Anticipatory kinodynamic motion planner for computing the best path and velocity trajectory in autonomous driving
This paper presents an approach, using an anticipatory kinodynamic motion planner, for obtaining the best trajectory and velocity profile for autonomous driving in dynamic complex environments, such as driving in urban scenarios. The planner discretizes the road search space and looks for the best v...
| Autores: | , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2019 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/130073 |
| Acceso en línea: | https://hdl.handle.net/2117/130073 https://dx.doi.org/10.1016/j.robot.2018.11.022 |
| Access Level: | acceso abierto |
| Palabra clave: | Automobile driving Autonomous driving ADAS Urban Anticipation Kinodynamic motion planning Path planning -splines Velocity profiles Automòbils -- Conducció -- Simuladors Àrees temàtiques de la UPC::Informàtica::Automàtica i control |
| Sumario: | This paper presents an approach, using an anticipatory kinodynamic motion planner, for obtaining the best trajectory and velocity profile for autonomous driving in dynamic complex environments, such as driving in urban scenarios. The planner discretizes the road search space and looks for the best vehicle path and velocity profile at each control period of time, assuming that the static and dynamic objects have been detected. The main contributions of the work are in the anticipatory kinodynamic motion planner, in a fast method for obtaining the -splines for path generation, and in a method to compute and select the best velocity profile at each candidate path that fulfills the vehicle kinodynamic constraints, taking into account the passenger comfort. The method has been developed and tested in MATLAB through a set of simulations in different representative scenarios, involving fixed obstacles and moving vehicles. The outcome of the simulations shows that the anticipatory kinodynamic planner performs correctly in diverse dynamic scenarios, maintaining smooth accelerations for passenger comfort |
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