Autonomous Vehicles.

ADAS

During the latest years the advances in automation and computational capabilities have allowed to create the first autonomous vehicles. On thi sense, the AMPL have worked in different platforms for automated driving. From an autonomous street vehicle, created to test the most advances development in the automotive sector for autonomous movement, to an off road autonomous vehicle, developed for shared traffic scenarios, where the challenges to overcome are different and the interaction with other users is mandatory. 

This web page summarizes the differnt platforms where the AMPL has worked, related to autonomous and connected vehicles. 

Autonomous Driverless Automobile

​Autonomous Driverless Automobile is the first street car of the AMPL, which has been developed together with CESVIMAP from MAPFRE Group  and Polytechnic University of Madrid. 

The vehicle is a Mitsubishi i-Miev, donated by CESVIMAP, which has been modified to allow drive by wire, and created to test the different technologies available for autonomous driving. 
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This partnership has been created with the intention of fostering in the development and testing of the technologies essential for the future autonomous vehicles platforms.  

iCab

iCab is an acronym for Intelligent Campus Automobile, at which multiple unmanned ground vehicles (electric golf carts) navigate autonomously within the campus vicinity to transport visitors from one spot to another between buildings. Developed in the Systems and Engineering Department, with the collaboration of AMPL researches. It  consists on a fleet of two golf carts, modified mechanically and electronically in order to achieve the project objective.

Each golf cart is equipped with multiple sensors for the environment perception. These sensors enable the vehicle to navigate in dynamic surroundings. Accordingly, the vehicle is able to perform the necessary intelligent maneuvers to reach its destination through a collision-free path. Furthermore, the system is able to detect and classify static obstacles (e.g. trees, lamp poles, buildings, generic obstacles), dynamic obstacles (e.g. vehicles, pedestrians, animals, cyclists, motorists), free navigable space among others. The system bases its navigational decisions on global and local maps, using path planning algorithm.