dc.contributor.author | Dakibay, Assylbek | |
dc.date.accessioned | 2017-07-13 15:12:57 (GMT) | |
dc.date.available | 2017-07-13 15:12:57 (GMT) | |
dc.date.issued | 2017-07-13 | |
dc.date.submitted | 2017-07-05 | |
dc.identifier.uri | http://hdl.handle.net/10012/12065 | |
dc.description.abstract | In near future Autonomous driving will affect every aspect of transportation and offer a
significant boost in mobility for everyone. Autonomous driving techniques and modules
must be chosen according to the task the platform is developed for. Slow speed driving
on campus or highway driving in poor weather conditions, may require different sets of
sensors, vehicle models and as a result different software architecture. Some of the main
modules that an autonomous driving system needs are the vehicle state estimator and
vehicle controller. The development of these two modules relies heavily on the robustness
of the vehicle model chosen and the task at hand.
University of Waterloo decided to join the Autonomous Driving research by partici-
pating in the project, which required development and implementation of the autonomous
driving demo sequence for Consumer Electronics Show in 2017. Since the demo sequence
was to be performed at slow speeds and, because certain vehicle parameters were not
available at the time, a kinematic vehicle model was used in implementation of the core
autonomous driving modules: state estimation and control. These modules are imple-
mented on a full scale autonomous driving platform and were designed based on the needs
and requirements of the demo sequence. The implementation results show that the cho-
sen vehicle model enables the state estimator to fuse incoming sensor data and allows the
controller to track the desired path and velocity profile.
For further deployment of the autonomous driving platform for research in urban and
highway driving an aggressive driving framework was proposed that is based on dynamic
vehicle model and incorporates the tire forces in the generation of the speed profile and
keeps the vehicle at the limits of adhesion. The aggressive driving controller can be utilized
for emergency evasive maneuvers at low road friction conditions. The controller was tested
on a high fidelity simulation software for a double lane change emergency maneuver. The
results showed that the aggressive driving framework proposed can be successfully incor-
porated into the autonomous driving architecture and can perform position and velocity
tracking at maximum possible speed. | en |
dc.language.iso | en | en |
dc.publisher | University of Waterloo | en |
dc.subject | Autonomous driving | en |
dc.subject | Vehicle Estimation | en |
dc.subject | Vehicle Control | en |
dc.title | Autonomous Driving: Baseline Autonomy | en |
dc.type | Master Thesis | en |
dc.pending | false | |
uws-etd.degree.department | Mechanical and Mechatronics Engineering | en |
uws-etd.degree.discipline | Mechanical Engineering | en |
uws-etd.degree.grantor | University of Waterloo | en |
uws-etd.degree | Master of Applied Science | en |
uws.contributor.advisor | Waslander, Steven | |
uws.contributor.affiliation1 | Faculty of Engineering | en |
uws.published.city | Waterloo | en |
uws.published.country | Canada | en |
uws.published.province | Ontario | en |
uws.typeOfResource | Text | en |
uws.peerReviewStatus | Unreviewed | en |
uws.scholarLevel | Graduate | en |