Enhancing Autonomous Driving in Urban Scenarios: A Hybrid Approach with Reinforcement Learning and Classical Control.

The use of Deep Learning algorithms in the domain of Decision Making for Autonomous Vehicles has garnered significant attention in the literature in recent years, showcasing considerable potential. Nevertheless, most of the solutions proposed by the scientific community encounter difficulties in real-world applications. This paper aims to provide a realistic implementation of a hybrid Decision Making module in an Autonomous Driving stack, integrating the learning capabilities from the experience of Deep Reinforcement Learning algorithms and the reliability of classical methodologies.

Combination of In Situ Diffraction Experiments and Acoustic Emission Testing to Understand Compression Behavior of Mg-Gd Alloys.

The compressive deformation of the extruded binary Mg-Gd with gadolinium in solid solution has been studied in situ by combining synchrotron diffraction and acoustic emission techniques during compression tests. These two techniques are useful in investigating the evolution of twinning in all its stages. The extruded bars develop a fiber texture with the basal plane parallel to the extrusion direction.

Diagnosis of multiple sclerosis using optical coherence tomography supported by explainable artificial intelligence.

Background/objectives: Study of retinal structure based on optical coherence tomography (OCT) data can facilitate early diagnosis of relapsing-remitting multiple sclerosis (RRMS). Although artificial intelligence can provide highly reliable diagnoses, the results obtained must be explainable.

Subjects/methods: The study included 79 recently diagnosed RRMS patients and 69 age matched healthy control subjects.

Reinforcement Learning-Based Autonomous Driving at Intersections in CARLA Simulator.

Intersections are considered one of the most complex scenarios in a self-driving framework due to the uncertainty in the behaviors of surrounding vehicles and the different types of scenarios that can be found. To deal with this problem, we provide a Deep Reinforcement Learning approach for intersection handling, which is combined with Curriculum Learning to improve the training process. The state space is defined by two vectors, containing adversaries and ego vehicle information.

Early Diagnosis of Multiple Sclerosis Using Swept-Source Optical Coherence Tomography and Convolutional Neural Networks Trained with Data Augmentation.

Background: The aim of this paper is to implement a system to facilitate the diagnosis of multiple sclerosis (MS) in its initial stages. It does so using a convolutional neural network (CNN) to classify images captured with swept-source optical coherence tomography (SS-OCT).

Methods: SS-OCT images from 48 control subjects and 48 recently diagnosed MS patients have been used.