Browsing by Keyword "Intelligent transportation systems"
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Item A complete framework for developing and testing automated driving controllers(2017-07) Lattarulo, Ray; Perez, Joshue; Dendaluze, Martin; Dendaluce, Martin; CCAM; Tecnalia Research & InnovationIntelligent vehicles have improved their highly and fully automated driving capacities in the last years. Most of the developments are driven by the fast evolution of embedded systems for the acquisition, perception and communication modules. However, the fast growing of the automated vehicle market demands modern tools for validation, integration and testing of these new embedded functionalities, specially related to Advanced Driving Assistance Systems (ADAS). In this paper, a testing methodology for validation of path planning and control algorithms for current and future automated vehicles is presented. A high degree of modularity and adaptability have been considered in the design of the proposed method. It is based on a software tool for vehicle modeling, called Dynacar, which allows a good trajectory definition, cooperative maneuvers interaction and virtual validation. Different types of vehicles, scenarios (i.e.: urban, interurban, highways under different environmental conditions) and controllers can be tested. Moreover, Hardware-In-the-Loop configuration (i.e. electronic control units) can be also tested. Simulation results show a good performance in the implementation and configuration of urban scenarios, using different controllers in the proposed frameworkItem From Data to Actions in Intelligent Transportation Systems: A Prescription of Functional Requirements for Model Actionability(Multidisciplinary Digital Publishing Institute (MDPI), 2021-02-05) Laña, Ibai; Sanchez-Medina, Javier J.; Vlahogianni, Eleni I.; Del Ser, JavierAdvances in Data Science permeate every field of Transportation Science and Engineering, resulting in developments in the transportation sector that are data-driven. Nowadays, Intelligent Transportation Systems (ITS) could be arguably approached as a “story” intensively producing and consuming large amounts of data. A diversity of sensing devices densely spread over the infrastructure, vehicles or the travelers’ personal devices act as sources of data flows that are eventually fed into software running on automatic devices, actuators or control systems producing, in turn, complex information flows among users, traffic managers, data analysts, traffic modeling scientists, etc. These information flows provide enormous opportunities to improve model development and decision-making. This work aims to describe how data, coming from diverse ITS sources, can be used to learn and adapt data-driven models for efficiently operating ITS assets, systems and processes; in other words, for data-based models to fully become actionable. Grounded in this described data modeling pipeline for ITS, we define the characteristics, engineering requisites and challenges intrinsic to its three compounding stages, namely, data fusion, adaptive learning and model evaluation. We deliberately generalize model learning to be adaptive, since, in the core of our paper is the firm conviction that most learners will have to adapt to the ever-changing phenomenon scenario underlying the majority of ITS applications. Finally, we provide a prospect of current research lines within Data Science that can bring notable advances to data-based ITS modeling, which will eventually bridge the gap towards the practicality and actionability of such models.Item Parametric-based path generation for automated vehicles at roundabouts(2017-04-01) González, David; Perez, Joshue; Milanés, Vicente; CCAMUrban environments are becoming more and more complex because several factors as consecutive cross- roads or lanes changes. These scenarios demand specific infrastructures—i.e. roundabouts, for improving traffic flow compared with traditional intersections. A roundabout removes timeouts associated with traf- fic lights at crossroads and trajectory conflicts among drivers. However, it is a challenging scenario for both humans and automated vehicles. This work presents a path planning method for automated vehi- cle driving at roundabouts. The proposed system achieves a G 1 continuous path, minimizing curvature steps to increase smoothness, dividing the driving process in three stages: entrance maneuver, driving within the roundabout and exit maneuver. Parametric equations are generated to deal with automated roundabout driving. This approach allows a real time planning considering two-lane roundabouts, taking different exits. Tests in simulated environments and on our prototype platform—Cybercar—validate the system on real urban environments, showing the proper behavior of the system.Item Study of the lane change maneuver: automated driving use case(2017) Lattarulo, Ray; Perez, JoshueNowadays, the idea of a completely interconnected city with infrastructure, vehicles and even the humans on a connectivity loop is a reality.Item A Two-Stage Real-Time Path Planning: Application to the Overtaking Manuever: Application to the Overtaking Manuever(2020) Garrido, Fernando; Gonzalez, Leonardo; Milanes, Vicente; Perez, Joshue; Nashashibi, Fawzi; Rastelli, Joshue Perez; CCAMThis paper proposes a two-stage local path planning approach to deal with all kinds of scenarios (i.e. intersections, turns, roundabouts). The first stage carries out an off-line optimization, considering vehicle kinematics and road constraints. The second stage includes all dynamic obstacles in the scene, generating a continuous path in real-time. Human-like driving style is provided by evaluating the sharpness of the road bends and the available space among them, optimizing the drivable area. The proposed approach is validated on overtaking scenarios where real-time path planning generation plays a key role. Simulation and real results on an experimental automated platform provide encouraging results, generating real-time collision-free paths while maintaining the defined smoothness criteria.