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Selecting Spots by Explicitly Predicting Intention from Motion History Improves Performance in Autonomous Parking

Long Kiu Chung, David Isele, Faizan M. Tariq, Sangjae Bae, Shreyas Kousik, Jovin D'sa

TL;DR

This work proposes an AVP pipeline that selects parking spots by explicitly predicting where other agents are going to park from their motion history using learned models and probabilistic belief maps, and shows that this method outperforms existing works that infer intentions from future predicted motion or embed them implicitly in end-to-end models.

Abstract

In many applications of social navigation, existing works have shown that predicting and reasoning about human intentions can help robotic agents make safer and more socially acceptable decisions. In this work, we study this problem for autonomous valet parking (AVP), where an autonomous vehicle ego agent must drop off its passengers, explore the parking lot, find a parking spot, negotiate for the spot with other vehicles, and park in the spot without human supervision. Specifically, we propose an AVP pipeline that selects parking spots by explicitly predicting where other agents are going to park from their motion history using learned models and probabilistic belief maps. To test this pipeline, we build a simulation environment with reactive agents and realistic modeling assumptions on the ego agent, such as occlusion-aware observations, and imperfect trajectory prediction. Simulation experiments show that our proposed method outperforms existing works that infer intentions from future predicted motion or embed them implicitly in end-to-end models, yielding better results in prediction accuracy, social acceptance, and task completion. Our key insight is that, in parking, where driving regulations are more lax, explicit intention prediction is crucial for reasoning about diverse and ambiguous long-term goals, which cannot be reliably inferred from short-term motion prediction alone, but can be effectively learned from motion history.

Selecting Spots by Explicitly Predicting Intention from Motion History Improves Performance in Autonomous Parking

TL;DR

This work proposes an AVP pipeline that selects parking spots by explicitly predicting where other agents are going to park from their motion history using learned models and probabilistic belief maps, and shows that this method outperforms existing works that infer intentions from future predicted motion or embed them implicitly in end-to-end models.

Abstract

In many applications of social navigation, existing works have shown that predicting and reasoning about human intentions can help robotic agents make safer and more socially acceptable decisions. In this work, we study this problem for autonomous valet parking (AVP), where an autonomous vehicle ego agent must drop off its passengers, explore the parking lot, find a parking spot, negotiate for the spot with other vehicles, and park in the spot without human supervision. Specifically, we propose an AVP pipeline that selects parking spots by explicitly predicting where other agents are going to park from their motion history using learned models and probabilistic belief maps. To test this pipeline, we build a simulation environment with reactive agents and realistic modeling assumptions on the ego agent, such as occlusion-aware observations, and imperfect trajectory prediction. Simulation experiments show that our proposed method outperforms existing works that infer intentions from future predicted motion or embed them implicitly in end-to-end models, yielding better results in prediction accuracy, social acceptance, and task completion. Our key insight is that, in parking, where driving regulations are more lax, explicit intention prediction is crucial for reasoning about diverse and ambiguous long-term goals, which cannot be reliably inferred from short-term motion prediction alone, but can be effectively learned from motion history.
Paper Structure (29 sections, 6 equations, 4 figures, 1 table)

This paper contains 29 sections, 6 equations, 4 figures, 1 table.

Table of Contents

  1. Introduction
  2. Related Work
  3. Intents in Social Navigation
  4. Autonomous Parking
  5. Contributions
  6. Problem Formulation
  7. Parking Lot
  8. Vehicle Model
  9. Pedestrian Model
  10. Problem Statement
  11. Simulation Environment
  12. Observation Model
  13. Reactive Agents
  14. Proposed Method: AVP Pipeline
  15. Intention Model
  16. ...and 14 more sections

Figures (4)

  • Figure 1: A flow chart of our method, with each component marked by its corresponding section number. At each timestep, we combine information from the current observation and a probabilistic belief map of where cars are parked to reconstruct the bird's-eye view (BEV) map of other agents. This enables the use of a learned intention model, which we use to make safe and socially acceptable parking decisions.
  • Figure 2: An overview of the methods under comparison, with legend shown on top right. In this scenario, the dynamic vehicle (other agent) is backing into a spot after moving forward to adjust its angle (a "right-south-up" maneuver shen2024parking). Our proposed method is the only one to explicitly and correctly predicted this behavior.
  • Figure 3: Setup of our experiment at $t=0$. At each iteration, some of the bottom 10 spots are randomly chosen to be vacant. Then, 1 or 2 dynamic vehicles would spawn, with pots being their goal. There are always 2 vacant spots on the sides in case the ego failed to compete.
  • Figure 4: Example trajectory predictions (yellow outline) conditioned on the predicted intention (green spot) which we compared in our experiment.