Motion Planning and Safety for Autonomous Driving
Loading...
Date
2019-12-11
Authors
De Iaco, Ryan
Advisor
Smith, Stephen
Czarnecki, Krzysztof
Czarnecki, Krzysztof
Journal Title
Journal ISSN
Volume Title
Publisher
University of Waterloo
Abstract
This thesis discusses two different problems in motion planning for autonomous driving.
The first is the problem of optimizing a lattice planner control set for any particular
autonomous driving task, with the goal of reducing planning time for that task. The
driving task is encoded in the form of a dataset of trajectories executed while performing
said task. In addition to improving planning time, the optimized control set should capture
the driving style of the dataset. In this sense, the control set is learned from the data and is
tailored to a particular task. To determine the value of control actions to add to the control
set, a modified version of the Fréchet distance is used to score how useful control actions
are for generating paths similar to those in the dataset. This method is then compared to
the state of the art lattice planner control set optimization technique in terms of planning
runtime for the learned task.
The second problem is the task of extending the Responsibility-Sensitive Safety (RSS)
framework by introducing swerve manoeuvres in addition to the nominal braking manoeu-
vres present in the framework. This includes comparing the clearance distances required by
a swerve to the braking distances in the original framework. This comparison shows that
swerve manoeuvres require less distance gap in order to reach safety from a braking agent
in front of the autonomous vehicle at higher speeds. For more realistic swerve manoeuvres,
the kinematic bicycle model is used rather than the 2-D double integrator model consid-
ered in RSS. An upper bound is then computed on the required clearance distance for a
swerve manoeuvre that satisfies bicycle kinematics. A longitudinal safe following distance
is then derived that is provably safe, and is shown to be lower than the following distance
required by RSS at higher speeds. The use of the kinematic bicycle model is then validated
by computing swerve manoeuvres with a dynamic single-track car model and Pacejka tire
model, and comparing the single-track swerves to the bicycle swerves.
Description
Keywords
autonomous driving, motion planning, safety, lattice planning, responsibility-sensitive safety, algorithms, collision avoidance, vehicle kinematics, combinatorial optimization, machine learning