Search Results (2,137)

The reliability of the steering system directly impacts the safety of autonomous driving. Addressing the issue of trajectory deviation easily caused by motor failure in redundant steer-by-wire (SBW) systems, this paper aims to improve vehicle tracking accuracy under fault conditions. A hierarchical fault-tolerant control strategy based on a zeroing neural network (ZNN) is proposed: the upper layer uses the Stanley algorithm for path planning, while the lower layer designs a ZNN controller with preset performance constraints, and instantaneous power reconfiguration is achieved through Jacobi pseudo-inverse. Simulation results show that under high-speed lane changes and sinusoidal conditions, this strategy can achieve millisecond-level task reassignment, and compared to PID control, the maximum absolute error of lateral tracking under fault conditions is reduced by over 50%, and the root mean square error is reduced by over 30%. This method effectively improves driving safety and trajectory fidelity when actuators fail. Full article

Autonomous navigation in unstructured off-road environments presents fundamental challenges due to terrain heterogeneity, the absence of structured road markings, and the necessity for real-time traversability reasoning from raw sensory observations. We present TerrainFormer, a hierarchical framework that integrates a world model for terrain dynamics prediction with a temporal decision transformer for action selection. Our methodology employs a two-phase training paradigm: (1) self-supervised world model pretraining on LiDAR point clouds to learn terrain representations encompassing traversability, elevation, and semantic segmentation; (2) behavioral cloning of the decision transformer conditioned on frozen world model features with temporally derived goal directions. The world model processes raw 3D LiDAR point clouds through a PointPillars encoder for real-time bird’s-eye-view (BEV) projection, followed by a Vision Transformer backbone that produces latent terrain representations. A principal contribution is our cross-dataset generalization paradigm: the world model is trained on separate datasets while the decision transformer is trained on separate sequences, ensuring zero data overlap between training phases. We introduce automatic goal direction computation from vehicle pose trajectories, enabling the model to learn directionally conditioned navigation policies. To address the class imbalance inherent in off-road driving data, we employ focal loss with inverse-frequency class weighting and action-chunk supervision. Experimental evaluation on the RELLIS-3D dataset achieves 87.31% test accuracy with 0.7948 macro F1 across all 12 action classes. The world model’s predicted future frames produce only a 0.79% accuracy drop versus ground-truth observations, with 98.82% action agreement, demonstrating effective cross-dataset generalization for real-time off-road navigation. Full article

Firms can gain a competitive advantage through a strategic patent portfolio, wherein patents elucidate technological advancements and establish legal barriers that keep competitors out. However, patents do not provide a perpetual monopoly within the prevailing open innovation paradigm, which means that firms should keep up with innovation input and patent applications to preserve their market dominance. Fostering technological breakthroughs in the patent network thus becomes a critical issue. Anchored in the theoretical views of open innovation, this study conducts an empirical analysis of patent data to examine how patent network structural features influence the technologies’ breakthrough tendency in the field of autonomous driving (AD). The findings indicate that centrality metrics such as degree centrality, harmonic centrality, and betweenness centrality within AD patent networks exert significant influence on technological breakthrough tendency, and the patent family size plays a moderating role in these relationships. Moreover, this research advances theoretical insights for patent strategy formulation in emerging firms of AD, with broader implications for other technology-intensive sectors. Full article

Robust autonomous-driving detection requires using RGB texture and infrared thermal cues without sacrificing real-time inference. Existing RGB-IR detectors often rely on static feature concatenation or quadratic attention, which makes them sensitive to modality imbalance, small spatial offsets, and deployment cost. We propose MobileMamba-DETR, a lightweight DETR-style detector that treats dual-modal fusion as a selective state-space process. Its principal design is an SS2D-based cross-modal interaction module that uses normalized RGB-IR contrast as a guide, while a MobileMamba backbone, spectral–spatial encoder, and dynamic convolutional decoder provide efficient multi-scale representation and query localization. On M3FD and FLIR-ADAS, MobileMamba-DETR achieves mAP${}_{50}$ of 83.6% and 78.3%, respectively, with 38.7M parameters and 42 FPS inference at $640\times 640$ on an RTX 3090. The results, ablations, and seed-based validation show that selective state-space fusion improves accuracy while retaining real-time throughput. Full article

Vehicle-to-Everything (V2X) communication is a critical enabler of autonomous driving, supporting real-time information exchange among vehicles, roadside infrastructure, pedestrians, and cloud services. However, the security of current V2X systems largely relies on classical cryptographic mechanisms, which are expected to become vulnerable in the presence of large-scale quantum computers. Given the long operational lifespan and stringent safety requirements of autonomous vehicular networks, the transition toward quantum-resistant authentication and key management mechanisms has become increasingly important. This paper presents a comprehensive survey of post-quantum Authentication and Key Agreement (AKA) protocols for secure V2X communications. The survey systematically reviews V2X communication architectures, security and privacy requirements, existing authentication frameworks, and emerging post-quantum cryptographic approaches. Representative AKA schemes and NIST-standardized post-quantum algorithms are comparatively analyzed in terms of security strength, computational complexity, communication overhead, storage requirements, scalability, and deployment suitability for resource-constrained vehicular environments. The survey further examines practical implementation challenges, including latency constraints, bandwidth limitations, signature size expansion, memory consumption, and hardware resource requirements. The analysis reveals that achieving quantum-resistant security in V2X networks requires balancing strong cryptographic protection with the stringent performance demands of safety-critical vehicular applications. While recent post-quantum approaches offer promising security guarantees against quantum adversaries, their practical deployment remains constrained by computational and communication overhead. Finally, this survey identifies key research gaps and outlines future directions for the development of lightweight, scalable, and quantum-resilient AKA frameworks capable of supporting next-generation autonomous transportation systems. The findings provide researchers and practitioners with a structured understanding of the opportunities, limitations, and challenges associated with securing future V2X communications in the quantum era. Full article

Decision-making is a fundamental component of autonomous driving, where complex urban scenarios require safe, robust, and adaptable behaviours. This work presents a curriculum learning approach to reduce the dynamics-related reality gap in autonomous driving decision-making through a hybrid architecture that combines learning-based tactical decisions with classical planning and control methods. The proposed methodology follows a staged sim-to-real process: first, the decision-making policies are trained in a lightweight simulator to learn basic kinematic behaviours; then, they are transferred and refined in CARLA to account for vehicle dynamics; subsequently, a digital twin of the real platform and test environment is used for scenario-specific fine-tuning; finally, the resulting architecture is validated through parallel execution with a real vehicle. The proposed approach focuses on vehicle dynamics, actuation response, and scenario geometry rather than on the complete sim-to-real problem for autonomous driving. The approach is evaluated across multiple urban driving scenarios in simulation, including lane changing, roundabouts, merging, and crossroads, while real-world validation is conducted in a controlled merge scenario. Experimental results show that the proposed curriculum improves training efficiency and final performance across the different stages, achieving success rates above 91% in SMARTS. In CARLA, the proposed architecture completes the evaluated scenarios up to 50% faster than the Autopilot baseline while improving comfort and safety-related metrics in terms of acceleration and jerk. The real-world parallel execution experiment further demonstrates the feasibility of transferring the decision-making architecture to a physical vehicle under controlled conditions. Finally, an ablation study quantifies the contribution of each curriculum stage to the overall system performance. Full article

Nonlinear Model Predictive Control (NMPC) has garnered significant attention in autonomous systems due to its ability to predict future states and manage complex vehicle dynamics. However, the adaptability of existing NMPC methods is constrained by having to manually set the weight coefficients in the NMPC cost function. This study aims to explore a novel approach that integrates NMPC with Reinforcement Learning (RL), specifically employing Proximal Policy Optimization (PPO), to dynamically adjust NMPC weight matrices. The investigation begins by establishing a physics-based model for a two wheeled differential drive vehicle. A PPO model is then trained and deployed in real time to adapt to the NMPC weight matrices, achieving a 71% reduction in tracking error compared with the NMPC baseline. Importantly, the performance gain arises from PPO’s ability to reshape the NMPC cost function in real time, amplifying both orientation and lateral penalties in curves while relaxing them on straights, thereby enabling adaptive trade-offs between accuracy and control effort that static-weight NMPC cannot achieve. To enhance safety, the controller is integrated with a Control Barrier Function (CBF) layer for real-time obstacle avoidance, while PPO’s real-time weight adaptation contributes to improved tracking performance relative to NMPC+CBF. Finally, robustness evaluations under friction uncertainty, sensor noise, and path disturbances demonstrate that the PPO+NMPC+CBF method maintains reliable tracking accuracy and safety margins. Full article

Intelligent autonomous driving systems must not only predict the appropriate driving manoeuvre but also provide human-interpretable evidence that justifies the decision. However, existing methods typically address these objectives separately, leading to three practical limitations: multi-stage perception-to-language pipelines can propagate upstream perception errors into downstream explanations; post hoc saliency methods often produce pixel-level highlights that are difficult to interpret semantically; and decoupled decision and explanation modules cannot guarantee that the explanation reflects the same scene evidence used for behaviour prediction. In this paper, we propose a unified framework that jointly performs vehicle behaviour prediction and human-centric interpretation from a shared visual backbone. Specifically, a hierarchical Swin Transformer encodes the driving scene into a sequence of spatial tokens, which are processed by two complementary branches. The first branch, termed the Object Selection Module (OSM), learns a compact scene-level semantic representation through query-guided cross-attention, while the second branch extracts a small set of class-agnostic object-centric tokens without requiring bounding-box or segmentation supervision. These two representations are subsequently integrated by a Semantic–Object Fusion (SOF) module based on scaled dot-product attention, residual connections, and a feed-forward network. The behaviour prediction head operates on the fused representation, whereas the interpretation head leverages the semantic representation through a skip connection to preserve decision-relevant context. For surround-view perception, learnable per-camera embeddings are introduced to maintain viewpoint identity with negligible additional parameter cost. Furthermore, a compact language model fine-tuned via Low-Rank Adaptation (LoRA) generates fluent, label-conditioned natural-language justifications. Extensive experiments on two public benchmarks, BDD-OIA and nu-AD, demonstrate that the proposed framework consistently delivers superior performance and provides effective, human-readable interpretations of driving decisions. Full article

Ensuring safety in autonomous vehicles (AVs) requires predictive control methods that can handle dynamic constraints, uncertain interactions, and real-time decision making. This review examines safety-oriented model predictive control (MPC) for AVs using a PRISMA-guided screening process. From 363 records published between January 2015 and March 2026, 101 peer-reviewed studies were selected for qualitative synthesis. The literature is organized into three domains: collision avoidance and risk mitigation, trajectory tracking and path following, and intersection and coordination tasks. Across these domains, MPC has evolved from nominal tracking and geometric avoidance toward risk-aware, robust, hierarchical, and learning-enhanced formulations. Unlike broader reviews on autonomous driving control, this review focuses specifically on safety-oriented MPC and compares the reviewed literature in terms of safety mechanisms, uncertainty treatment, validation practice, computational feasibility, and deployment limitations. The review shows that MPC remains one of the most versatile frameworks for AV safety, but the evidence base is weakened by heavy reliance on simulation, inconsistent safety metrics, limited validation under uncertainty, and uneven treatment of computational feasibility. The most promising directions are hybrid architectures that combine model-based safety guarantees with uncertainty-aware prediction, learning-assisted adaptation, and scalable coordination mechanisms. Full article

In the automotive industry, the increasing number of vehicle variants necessitates the configuration of electronic control unit (ECU) software through parameterization. However, with the transition toward software-defined vehicles, autonomous driving, and growing regulatory demands, current approaches to parameter management and ECU parameterization have reached their limits. This raises the question of which methodologies for parameter management are applied in other variant-intensive industries, and to what extent the automotive sector can learn from them. To address this question, semi-structured interviews were conducted across several variant-rich industries to examine how parameter management in embedded systems is organized in their respective development processes. The study identified different practices, challenges, and methodologies, including the adoption of software product line engineering principles as a potential best practice. Nevertheless, the findings reveal that a direct transfer of methods from other industries to the automotive industry is not straightforward, as the automotive domain represents a unique combination of high production volumes, extensive variability, and substantial product complexity. Full article

With the rapid development of big data and artificial intelligence, motor fault diagnosis has become increasingly important in the fields of intelligent transportation and new energy vehicles. As a core component of the electric vehicle drive system, the operating condition of an asynchronous AC motor is directly related to the safety and reliability of the vehicle powertrain. Once a motor fault occurs, it may lead to powertrain failure, thereby causing traffic accidents, financial losses, and even threats to human life, particularly under high-speed driving conditions where the safety risks are more severe. Therefore, the timely and accurate diagnosis of motor faults in electric vehicles, especially autonomous vehicles, is of great practical significance. To effectively capture the fault-related features embedded in the vibration and voltage signals of asynchronous AC motors and efficiently perform fault diagnosis, this paper introduces a fault-diagnosis model that integrates the exponential moving average (EMA), a two-dimensional multi-scale convolutional neural network (2DMSCNN), and the Squeeze-and-Excitation Network (SENet) mechanism. Experimental validation based on a publicly available asynchronous AC motor fault-diagnosis dataset demonstrates that, compared with traditional machine learning models and ensemble learning methods, the proposed EMA-2DMSCNN-SENet model achieves higher diagnostic accuracy and stronger robustness. Full article

Autonomous driving, as a core component of intelligent transportation systems, relies highly on precise environmental perception capabilities. Vehicle target detection is the fundamental task of environmental perception. However, complex factors in real scenarios (such as target occlusion, illumination changes, and dense traffic flow) often lead to feature misjudgments, missed detections, target positioning deviations, and category confusions in existing methods. To address these challenges, this paper proposes the MLRP-YOLOv8n model that integrates Mixed Local Channel Attention (MLCA) and large kernel separable attention (LSKA). Three complementary attention mechanisms as well as improved regression loss are integrated into the lightweight YOLOv8n architecture to improve the accuracy of vehicle detection while maintaining computational efficiency. Firstly, MLCA is embedded in the C2f feature extraction module to enhance local feature focus; the SPPF module integrates LSKA optimize multi-scale feature fusion; RFCBAMConv convolution is used to replace the original convolution in the neck to enhance cross-level feature correlation; the PIoUv2 loss function is introduced instead of Complete Intersection over Union (CIoU) to accelerate model convergence and reduce regression errors. Experiments on the KITTI Detection dataset subset and UA-DETRAC datasets show that MLRP-YOLOv8n improves the mean average precision (mAP) by 1.9% and 3.2% respectively on the KITTI Detection dataset subset and UA-DETRAC datasets. This model achieves a balance between detection accuracy, tracking robustness, and computational efficiency, providing a reliable solution for autonomous driving environment perception. Full article

Autonomous navigation on unknown uneven terrain remains a critical challenge for unmanned ground vehicle (UGV) deployed in unstructured environments such as disaster relief, wilderness exploration, and off-road logistics. Existing motion planning methods for such environments suffer from three key limitations: under-utilization of the solution space due to discretized terrain assessment, difficulty in transforming complex terrain safety constraints into optimization-compatible forms, and the inherent trade-off between environmental modeling accuracy and real-time performance. This paper presents a hierarchical motion planning framework that enables safe and fast navigation of UGV on unknown uneven terrain. We first construct a traversability map based on terrain slope, roughness, and sparsity extracted from ground point cloud clusters. Non-traversable points are then transformed via spherical inversion and inverse mapping to generate terrain safety corridors composed of a series of convex polygons. The geometric containment relationship between the vehicle’s convex hull and the corridor is reformulated as continuously differentiable Control Barrier Function (CBF) constraints to ensure driving safety. The front-end employs a kinodynamic Hybrid A* algorithm with a traversability-aware node pruning strategy, while the back-end trajectory optimization embeds the CBF constraints as hard constraints within the optimization loop to guarantee forward invariance of the safety set under the linearized dynamics. The proposed framework achieves full-shape collision avoidance without sacrificing the solution space, while maintaining real-time performance for autonomous navigation on complex terrain. Full article

The rapid progress of sensing technologies, artificial intelligence, and embedded computing has significantly accelerated the development of autonomous vehicles. Among the core challenges of higher-level driving automation, reliable environmental perception remains one of the most critical. This review presents a systematic PRISMA-based analysis of multimodal sensor technologies and fusion architectures applied in autonomous driving, based on 66 peer-reviewed studies published between 2014 and 2025. The study examines the operational characteristics, advantages, and limitations of major sensing modalities, including cameras, LiDAR, radar, ultrasonic sensors, and GNSS/IMU-based localization systems. Particular attention is given to multimodal fusion strategies, covering early, mid-level, high-level, and transformer-based architectures that combine complementary sensor information to improve perception robustness and decision reliability. The review further synthesizes current evidence on performance under adverse environmental conditions, benchmark validation practices, real-time computational constraints, and the growing role of functional safety frameworks such as ISO 26262 and SOTIF. Emerging research directions, including 4D radar, self-supervised long-range fusion, foundation models, and cooperative V2X perception, are also discussed. The findings indicate that multimodal sensor fusion is a highly effective architectural strategy for improving scalability, fail-operational robustness, and certifiable safety in autonomous driving systems, particularly in higher-level automation scenarios. Future research should focus on uncertainty-aware fusion, explainable cross-modal reasoning, large-scale real-world validation, and efficient hardware–software co-design to support robust Level 4–5 vehicle autonomy. Full article

Collaborative driving, in which autonomous vehicles cooperate with other vehicles and roadside infrastructure to improve safety, perception, and traffic efficiency, is emerging as a key paradigm for next-generation transportation systems. While such collaboration enhances situational awareness, it also introduces new security vulnerabilities across perception, communication, planning, decision-making, and control layers. In this survey, we present a unified taxonomy of security threats and defense mechanisms in collaborative driving systems, systematically organizing attacks and countermeasures across system layers. We further examine the integration of language models, including vision-based and multimodal reasoning models, into collaborative driving pipelines, highlighting the resulting security risks and design challenges. Finally, we identify key open research challenges, including cross-layer and end-to-end security, uncertainty-aware defenses, and real-world validation, outlining promising directions for future work toward secure and resilient collaborative autonomous mobility. Full article