Unmanned aerial vehicles (UAVs) exhibit considerable potential in post-disaster search and rescue, environmental monitoring, and other related applications. However, achieving collaborative path planning to cover irregular regions remains a key challenge. To address this issue, this study designs a comprehensive pipeline to generate paths for UAVs to cover a nonconvex region with nonconvex obstacles, thereby minimizing not only path length but also the number of turns in the sky. The pipeline comprises three main modules, i.e., region division, task allocation, and path generation. In the first module, a region division module upon polygon decomposition is proposed for scenarios including nonconvex regions with nonconvex obstacles. The task allocation module is modeled as a graph node grouping problem, which is then solved by an iterative algorithm designed upon the graph structure to balance computing load and path cost. In the last module, different from traditional vertical scanning, a direction-optimized scanning strategy is proposed, which solves for the direction that minimizes path length and number of turns first, and then scans the region. Simulations and experiments using 3 UAVs are carried out. Results show the effectiveness of the proposed algorithm in various irregular regions and its applicability to real platforms.
Adverse weather poses a critical challenge to autonomous vehicles due to asymmetric sensor degradation: rain and fog severely degrade cameras, snow attenuates LiDAR by causing severe backscattering, while radar remains robust. This asymmetry breaks conventional fusion architectures that assume consistent sensor reliability—the "static fusion paradox." Existing surveys focus on clear-weather scenarios, leaving gaps in understanding how architectures handle dynamically degrading sensors. This survey provides the first comprehensive analysis of deep learning-based multimodal fusion for asymmetric degradation under adverse weather. We systematically reviewed 30 highly relevant multimodal fusion papers from the broader autonomous vehicle literature (2016–2025), presenting: (1) a degradation-centric fusion taxonomy categorizing architectures by how they handle unreliable sensors, revealing 70% (21/30) use static weighting despite dynamic conditions; (2) a unified restoration-fusion framework analyzing 8 integrated approaches; (3) critical benchmark evaluation of 12 datasets showing only 3 provide severe asymmetric conditions; and (4) a prioritized research roadmap toward weather-agnostic foundation models. Our synthesis establishes that weather-robust perception requires co-evolution of restoration and fusion, not separate optimization, providing both a definitive reference and strategic guidance for next-generation autonomous perception systems.
The security of Unmanned Aerial Vehicles (UAVs) is compromised by the UAVCAN protocol’s inherent vulnerabilities and the computational constraints that preclude resource-intensive Machine Learning (ML) and Deep Learning (DL) detection solutions. This paper proposes a novel rule-based Intrusion Detection System (IDS), specifically engineered for the UAVCAN bus, which we designate LUMI (Lightweight UAVCAN Message Window-Based IDS). We first construct a new, robust UAVCAN dataset featuring sophisticated, watchdog-mimicking attack scenarios. We then demonstrate the dataset’s complexity by applying adapted ML/DL models from Controller Area Network (CAN) literature, which show significant performance degradation. LUMI addresses this challenge by enforcing sequential Transfer ID (TID) continuity within a sliding message window. Experimental results demonstrate that LUMI achieves a near-perfect F1-score of 0.9999—matching the performance of the most complex models—while maintaining linear time complexity and delivering a 16ms detection latency. This resolves the critical performance-complexity trade-off, validating LUMI as a practical and effective solution for real-time, resource-constrained UAVCAN systems.
Automated vehicles need to drive at least as well as humans using sensors, data, and machine intelligence. Therefore, this paper investigates data quality and its impact on upstream machine learning. Clearly, poor data increases the risk of hazardous situations and requires additional cycles in the drive-gather-process-assess process, increasing costs and probably delays. A complicating factor for data quality is adverse weather, prevalent in Nordic conditions.This work introduces a novel measure for quantifying the data quality. Akin to Technical Readiness Levels, Data Readiness Levels have been developed within this work to quantify data quality. The idea is a simple overall measure of the dataset, ranging from 1-9. As well as a framework, DRLs normalise values and metrics from various assessment tools.To evaluate the concept, three autonomous driving datasets were used, augmented with additional weather conditions not present in some scenes. Two of the datasets are from open-road driving scenes, and one from a test track. Images were further distorted with rain and fog, and point clouds with fog. All source code is available and, one data set is, with the other two part of a larger release.We show that a simple, single metric can be derived from 46 image and 2 point cloud quality metrics tested across nearly 5 TB of data. Through considerable statistical rankings, two image quality metrics performed well, at some computational cost, whilst state of the art point cloud metrics were inconclusive across our data.
Narrow-space parallel parking is one of the most challenging tasks in autonomous driving, attributed to limited maneuvering space and strict kinematic constraints. Conventional rule-based planners (e.g., Reeds–Shepp (RS) curves) can generate feasible trajectories but suffer from high computational costs and limited adaptability in narrow environments. Recent reinforcement learning (RL) approaches offer enhanced flexibility, yet their performance degrades under severe spatial constraints and nonholonomic vehicle dynamics. To address these issues, this work proposes a learnable RS-integrated reinforcement learning framework. First, a learnable RS cost field based on attention networks is developed to encode kinematic feasibility and provide soft heuristic guidance for reward design. Then, by fusing the RS-guided reward with a direct parking reward under a dual-critic architecture, the framework effectively integrates rule-based priors with the adaptability of RL. Experimental results show that the proposed learnable RS field accurately generates RS cost distributions with a 161× computational speedup. Compared with baselines, the RS-RL framework improves the success rate by 8.7% and reduces the travel distance by 20.3%. Particularly for narrow parallel parking spaces with a length of only 1.3 times the vehicle length (6.5m), our method attains a 97.8% parking success rate with adequate safety margins, demonstrating superior safety performance and adaptability to narrow parking scenarios.
