SVM Enhanced Frenet Frame Planner For Safe Navigation Amidst Moving Agents

TL;DR

This paper introduces an SVM-enhanced Frenet frame trajectory planner that improves safety margins in dynamic environments, particularly for autonomous vehicles navigating among moving pedestrians and obstacles.

Contribution

It integrates an SVM layer into the Frenet frame planner to maximize obstacle separation, enhancing safety and navigation capabilities in dynamic scenes.

Findings

Provides larger safety offsets compared to traditional methods.

Demonstrates effectiveness through simulations and real-world experiments.

Suitable for pedestrian-rich environments.

Abstract

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.