Search Results (197)

Autonomous vehicles depend on heterogeneous sensing systems whose performance varies with range, illumination, weather, object material, traffic geometry, contamination, calibration quality, and cyber-physical interference. This PRISMA 2020 and PRISMA-S systematic review synthesized 65 peer-reviewed primary studies selected from 2143 records identified through four databases. After removal of 793 records before screening, 1350 titles and abstracts were screened; 273 full texts were assessed and 208 were excluded with documented reasons. The final evidence base covers cameras, LiDAR, radar, thermal and event cameras, GNSS/IMU localization, calibration, synchronization, multimodal fusion, adverse-weather perception, sensor-health monitoring, and fault-tolerant perception. No modality was universally superior: comparative performance depended on hardware generation, dataset, environmental severity, range, and metric. Direct evidence was strongest for component-level perception and controlled degradation, whereas health-conditioned fusion, ODD restriction, and minimum-risk behavior were supported mainly by partial experimental evidence and safety-oriented synthesis. The review therefore proposes, rather than claims to validate, a reliability-aware architecture that separates sensor health from task confidence, preserves uncertainty and provenance, adapts fusion, and constrains operation when residual evidence is insufficient. The review was retrospectively registered in PROSPERO on 30 July 2026 (CRD420261465869). Full article

Autonomous field robots are increasingly used in precision agriculture for monitoring, phenotyping, spraying, and site-specific intervention, yet reliable autonomy remains difficult under vegetation occlusion, uneven terrain, variable illumination, and intermittent communications. This review provides a deployment-oriented synthesis of navigation and sensor-fusion methods for agricultural robots, with emphasis on what is practical under field conditions rather than only in laboratory settings. The literature was examined through a structured narrative-review workflow using Scopus, Web of Science, IEEE Xplore, ScienceDirect, SpringerLink, and related citation tracking, with primary emphasis on studies published between 2015 and 2025. We compare global, local, and hybrid planning methods; motion-control strategies such as PID, Pure Pursuit, and MPC; and localization pipelines that combine GNSS, IMU, LiDAR, cameras, odometry, and SLAM or Kalman-family fusion. Beyond algorithm summaries, the review links method selection to agricultural deployment constraints, including GNSS degradation, dynamic obstacles, compute limits, ROS 2 integration, time synchronization, and coordinate-frame management. The synthesis shows that no single stack is optimal across all crop systems: lightweight GNSS/IMU-based solutions remain attractive in structured open fields, whereas orchards, vineyards, and other occluded environments benefit more from tighter multi-sensor fusion and SLAM-supported localization. Finally, the review distills design guidance for sensing, planning, validation, and digital-twin-supported testing, and identifies research gaps related to robustness, benchmarking, safety, and scalable on-farm deployment. Full article

A UAV operating without GNSS faces unbounded inertial drift. A layered navigation architecture is evaluated in which terrain contour matching (TERCOM) provides periodic position corrections and satellite-image scene matching adds a condition-dependent precision layer. The architecture is examined through a single-trajectory simulation over a 1° × 1° ASTER GDEM V2 tile (N31E081, Tibetan Plateau, 4555–6468 m elevation, 16.1 mean slope) representing a one-hour flight (127 km, 35.2 m/s). The simulation models GNSS loss with idealised sensor behaviour: IMU error is described by a Gauss–Markov model without temperature dependence, and the radar altimeter is represented with additive Gaussian noise. Under these conditions, TERCOM reduced RMS position error from 1467 m to 317 m (78.4% reduction); with ideal noise-free scene-matching registration added, RMS further decreased to 103 m (a best-case estimate). The idealised Cramér–Rao lower bound already incorporates the 5 m radar-altimeter and 20 m DEM noise terms (it is therefore not a noise-free value) at the flight mean slope of 16.1°; averaging this local bound over the full trajectory—where near-flat segments inflate it—gives the tile-averaged CRLB of ≈150 m. The remaining gap between the realised TERCOM RMS (317 m) and this realistic bound is attributed to residual INS drift during profile collection, DEM interpolation error, and low-entropy terrain segments; a quantitative decomposition of these factors is provided in this paper. Results are based on a single noise realisation and a single trajectory; they characterise the specific simulation scenario rather than the architecture’s general performance. The altitude-error decomposition argument—that TERCOM’s sensitivity depends primarily on short-term dynamic altitude drift rather than the accumulated systematic error—is developed specifically for the normalised cross-correlation (NCC) metric and requires mean-centring of the terrain profile for generalisation to other correlation metrics. Full article

Autonomous flight planners consume sensor-derived estimated states rather than simulator ground truth. Learned dynamics surrogates are queried over a planning horizon through architecture-native multi-step interfaces, so strong short-horizon accuracy can still yield unreliable long-horizon trajectory scores. We address this with a JSBSim sensor-interface evaluation protocol that assesses surrogate predictions from onboard sensing and lightweight estimation—inertial measurement unit (IMU), global navigation satellite system (GNSS)/air-data, barometric, and vertical-speed channels with delay, dropout, asynchronous refresh, and estimator filtering—and guides surrogate selection by a planning task. This paper contributes an evaluation-and-selection methodology for learned flight-dynamics surrogates used by autonomous planners; it is not a new flight-dynamics, guidance, or control method, and it does not propose a new neural architecture. The contribution is a transparent and auditable sensor-interface and estimator-conditioned protocol, instantiated on five surrogate families over a 20 s prediction horizon. We emphasize at the outset that under strict native-unit physical tolerances, all evaluated surrogates diverge on 90–100% of validation windows and none is field-ready; all results are comparative JSBSim stress-test evidence, not field-readiness evidence. Results reveal criterion-dependent ordering: long short-term memory (LSTM) networks are strongest on root-mean-square error (RMSE@1s/RMSE@20s), shared-threshold failure, and absolute fidelity, whereas the Transformer is favored under self-scaled divergence and capped-risk criteria. These aggregate scores combine heterogeneous simulator-coordinate units and are benchmark diagnostics rather than physical safety margins. Because different threshold families select different winners, our central conclusion is not that one surrogate is best, but that the choice of metric and threshold family is itself part of the benchmark assessment. In separately generated JSBSim candidate-screening episodes, Transformer has approximately 8% lower mean cost under abstract sensing corruption, whereas estimator-loop screening shifts toward LSTM; feedback-rich tracking remains exploratory at 40 episodes per scenario and has mixed cost and success endpoints. Cross-aircraft transfer degrades substantially. Surrogates should be assessed using sensing-aware, estimator-conditioned, task-specific long-horizon criteria, not short-horizon accuracy alone. Full article

Accurate and real-time localization for unmanned vehicles in complex motion environments is challenged by asynchronous multi-sensor measurements, time-varying measurement quality, and the growing computational burden of long-term factor graph optimization. To address these problems, this study proposes AVFGO, a sliding-window incremental factor graph optimization framework for motion-state-aware adaptive multi-sensor fusion localization. IMU pre-integration is used as the primary state-propagation backbone, while asynchronous LiDAR odometry, AHRS, wheel odometry, and barometer measurements are uniformly represented as factor-graph constraints. A sliding-window marginalization mechanism bounds the optimization scale by retaining historical information as prior factors. A motion-state-aware fusion-rate strategy controls the insertion density of external factors, and a residual-driven vector-wise adaptive weighting model adjusts the covariance of heterogeneous sensors in different measurement dimensions. Field experiments on a GNSS-denied wheeled unmanned vehicle dataset show that AVFGO achieves a 3D position RMSE of 0.380 m, reducing the error by 65.36% relative to IFGO and 52.71% relative to SWIFGO. The mean single-step optimization time is 0.0389 s, corresponding to an 11.9× speedup over IFGO and a 75.81% reduction relative to SWIFGO. These results indicate that the proposed framework improves accuracy and real-time performance while remaining limited by the single-platform field-test scope, which is explicitly discussed as a direction for future validation. Full article

Autonomous driving systems rely on a sophisticated pipeline of artificial intelligence models to perceive, predict, and plan in dynamic environments. This review presents a systematic analysis of the machine learning and deep learning models underpinning vehicle autonomy, spanning classical convolutional neural networks (CNNs) for object detection and semantic segmentation to recurrent and Transformer-based architectures for trajectory prediction and motion planning. It also provides a critical examination of the autonomous vehicle sensor stack, including cameras, LiDAR, radar, ultrasonics, and GNSS/IMU as data acquisition systems, highlighting modality-specific AI challenges such as monocular depth estimation, 3D point cloud processing, and radar Doppler interpretation. The evolution of perception and decision-making pipelines is reviewed, contrasting modular architectures with end-to-end learning paradigms that directly map raw sensor data to control commands, and discussing their trade-offs in interpretability, safety assurance, and robustness to rare edge cases. We further survey specialized hardware accelerators and heterogeneous automotive SoCs designed to meet stringent real-time and power constraints. Industrial strategies are compared, including multi-modal sensor fusion and vision-centric approaches based on large-scale imitation learning. Finally, we identify open challenges related to robustness under adverse conditions, domain shift, causal ambiguity, and the need for interpretable and certifiable AI in safety-critical autonomous driving systems. Full article

Reliable outdoor localization is essential for autonomous mobile robots, where the Global Navigation Satellite System (GNSS) is widely used to provide global positioning information. However, GNSS signals are often degraded in real-world environments due to occlusions, multipath effects, and environmental interference, leading to unstable localization and degraded navigation performance. This paper proposes an adaptive multi-sensor fusion framework for robust outdoor localization and path tracking under weak GNSS conditions. The proposed system integrates GNSS, LiDAR, wheel odometry, and inertial measurement unit (IMU) measurements within an Extended Kalman Filter (EKF) framework. To address the limitations of GNSS, an adaptive weighting mechanism is introduced to dynamically adjust the influence of GNSS observations based on signal quality indicators. Furthermore, a GNSS quality-aware mode-switching strategy is developed, enabling seamless transition between GNSS-dominant localization and multi-sensor fusion-based localization. In the fusion mode, LiDAR, odometry, and IMU jointly provide robust pose estimation, while GNSS acts as a weak global constraint. The IMU further enhances heading estimation, improving orientation stability and path tracking performance. The estimated pose is then used for trajectory tracking using a path-following controller. Experimental results conducted in outdoor environments demonstrate that the proposed framework significantly improves localization robustness and path tracking performance under degraded GNSS conditions. Compared with raw GNSS localization, the proposed method reduces the mean localization error by 47.2% and decreases the root mean square localization error by 55.5%, while maintaining smoother and more continuous trajectory estimation in weak GNSS environments. Full article

Autonomous driving systems (ADSs) are reliable only when heterogeneous sensors, estimation algorithms, and safety mechanisms are engineered as a single coherent safety-critical measurement system rather than as loosely coupled modules. Production stacks integrate cameras, LiDAR, automotive radar, and GNSS/IMU, yet deployment remains constrained by modality-specific failure modes, calibration and synchronization drift, and out-of-distribution (OOD) conditions that violate modeling assumptions. These limitations induce overconfidence and downstream decision errors whenever planning assumes certainty sharper than sensing can justify. This survey introduces a sensor-centric framework linking measurement physics, uncertainty propagation, fusion integrity, safety assurance, and risk-aware planning and control. We formalize what each modality physically measures; unify probabilistic, evidential, and conformal uncertainty representations; analyze filtering, factor-graph, BEV, transformer, and state-space fusion architectures with an emphasis on robustness and graceful degradation; and generalize aviation-style integrity concepts (RAIM/ARAIM) to multi-modal autonomy. The distinctive contribution is a single sensor-to-assurance throughline in which every uncertainty representation is tied to its measurement physics, every fusion architecture is evaluated against an explicit integrity-monitoring requirement generalized from RAIM/ARAIM, and every safety-standard clause is mapped to a concrete architectural mechanism. We map these mechanisms onto ISO 26262, ISO 21448 (SOTIF), ISO/PAS 8800, ANSI/UL 4600, and the UNECE framework, and connect perception uncertainty to decision-making through chance-constrained MPC and formal safety filters (RSS, CBF). Industry case studies and emerging V2X and generative-simulation approaches close the loop to deployable safety arguments. Full article

Multi-sensor fusion of GNSS, IMU, and vision sensors has been extensively applied in urban Internet of Things systems and automated driving to improve positioning accuracy in complex environments. However, conventional FGO algorithms are based on fixed sensor weights, which limit their adaptability to fluctuations in sensor errors caused by environmental changes, thereby compromising positioning performance. To overcome this limitation, a novel multi-sensor adaptive weighted localization algorithm based on joint residuals detection was proposed in this study. The algorithm computes joint residuals by the sliding window accumulation of GNSS, IMU, and vision sensor measurements. By integrating a global weight decay factor into the M-estimation framework, the weights of each sensor were dynamically adjusted, thereby suppressing the effects of outliers on the state estimation. This approach enables high-precision and robust estimation of position, velocity, and attitude. Experimental results demonstrate that, based on validation with the GNSS–Visual–Inertial Navigation System (GVINS) public datasets sports field and complex environments, the proposed method exhibits superior performance in challenging low-altitude economic scenarios such as weak GNSS signals and significant IMU drift—specifically, it improves positioning accuracy by 32.3% and reduces velocity error by 32% compared to traditional FGO algorithms. In scenarios with GNSS signal interference, the system effectively mitigates error accumulation and maintains the stability of position and velocity estimation. The proposed algorithm demonstrates exceptional positioning accuracy and robustness in complex and dynamic environments, making it highly suitable for advanced urban IoT and automated driving applications. Full article

Real-time telemetry is essential for performance optimization and safety in motorcycle racing, yet commercial solutions remain proprietary, expensive, and poorly extensible. This paper presents the design, implementation, and experimental evaluation of an open-architecture embedded telemetry unit built around the STM32H745 dual-core microcontroller. The system integrates a u-blox ZED-F9P RTK-GNSS receiver, a Bosch BNO085 9-DoF IMU with on-chip sensor fusion, a CAN-FD interface for powertrain data acquisition, and a SIM7600E-H 4G/LTE module for real-time remote streaming, all housed in a 3D-printed vibration-resistant enclosure. The firmware employs deterministic dual-core task partitioning: the Cortex-M7 core handles sensor fusion and CAN-FD at high frequency, while the Cortex-M4 core manages 4G communication and microSD logging. We explicitly delimit the scope of the evidence presented: CAN-FD powertrain acquisition and end-to-end operational reliability are experimentally validated on real circuit data spanning four campaigns, over 100 laps, and 5.8 h of logging—with sustained acquisition of 13 powertrain channels at speeds up to 185 km/h and zero system resets or data-integrity errors. In contrast, RTK positioning accuracy (2.5 cm CEP), sensor-fusion latency (sub-2 ms at the 99th percentile), 4G-uplink reliability, and thermal margins are characterized through manufacturer specifications, Monte Carlo simulation, and analytical models, with a fully instrumented end-to-end measurement campaign identified as the immediate next step. The 50 Hz effective positioning rate combines 25 Hz GNSS with IMU interpolation. With a bill of materials of approximately EUR 265, the platform offers an order-of-magnitude cost reduction over commercial alternatives while providing full openness and extensibility for distributed intelligence applications. Full article

Achieving long-term, continuous, and accurate localization for Unmanned Aerial Vehicles (UAVs) in outdoor GNSS-denied environments where pre-existing reference maps are available is challenging. To this end, this paper proposes a Cross-view Particle Filter Localization (CPFL) framework. Unlike existing particle filter approaches that rely on inertial sensors for state propagation or sparse semantic labels for observation updates, CPFL is a vision-driven solution. This framework introduces specific adaptations into the two core stages of particle filtering: In the motion propagation stage, it achieves visual state transition by calculating a feature-based inter-frame homography mapping to estimate the 2D global relative motion components, eliminating the dependency on inertial priors; in the observation correction stage, a Dual-Granularity Adaptive Gating (DGAG) cross-view network is designed to mitigate perceptual aliasing and generate discriminative absolute position weights for the particles. By fusing these two stages through a filter mechanism, the framework transforms unbounded cumulative drift into bounded absolute localization errors. Furthermore, addressing the measurement deficiencies of traditional single-frame metrics, this paper also proposes a Trajectory Continuity Index (TCI@d) tailored for continuous localization tasks. Experiments on the real-world MAFS dataset confirm that this framework achieves a mean localization error of 5.28 m and a localization success rate of 89.7% under a 10-m threshold. Compared with mainstream vision-only algorithms and IMU-fusion baselines, this framework demonstrates lower mean errors and improved trajectory continuity, validating its effectiveness for long-term robustness. 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

Indoor localization for unmanned aerial vehicles (UAVs) remains challenging in GNSS-denied environments due to the difficulty of position calibration of multiple ultra-wideband (UWB) anchors and the asynchronous fusion of heterogeneous sensors. This paper proposes a multi-sensor fusion localization framework that integrates multi-anchor one-shot calibration with factor graph optimization (FGO). First, Landmark Multidimensional Scaling (LMDS) is used to reconstruct the relative geometry of the anchors and the onboard tag from ranging measurements. Then, rigid Procrustes alignment is performed using a small number of anchors with known coordinates in the East–North–Up (ENU) frame to recover the transformation to the ENU frame, thereby enabling efficient position calibration of multiple UWB anchors and UAV pose initialization. Subsequently, a tightly coupled factor graph is constructed by incorporating inertial measurement unit (IMU) pre-integration, UWB ranging, laser rangefinder height measurements, and visual–inertial odometry (VIO) pose constraints. The resulting nonlinear optimization problem is solved using incremental smoothing, which improves robustness against non-line-of-sight (NLOS) errors and long-term drift. Experimental results on anchor calibration, public datasets, and real-world indoor UAV flights demonstrate that the proposed method improves the accuracy and robustness of indoor UAV localization. In particular, on the real-world rectangle trajectory, FGO-TC reduces the RMSE by approximately 38.8% compared with FGO-LC. Full article

The rising market penetration of new energy vehicles (NEVs) is transforming urban traffic into a heterogeneous mix of battery electric (BEVs), hybrid electric (HEVs), and conventional fuel vehicles (FVs). For analytical brevity, traditional internal combustion engine vehicles (ICEVs) are hereafter referred to as ‘fuel vehicles (FVs)’ in the discussion of New Energy Vehicle (NEV) networks. This research investigates the efficacy of centralized coordination for NEVs within a localized region, as opposed to individualized speed control, in enhancing the mitigation of traffic congestion. Evaluating traffic efficiency and decarbonization strategies in such settings often requires extensive random sampling and Monte Carlo simulations over a large set of parameter combinations. However, conventional microscopic traffic simulators, which rely on fine-grained modeling of vehicle dynamics and signal control, incur prohibitive computational time when scaled to large networks and numerous experimental scenarios. In this study, battery electric vehicles and hybrid electric vehicles are designed as density-aware vehicles, whose movement speed is adaptively adjusted according to the regional traffic density in their vicinity and the control parameter β. In contrast, fuel vehicles adopt a stochastic movement speed and, together with other vehicle types, exhibit either movement or stoppage in the lattice environment. This density-driven speed-adaptive control and lattice arbitration mechanism is intended to reproduce, in a simplified yet extensible manner, changes in mobility and traffic-flow stability under high-density traffic conditions. The simulation results indicate that, under the same Manhattan road network and vehicle-density conditions, tuning the β parameter of new energy vehicles to reduce their movement speed in high-density areas and to mitigate abrupt position changes can suppress traffic-flow oscillations, delay the onset of the congestion phase transition, and promote spatial equilibrium of traffic flow. Meanwhile, this study develops simplified energy-consumption and carbon emission models for battery electric vehicles, hybrid electric vehicles, and fuel vehicles, demonstrating that incorporating a speed-adaptive density strategy into mixed traffic flow not only helps alleviate abnormal congestion but also reduces potential energy use and carbon emissions caused by congestion and stop-and-go behavior. From a sensing and practical perspective, the proposed framework assumes that future connected and autonomous vehicles (CAVs) can estimate vehicle states and local traffic density through GNSS–IMU multi-sensor fusion and V2X communications, indicating methodological consistency between the proposed model and real-world CAV sensing capabilities and making it a suitable and effective experimental platform for investigating the relationships among new energy vehicle penetration, density-control strategies, and carbon footprint. Full article

In Chuckwagon racing, teams of four Thoroughbred horses pull wagons at high speeds. Movement symmetry is a key locomotion metric linked to force production, racing direction, and lameness. Racehorse symmetry in trot during on-track warmups and cooldowns was assessed. Over 10 days, 60 horses (average 8 per day) were fitted with Global Navigation Satellite Systems combined with Inertial Measurement Unit (GNSS-IMU) sensors. Weight-bearing asymmetry was quantified using the minimum difference (MnD) in vertical trunk displacement between diagonal limb pairs, and push-off asymmetry was quantified using the upwards difference (UpD). Absolute (mm) and normalized (% ROM) asymmetries were compared between warmups and cooldowns using linear mixed models. Mean MnD was similar between warmup (6.2 mm; 17.6%) and cooldown (6.4 mm, 19.7%). Mean UpD increased from warmup (11.3 mm, 31.7%) to cooldown (12.8 mm, 38.0%), with UpD% significantly higher in cooldown (p = 0.046). No other differences were significant (all p ≥ 0.202). One horse sustained a catastrophic musculoskeletal (MSK) injury. This horse’s UpD ranged from 3.3–29.7 mm (11.4–69.3%) during warmups and 24.3–25.5 mm (47.8–76.4%) during cooldowns. Push-off asymmetry may increase after Chuckwagon racing. The injured horse showed high asymmetries, but high values also occurred in uninjured horses. Further work needs to establish normal asymmetry ranges in Chuckwagon racing and identify patterns associated with MSK injuries. Full article