Trajectory Planning for Autonomous Vehicles in Time Varying Environments Using Support Vector Machines
A novel trajectory planning method is proposed in time varying
environments for highway driving scenarios. The main objective is
to ensure computational efficiency in the approach, while still
ensuring collision avoidance with moving obstacles and respecting
vehicle constraints such as comfort criteria and roll-over
limits. The trajectory planning problem is separated into finding a
collision free corridor in space-time domain using a support vector
machine (SVM), which means solving a convex optimization
problem. After that a time-monotonic path is found in the collision
free corridor by solving a simple search problem that can be solved
efficiently. The resulting path in space-time domain corresponds to
the resulting planned trajectory of the vehicle.
% The time dimension is added to stationary and moving obstacles to
% predict their location and considering the location of the
% obstacles with respect to the street center line the obstacles are
% labeled to left and right obstacles.
% The support vector machine (SVM) finds a collision free corridor and
% then the trajectory planning takes place in dimensions (x, y ,t)
% with simultaneous path and speed planning.
The planner is a deterministic search method associated with a cost
function that keeps the trajectory kinematically feasible and close
to the maximum separating surface, given by the SVM. A kinematic
motion model is used to construct motion primitives in the
space-time domain representing the non-holonomic behavior of the
vehicle and is used to ensure physical constraints on the states of
the vehicle such as acceleration, speed, jerk, steer and steer
rate. The speed limits include limitations by law and also rollover
speed limits. Two highway maneuvers have been used as test
scenarios to illustrate the performance of the proposed algorithm.
Mahdi Morsali, Jan Åslund and Erik Frisk
Senast uppdaterad: 2019-04-24