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Scenario Model Predictive Control for autonomous driving on highway

Author(s):

G. Cesari
Conference/Journal:

Master Thesis, HS15 (10501)
Abstract:

This thesis presents an innovative control design for autonomous vehicle driving on a highway. The controlled vehicle is capable of following road lanes while maintaining a security distance from a potential vehicle ahead. If the leading vehicle is traveling at a lower velocity than the controlled vehicle, the algorithm can perform autonomous lane changes provided that they are deemed safe. Lane changes that appear like they would cause a collision are delayed until they are considered to be safe. The control design is based on Scenario Model Predictive Control that takes into account the uncertainty of the traffic environment. A small number of future trajectories for every vehicle in the environment are predicted on the basis of a novel algorithm described. Given the state of a vehicle, the prediction algorithm first identifies the maneuver that is being performed. Next, a few parameters that characterize the primitive motion associated with the trajectory are identified while a few others are sampled from random distributions. Finally, in order to present the effectiveness of the developed controller, simulations and experimental results are provided.

Supervisors: Francesco Borrelli (University of California, Berkeley), Ashwin Carvahlo (University of California, Berkeley, Manfred Morari

Year:

2016
Type of Publication:

(12)Diploma/Master Thesis
Supervisor:

M. Morari

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% Autogenerated BibTeX entry
@PhdThesis { Xxx:2016:IFA_5384
}
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