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Unifying Decision Making and Trajectory Planning in Automated Driving through Time-Varying Potential Fields

David Costa, Francesco Cerrito, Massimo Canale, Carlo Novara

Abstract

This paper proposes a unified decision making and local trajectory planning framework based on Time-Varying Artificial Potential Fields (TVAPFs). The TVAPF explicitly models the predicted motion via bounded uncertainty of dynamic obstacles over the planning horizon, using information from perception and V2X sources when available. TVAPFs are embedded into a finite horizon optimal control problem that jointly selects the driving maneuver and computes a feasible, collision free trajectory. The effectiveness and real-time suitability of the approach are demonstrated through a simulation test in a multi-actor scenario with real road topology, highlighting the advantages of the unified TVAPF-based formulation.

Unifying Decision Making and Trajectory Planning in Automated Driving through Time-Varying Potential Fields

Abstract

This paper proposes a unified decision making and local trajectory planning framework based on Time-Varying Artificial Potential Fields (TVAPFs). The TVAPF explicitly models the predicted motion via bounded uncertainty of dynamic obstacles over the planning horizon, using information from perception and V2X sources when available. TVAPFs are embedded into a finite horizon optimal control problem that jointly selects the driving maneuver and computes a feasible, collision free trajectory. The effectiveness and real-time suitability of the approach are demonstrated through a simulation test in a multi-actor scenario with real road topology, highlighting the advantages of the unified TVAPF-based formulation.
Paper Structure (18 sections, 22 equations, 6 figures, 2 tables)

This paper contains 18 sections, 22 equations, 6 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Notation
  3. Problem set-up
  4. Reference Frames
  5. Control architecture
  6. Trajectory Planner Objectives
  7. Speed Tracking Objective
  8. Static APFs
  9. Time-Varying APF
  10. Optimal Control Problem for Trajectory Planning
  11. Prediction model
  12. Optimal Control Problem
  13. Motion controller
  14. Simulation Results
  15. Test Environment
  16. ...and 3 more sections

Figures (6)

  • Figure 1: Architecture reference frames. Cartesian coordinates, Frenet-Serret coordinates, GPP reference path $\Pi$, and Road boundaries.
  • Figure 2: Control structure architecture and exchanged information.
  • Figure 3: TVAPF prediction in the LTP FHOCP. Top: Feasible overtaking maneuver. Bottom: The LTP detects collision risk, deciding to maintain the current lane behind vehicle A.
  • Figure 4: Overview of the scenario in Roadrunner, detailing the position of the ego vehicle and the actors (L1,O1,O2) before the overtake maneuver.
  • Figure 5: LTP trajectories: Top: Predicted trajectory at a specific LTP instance, showing the current position of the actors. Bottom: Trajectory segments followed by the ego vehicle, including semi-transparent extensions that represent the complete predicted path at each LTP instance.
  • ...and 1 more figures

Theorems & Definitions (3)

  • Remark 1
  • Remark 2
  • Remark 3