Pedro Revenga

The successful transfer of autonomous driving stacks (ADS) from simulation to the real world faces two main challenges: the Reality Gap (RG)—mismatches between simulated and real behaviors—and the Performance Gap (PG)—differences between expected and achieved performance across domains. We propose a Methodology for Closing Reality and Performance Gaps (MCRPG), a structured and iterative approach that jointly reduces RG and PG through parameter tuning, cross-domain metrics, and staged validation. MCRPG comprises three stages—Digital Twin, Parallel Execution, and Real-World—to progressively align ADS behavior and performance. To ground and validate the method, we present an open-source, cost-effective Development and Validation Platform (DVP) that integrates an ROS-based modular ADS with the CARLA simulator and a custom autonomous electric vehicle. We also introduce a two-level metric suite: (i) Reality Alignment via Maximum Normalized Cross-Correlation (MNCC) over multi-modal signals (e.g., ego kinematics, detections), and (ii) Ego-Vehicle Performance covering safety, comfort, and driving efficiency. Experiments in an urban scenario show convergence between simulated and real behavior and increasingly consistent performance across stages. Overall, MCRPG and DVP provide a replicable framework for robust, scalable, and accessible Sim2Real research in autonomous navigation techniques.

Three robotic platforms designed to serve as an entry point to mobile robotics are presented. One platform (Alcabot Electrical) is oriented for elementary or early high school students with a robot telecontrolled by electromechanical elements, without a microprocessor. A second platform (TuBot) based on Arduino is oriented to serve as an entry point in high school with certain limitations of functionality. The third platform (Alcabot Starting) is also based on Arduino but with components that make it very versatile, with many possibilities for expansion and oriented to secondary education, high school or even as an entry point for university students. All three platforms are designed with components easily available on the internet, low cost and with online support documentation for both hardware and software. All three platforms have been used with students and in teacher training courses with very good acceptance. The Alcabot Electrical and Alcabot Starting platforms have been designed as an entry point for the Eurobot and Eurobot Junior competitions to introduce children and young people to robotics and then modify, complement or use their components in their own designs adapted to the competition problem.

RADARs and cameras have been present in automotives since the advent of ADAS, as they possess complementary strengths and weaknesses but have been underlooked in the context of learning-based methods. In this work, we propose a method to perform object detection in autonomous driving based on a geometrical and sequential sensor fusion of 3+1D RADAR and semantics extracted from camera data through point cloud painting from the perspective view. To achieve this objective, we adapt PointPainting from the LiDAR and camera domains to the sensors mentioned above. We first apply YOLOv8-seg to obtain instance segmentation masks and project their results to the point cloud. As a refinement stage, we design a set of heuristic rules to minimize the propagation of errors from the segmentation to the detection stage. Our pipeline concludes by applying PointPillars as an object detection network to the painted RADAR point cloud. We validate our approach in the novel View of Delft dataset, which includes 3+1D RADAR data sequences in urban environments. Experimental results show that this fusion is also suitable for RADAR and cameras as we obtain a significant improvement over the RADAR-only baseline, increasing mAP from 41.18 to 52.67 (+27.9%).

The incorporation of FPGA-based System on Chip design courses in the curriculum of undergraduate and master's degree programs is becoming more and more common. The cost of development boards is prohibitive for most students, which is detrimental to their academic performance by limiting the number of hours they can devote to experimentation. The availability of a remote lab would solve this problem. This paper presents the infrastructure of a remote laboratory for the design of FPGA-based SoCs. This infrastructure is used in the teaching of a subject of the last year of an engineering degree. The environment allows the use of evaluation boards and development tools with full functionality. In addition, users can conveniently reserve the boards via a web application for use outside class hours and the infrastructure manages the simultaneous use of several boards, preventing users from interfering with each other's work. These advantages are complemented by the ability to virtualize user I/O interfaces and to use remote instrumentation to measure the response of the design.

Monitoring driver attention using the gaze estimation is a typical approach used on road scenes. This indicator is of great importance for safe driving, specially on Level 3 and Level 4 automation systems, where the take over request control strategy could be based on the driver’s gaze estimation. Nowadays, gaze estimation techniques used in the state-of-the-art are intrusive and costly, and these two aspects are limiting the usage of these techniques on real vehicles. To test this kind of application, there are some databases focused on critical situations in simulation, but they do not show real accidents because of the complexity and the danger to record them. Within this context, this paper presents a low-cost and non-intrusive camera-based gaze mapping system integrating the open-source state-of-the-art OpenFace 2.0 Toolkit to visualize the driver focalization on a database composed of recorded real traffic scenes through a heat map using NARMAX (Nonlinear AutoRegressive Moving Average model with eXogenous inputs) to establish the correspondence between the OpenFace 2.0 parameters and the screen region the user is looking at. This proposal is an improvement of our previous work, which was based on a linear approximation using a projection matrix. The proposal has been validated using the recent and challenging public database DADA2000, which has 2000 video sequences with annotated driving scenarios based on real accidents. We compare our proposal with our previous one and with an expensive desktop-mounted eye-tracker, obtaining on par results. We proved that this method can be used to record driver attention databases.