Note: This content is accessible to all versions of every browser. However, this browser does not seem to support current Web standards, preventing the display of our site's design details.


Scenario Model Predictive Control for autonomous driving on highway


G. Cesari

Master Thesis, HS15 (10501)

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


Type of Publication:

(12)Diploma/Master Thesis

M. Morari

File Download:

Request a copy of this publication.
(Uses JavaScript)
% Autogenerated BibTeX entry
@PhdThesis { Xxx:2016:IFA_5384
Permanent link