Browsing by Author "Justo, Alberto"
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Item Enhancing Motion Prediction by a Cooperative Framework(Institute of Electrical and Electronics Engineers Inc., 2024) Araluce, Javier; Justo, Alberto; Arizala, Asier; González, Leonardo; Díaz, Sergio; CCAMCooperative perception is a technique that enhances the on-board sensing and perception of automated vehicles by fusing data from multiple sources, such as other vehicles, roadside infrastructure, cloud/edge servers, among others. It can improve the performance of automated driving in complex scenarios, like unsignalled roundabouts or intersections where the visibility and awareness of other road users are limited. Motion Prediction (MP) is a key component of cooperative perception, as it enables the estimation and prediction of microscopic traffic states, such as the positions and speeds of all vehicles. It relies on information from other agents and their relationships among them, so the information provided by external sources is valuable because it enhances the understanding of the scene.In this paper, we present improved MP through Vehicle to Vehicle (V2V) communication. We have trained Hierarchical Vector Transformer (HiVT) to be a map-less solution that can be used in road domains. With this model, we have implemented and compared two association methods to evaluate our framework on a real V2V dataset (V2V4Real). Our evaluation concludes that our V2V MP improves performance due to better scene understanding over a single-vehicle MP.Item SimBusters: Bridging Simulation Gaps in Intelligent Vehicles Perception(Institute of Electrical and Electronics Engineers Inc., 2024) Justo, Alberto; Araluce, Javier; Romera, Javier; Rodriguez-Arozamena, Mario; González, Leonardo; Díaz, Sergio; CCAMRecent advances in automated vehicle technology rely heavily on simulated environments for training and testing. However, a significant challenge lies in bridging the gap between simulated and real-world scenarios, as discrepancies between these environments can affect the performance and reliability after that transition, especially in perception. Particularly, LiDAR sensors are highly affected in this matter due to disparities in pointcloud distribution and intensity. Therefore, this paper presents an innovative approach to bridge the gap between simulation and reality. For it, we test and validate a realistic LiDAR library, PCSim, within the CARLA simulator, providing an enhanced simulation environment. Our method involves integrating perception models, pre-trained on real-world datasets, in this environment. Then, we develop a Real2Sim domain adaptation method to transfer these models into the library, leveraging their performance. Finally, we evaluate the 3D object detection models in PCSim LiDARs to prove our methodology.We have assessed this proposal in PCSim, obtaining promising results in mitigating the simulation-reality gap. Our evaluations provide a guidance for future effective transition from virtual environments to real-world applications.