The integration of Artificial Intelligence (AI) tools and techniques has provided a significant advance in drone technology. Besides the military applications, drones are being increasingly used for logistics and cargo transportation, agriculture, construction, security and surveillance, exploration, and mobile wireless communication. The synergy between drones and AI has led to notable progress in the autonomy of drones, which have become capable of completing complex missions without direct human supervision. This study of the state of the art examines the impact of AI on improving drone autonomous behavior, covering from automation to complex real-time decision making. The paper provides detailed examples of the latest developments and applications. Ethical and regulatory challenges are also considered for the future evolution of this field of research, because drones with AI have the potential to greatly change our socioeconomic landscape.

This study introduces an enhanced Predictive Dynamic Window Approach (P-DWA), developed as an offline trajectory planner for simulation-based analysis. The algorithm predicts nine candidate trajectories per iteration, evaluates their temporal and kinematic feasibility, and selects the top three based on energy efficiency. Results show an average reduction of approximately 9% in energy consumption compared to the traditional P-DWA, while maintaining efficient computational performance with average iteration times ranging from 15.6 ms to 18.5 ms. However, this gain in energy efficiency typically requires more iterations to complete a path, reflecting the algorithm’s more conservative motion strategy. The trade-off between energy savings and total simulation time underscores the value of this approach for testing sustainable navigation strategies. Overall, the proposed P-DWA provides a valuable tool for offline trajectory generation in autonomous mobile robotics, supporting energy-aware path planning under controlled simulation environments.

Obstacle avoidance is a critical issue in autonomous agent spatial navigation, moreover in the case of team coordinated navigation with moving obstacles. Algorithms for obstacle avoidance by unconstrained teams of agents, i.e. agents that have no relative position restrictions, such as the Reciprocal Velocity Obstacle (RVO), and the Hybrid Reciprocal Velocity Obstacle (HRVO) have been proposed in the literature. However, in some tasks the agents must comply with relative spatial restrictions, i.e. agents must maintain specific distances between them within some tolerance thresholds. These tasks include robotic team coordination, human-robot collaboration and autonomous vehicle platooning. This paper presents a novel Extended Hybrid Reciprocal Velocity Obstacle (EHRVO) algorithm for multi-agent collision avoidance that incorporates linked agent constraints. The proposed approach introduces proximity constraints between paired agents while adapting the collision avoidance geometry to maintain these relationships. This research work extends the HRVO framework to create a hierarchical set of constraints that prioritize maintenance of spatial consistent agent pairs or triads while ensuring collision-free trajectories. Prior to actual physical implementation, the EHRVO navigation algorithm is validated through exhaustive simulations involving multiple paired agents and triads navigating in cluttered spaces with many moving obstacles, namely the agents in the other teams. Results demonstrate that this novel approach successfully maintains the spatial relative positions of linked group agents while avoiding collisions with other agents, showing significant improvements over traditional RVO and HRVO algorithms in scenarios requiring coordinated movement.

Traffic accidents significantly impact societies and economies. The risk of collision is highest in urban areas, leading to devastating loss of life and escalating socioeconomic costs. In this context, numerous studies have focused on accurately predicting accident risk, severity, and duration using various methodologies. This paper presents an overview of traditional statistical models for accident prediction and a comprehensive systematic review of the literature on statistical modeling, machine learning (ML), and deep learning (DL) techniques employed in this field. Different methodologies and techniques are compared by categorizing studies that adopt similar approaches and analyzing them comparatively. Furthermore, a distinction is made between temporal and spatiotemporal models to describe how these approaches influence the accuracy of future predictions regarding accident occurrence and the duration of impact. This review distinguishes itself from similar works by not only comparing models and approaches, but also by analyzing how external features, such as meteorological data, road geometric design, and land usage, affect the probability of accidents and the models’ accuracy in forecasting road safety. The study explores the performance levels and limitations associated with a set of forecasting approaches, offering an analytical discussion of their differences and similarities, and potential future developments in this research space, including the use of hybrid models and reinforcement learning (RL). The results of this review indicate that DL models tend to be better suited to complex forecasting problems due to their superior ability to represent features and extract non-linear spatiotemporal correlations. This article concludes by describing various directions for further research, ranging from optimizing model architectures to integrating real-time big data into proactive prediction systems.

En esta investigación, introducimos un nuevo Enfoque de Ventana Dinámica Predictiva (P-DWA), donde el algoritmo no solo anticipa la trayectoria óptima en términos de tiempo, sino que también evalúa el consumo energético del movimiento del robot móvil. El P-DWA predice nueve posibles destinos, evalúa su rendimiento temporal y elige las tres mejores trayectorias. Mediante el modelado de los motores del robot móvil, se logra estimar el consumo energético y el par requerido para un mapa 2D y de las trayectorias predichas se determina el consumo de cada una de ellas en vatios-hora (W/h), para optar por aquellas que menor consumo requieran. Los resultados muestran que, mediante la consideración energética, es posible llegar a reducir el 9% del consumo energético comparación con el enfoque de Ventana Dinámica convencional.