SVM Enhanced Frenet Frame Planner For Safe Navigation Amidst Moving Agents
This work addresses safety in autonomous driving by improving obstacle avoidance, though it is incremental as it builds on existing Frenet frame methods.
The paper tackles safe navigation among moving agents by enhancing a Frenet frame trajectory planner with an SVM layer to maximize separation from dynamic obstacles, resulting in larger offsets compared to the baseline in simulations and real-world tests.
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.