Bird populations are important bioindicators, as the diversity of their habitats and their outstanding mobility make them sensitive to ecosystem changes. They also contribute to ecosystems by providing valuable services such as pest control, seed dispersal, and pollination, which are vital for maintaining ecological balance. Hence monitoring the composition of bird populations is essential for assessing ecosystem health and guiding conservation efforts. However, traditional observer-based surveys are expensive, time-consuming, and impractical for large-scale and hardly accessible applications. Our study proposes an approach by integrating automated acoustic monitoring with machine learning applications to develop a system that achieves high identification accuracy while keeping the pipeline simple. Our model design emphasizes a balance between efficiency and scalability, enabling deployment in resource-constrained environments. We provide a tool for low-complexity ecological monitoring by prioritizing lightweight and simple architectures. This work bridges the gap between ecological research and practical, scalable conservation technologies.

This paper presents an approach for global trajectory planning using quadratic optimization and dynamic programming. In the global route planning phase, the goal is to find a route with minimal curvature for any road geometry, while a speed profile is determined by dynamic programming based on the vehicle’s dynamic constraints. The method has a low computational cost and is well-suited for integration within a hierarchical system architecture, where the trajectory can be further refined with local route planning to enable the vehicle to adapt to changing environmental conditions. The developed method ensures vehicle stability while achieving the highest possible speed. The approach is implemented in the Frenet coordinate system within a self-developed simulation environment that allows for a comprehensive evaluation of vehicle dynamics, trajectory feasibility, and performance metrics. The results show that the proposed method can generate smooth and dynamically feasible trajectories while balancing curvature minimization, safety, and computational efficiency, making it well-suited for real-world applications.

Reduced capacity on motorways can easily lead to significant congestion. This congestion is a major contributor to environmental pollution, harming the livability of the peri-urban environment and public health. In this study, we have addressed the congestion caused by lane closures on motorways, one of the many difficulties encountered in the lane closure problem. To overcome this problem, the so-called variable speed limit control, a traffic management system is a helpful tool that improves overall traffic flow characteristics - travel time, waiting time, and queue length - and reduces critical sustainability indicators such as fuel consumption and CO2and NOxemissions. Deep Learning has repeatedly been shown to be an excellent solution to this problem. Hence, this study aims to use Reinforcement Learning to address the traffic management system and to find a general solution to congestion caused by the reduction of highway capacity to apply the model regardless of the number of lanes, improving and surpassing the results achieved in the literature in several aspects.

Performing evasive maneuvers with highly automated vehicles is a challenging task. The algorithm must fulfill safety constraints and complete the task while keeping the car in a controllable state. Furthermore, considering all aspects of vehicle dynamics, the path generation problem is numerically complex. Hence its classical solutions can hardly meet real-time requirements. On the other hand, single reinforcement learning based approaches only could handle this problem as a simple driving task and would not provide feasibility information on the whole task’s horizon. Therefore, this paper presents a hierarchical method for obstacle avoidance of an automated vehicle to overcome this issue, where the geometric path generation is provided by a single-step continuous Reinforcement Learning agent, while a model-predictive controller deals with lateral control to perform a double lane change maneuver. As the agent plays the optimization role in this architecture, it is trained in various scenarios to provide the necessary parameters for a geometric path generator in a one-step neural network output. During the training, the controller that follows the track evaluates the feasibility of the generated path whose performance metrics provide feedback to the agent so it can further improve its performance. The framework can train an agent for a given problem with various parameters. As a use case, it is presented as a static obstacle avoidance maneuver. the proposed framework was tested on an automotive proving ground with the geometric constraints of the ISO-3888-2 test. The results proved its real-time capability and performance compared to human drivers’ abilities.

This paper focuses on the design of lateral controllers for autonomous vehicles. To enhance passenger comfort while concurrently maintaining minimal deviation from the desired trajectory, the developed controllers are tuned by a Genetic Algorithm, whose cost function is following the ISO 2631 Standard. Three model-based controllers, a Linear Quadratic Regulator, a Linear Quadratic Servo algorithm, and a Model Predictive Controller have been compared in a simulation environment. The test case consists of a suburban road section, where the vehicles must successfully traverse at different velocities while minimizing the lateral acceleration and jerk affecting the passengers. To take into account the velocity-dependent dynamics of the system, the controllers are based on a Linear Parameter-Varying model of the system. The results show that the developed controllers meet the specified requirements regarding the equivalent acceleration, Motion Sickness Dose Value, and deviation from the de sired trajectory.

Academic research in the field of autonomous vehicles has reached high popularity in recent years related to several topics as sensor technologies, V2X communications, safety, security, decision making, control, and even legal and standardization rules. Besides classic control design approaches, Artificial Intelligence and Machine Learning methods are present in almost all of these fields. Another part of research focuses on different layers of Motion Planning, such as strategic decisions, trajectory planning, and control. A wide range of techniques in Machine Learning itself have been developed, and this article describes one of these fields, Deep Reinforcement Learning (DRL). The paper provides insight into the hierarchical motion planning problem and describes the basics of DRL. The main elements of designing such a system are the modeling of the environment, the modeling abstractions, the description of the state and the perception models, the appropriate rewarding, and the realization of the underlying neural network. The paper describes vehicle models, simulation possibilities and computational requirements. Strategic decisions on different layers and the observation models, e.g., continuous and discrete state representations, grid-based, and camera-based solutions are presented. The paper surveys the state-of-art solutions systematized by the different tasks and levels of autonomous driving, such as car-following, lane-keeping, trajectory following, merging, or driving in dense traffic. Finally, open questions and future challenges are discussed.

Reinforcement learning applications are spreading among different domains, including autonomous vehicle control. The diverse situations that can happen during, for instance, at a highway commute are infinite, and with labeled data, the perfect coverage of all use-cases sounds ambitious. However, with the complex tasks and complicated scenarios faced during an autonomous vehicle system design, the credit assignment problem arises. How to construct appropriate objectives for the artificial intelligence to learn and the preferences between the different goals also matter of the designer’s choice. This work attempts to tackle the problem by utilizing successor features and providing a possible decomposition of the reward functions, guiding the agent’s actions. This method makes the training easier for the agent and enables immediate, profound performance on new combined tasks. Furthermore, with the optimal composition, the desired behavior can be fine-tuned, and as an auxiliary gain, the decomposition empowers different driving styles and makes driving preferences rapidly changeable. We introduce the adaptation of FastRL algorithm to autonomous vehicle domain, meanwhile developing a stabilizing way of using Successor Features, namely DoubleFastRL. We compare our solution for a highway driving scenario with basic agents such as Q-learning having multi-objective training.

The paper presents a motion planning solution which combines classic control techniques with machine learning.For this task, a reinforcement learning environment has been created, where the quality of the fulfilment of the designed path by a classic control loop provides the reward function.System dynamics is described by a nonlinear planar single track vehicle model with dynamic wheel mode model.The goodness of the planned trajectory is evaluated by driving the vehicle along the track.The paper shows that this encapsulated problem and environment provides a one-step reinforcement learning task with continuous actions that can be handled with Deep Deterministic Policy Gradient learning agent.The solution of the problem provides a real-time neural network-based motion planner along with a tracking algorithm, and since the trained network provides a preliminary estimate on the expected reward of the current state-action pair, the system acts as a trajectory feasibility estimator as well.Highly automated and autonomous driving is expected to enhance the quality of road transportation in multiple aspects, such as increasing the level of safety while reducing fuel consumption and emissions.The development potential makes the topic one of the most intense research fields both for vehicle industry and related academic institutions.This paper deals with the problem of feasible motion planning, i.e. the design and evaluation of the trajectory that the vehicle must follow.Many different approaches have been evolved over the years to solve the motion planning problem for wheeled vehicles, all having advantages and drawbacks as well.Geometric approaches assemble the path of the vehicle from geometric curves as clothoids, circular arcs and splines.A popular choice is to define curvature as function of arc length (Li et al., 2015).They are often used in simple lowa

One of the basis of the development in future intelligent transportation systems is the vehicle itself, whose provided features and communication possibilities are continuously expanding. One of the main areas in development of the vehicle of the future is mechatronics. Teaching this topic is a complex challenge because of the multidisciplinary nature of the topic. On the other hand, the requirements of the smart city or the smart mobility of the future demand complex solutions from next generation of engineers. The topics include electrical, mechanical engineering, control theory, and information and communications technology, in which fields the students must deepen their knowledge. Tasks and solving problems in mechatronics require cognitive and operational knowledge and practical experience in systems design and analysis. There is a strong demand for project-based teaching using certain kinds of practical material. This paper presents two educational frameworks designed for students with a specialization in vehicle mechatronics. Both are aimed at the emulation of real vehicle behaviors: the first is on the electric control unit level, whereas the second is on the vehicle level.