optimized using particle swarm optimization (PSO) with the inD datasets. Based on MORCA, a partially observable Markov decision process (POMDP) framework is established. The action and state spaces of the POMDP are extended reasonably to satisfy the requirement of real-time application. The simulation results show that the prediction precision is improved by 15% using the MORCA compared with the obstacle reciprocal collision avoidance (ORCA) prediction model. Meanwhile, the results manifest that the proposed MORCA-based POMDP planning method improves 43.8% in commuting efficiency compared with a traditional rule-based method, nearly as good as the vehicle-to-vehicle (V2V) communication is equipped. Note to Practitioners—The motivation of this article is to establish a safe and efficient planning framework for the left-turning of autonomous vehicles at uncontrolled intersections. Rule-based approaches were widely used in the previous studies but they are not optimal. In this paper, the lateral movements of vehicles at the intersection are considered for the first time. A MORCA prediction model is established and optimized to formulate a POMDP planning framework. The CARLA simulation demonstrates the effectiveness of the proposed MORCA-based POMDP planner. In future work, we will migrate the algorithm to a real-world test platform for experimentation.

Speed planning system is generally equipped for intelligent and connected vehicles (ICVs). Under the circumstances of autonomous driving, an energy-optimal speed trajectory is usually desired, particularly on urban arterial roads with complex traffic conditions involved. However, the existing speed planning solutions in the literature have not dealt with the problem of time consuming. Also, the ego vehicle could not perfectly track a precalculated speed reference because of the dynamically varying traffic. Thus, optimal speed planning cannot always be guaranteed. In this article, a fast optimal speed planning system for complex urban driving situations is established through an adaptive hierarchical control framework. In the planning layer, dynamic programming (DP) and the interior-point optimizer are jointly used to compute the global speed trajectory with access to signal phase and timing (SPaT) information. The computational burden is greatly alleviated based on a weighted orientation graph assumption and problem decomposition. The following layer utilizes the Informer, which is a transformer-based model, to predict preceding vehicle speed. Then, a target-switching model-predictive controller (MPC) is adopted for global speed trajectory following and adaption. The proposed approach significantly reduces speed planning computation time compared to previous solutions. Simulation results based on real road traffic scenes manifest that 22.0% of energy is saved compared with human driving.