  This paper proposes an SVM Enhanced Trajectory Planner for dynamic scenes,
typically those encountered in on road settings. Frenet frame based trajectory
generation is popular in the context of autonomous driving both in research and
industry. We incorporate a safety based maximal margin criteria using a SVM
layer that generates control points that are maximally separated from all
dynamic obstacles in the scene. A kinematically consistent trajectory generator
then computes a path through these waypoints. We showcase through simulations
as well as real world experiments on a self driving car that the SVM enhanced
planner provides for a larger offset with dynamic obstacles than the regular
Frenet frame based trajectory generation. Thereby, the authors argue that such
a formulation is inherently suited for navigation amongst pedestrians. We
assume the availability of an intent or trajectory prediction module that
predicts the future trajectories of all dynamic actors in the scene.
