AI algorithm for predicting and optimizing trajectory of massive UAV swarm

Robotics and Autonomous Systems, Год журнала: 2025, Номер 186, С. 104910 - 104910

Опубликована: Янв. 6, 2025

Язык: Английский

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A novel reinforcement learning-based multi-operator differential evolution with cubic spline for the path planning problem

Artificial Intelligence Review, Год журнала: 2025, Номер 58(5)

Опубликована: Фев. 24, 2025

Язык: Английский

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Survey of Autonomous Vehicles’ Collision Avoidance Algorithms

Sensors, Год журнала: 2025, Номер 25(2), С. 395 - 395

Опубликована: Янв. 10, 2025

Since the field of autonomous vehicles is developing quickly, it becoming increasingly crucial for them to safely and effectively navigate their surroundings avoid collisions. The primary collision avoidance algorithms currently employed by self-driving cars are examined in this thorough survey. It looks into several methods, such as sensor-based methods precise obstacle identification, sophisticated path-planning that guarantee follow dependable safe paths, decision-making systems allow adaptable reactions a range driving situations. survey also emphasizes how Machine Learning can improve efficacy avoidance. Combined, these techniques necessary enhancing dependability safety systems, ultimately increasing public confidence game-changing technology.

Язык: Английский

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Trajectory planning and tracking control in autonomous driving system: Leveraging machine learning and advanced control algorithms

Engineering Science and Technology an International Journal, Год журнала: 2025, Номер 64, С. 101950 - 101950

Опубликована: Фев. 18, 2025

Язык: Английский

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Cooperative motion planning and control of a group of autonomous underwater vehicles using twin-delayed deep deterministic policy gradient

Applied Ocean Research, Год журнала: 2024, Номер 147, С. 103977 - 103977

Опубликована: Апрель 10, 2024

Язык: Английский

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Research on Autonomous Vehicle Path Planning Algorithm Based on Improved RRT* Algorithm and Artificial Potential Field Method

Sensors, Год журнала: 2024, Номер 24(12), С. 3899 - 3899

Опубликована: Июнь 16, 2024

For the RRT* algorithm, there are problems such as greater randomness, longer time consumption, more redundant nodes, and inability to perform local obstacle avoidance when encountering unknown obstacles in path planning process of autonomous vehicles. And artificial potential field method (APF) applied vehicles is prone optimality, unreachable targets, inapplicability global scenarios. A fusion algorithm combining improved proposed. First all, for concept probability sampling optimization strategy introduced, adaptive step size designed according road curvature. The post-processing planned carried out reduce nodes generated path, enhance purpose sampling, solve problem where oscillation may occur expanding near target point, randomness node improve efficiency generation. Secondly, method, by designing constraints, adding a boundary repulsion field, optimizing function safety ellipse, targets can be solved, unnecessary steering reduced, improved. In face U-shaped obstacles, virtual gravity points minimum passing performance obstacles. Finally, which combines designed. former first plans extracts temporary point latter, guides vehicle drive, avoids through encountered with then smooths making satisfy kinematic constraints. simulation results different scenes show that proposed this paper quickly plan smooth stable, accurate, suitable driving.

Язык: Английский

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A Survey on Recent Advancements in Autonomous Driving Using Deep Reinforcement Learning: Applications, Challenges, and Solutions

IEEE Transactions on Intelligent Transportation Systems, Год журнала: 2024, Номер 25(12), С. 19365 - 19398

Опубликована: Сен. 18, 2024

Язык: Английский

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STViT+: improving self-supervised multi-camera depth estimation with spatial-temporal context and adversarial geometry regularization

Applied Intelligence, Год журнала: 2025, Номер 55(5)

Опубликована: Янв. 16, 2025

Abstract Multi-camera depth estimation has gained significant attention in autonomous driving due to its importance perceiving complex environments. However, extending monocular self-supervised methods multi-camera setups introduces unique challenges that existing techniques often fail address. In this paper, we propose STViT+ , a novel Transformer-based framework for estimation. Our key contributions include: 1) the Spatial-Temporal Transformer (STTrans) which integrates local spatial connectivity and global context capture enriched spatial-temporal cross-view correlations, resulting more accurate 3D geometry reconstruction; 2) Photometric Consistency Correction (STPCC) strategy mitigates impact of varying illumination, ensuring brightness consistency across frames during photometric loss calculation; 3) Adversarial Geometry Regularization (AGR) module, employs Generative Networks impose constraints by using unpaired maps, enhancing performance under adverse conditions such as rain nighttime driving. Extensive evaluations on large-scale datasets, including Nuscenes DDAD, confirm sets new benchmark

Язык: Английский

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Enhancing Roundabout Efficiency Through Autonomous Vehicle Coordination

Vehicles, Год журнала: 2025, Номер 7(1), С. 9 - 9

Опубликована: Янв. 24, 2025

The paper discusses the potential for autonomous vehicles to improve traffic flow on roundabouts, suggesting that their ability slow down strategically can enhance and reduce pollution both main yielding roads. A simulator a roundabout was developed busy intersection of new city neighborhood. We consider some cars are self-driving, they fully aware scenario. By optimizing speed timing reduction, these help maintain balance between number time crossing This study evaluates effectiveness intervention, demonstrating significantly efficiency, reducing congestion pollution. application genetic algorithms is highlighted as an effective optimization method find right vehicle’s reduction ratio combination road efficiency.

Язык: Английский

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Exploring the Unseen: A Survey of Multi-Sensor Fusion and the Role of Explainable AI (XAI) in Autonomous Vehicles

Sensors, Год журнала: 2025, Номер 25(3), С. 856 - 856

Опубликована: Янв. 31, 2025

Autonomous vehicles (AVs) rely heavily on multi-sensor fusion to perceive their environment and make critical, real-time decisions by integrating data from various sensors such as radar, cameras, Lidar, GPS. However, the complexity of these systems often leads a lack transparency, posing challenges in terms safety, accountability, public trust. This review investigates intersection explainable artificial intelligence (XAI), aiming address implementing accurate interpretable AV systems. We systematically cutting-edge techniques, along with explainability approaches, context While technologies have achieved significant advancement improving perception, transparency autonomous decision-making remains primary challenge. Our findings underscore necessity balanced approach XAI driving applications, acknowledging trade-offs between performance explainability. The key identified span range technical, social, ethical, regulatory aspects. conclude underscoring importance developing techniques that ensure explainability, specifically high-stakes stakeholders without compromising safety accuracy, well outlining future research directions aimed at bridging gap high-performance trustworthy

Язык: Английский

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