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Efficient and Cost-effective Vehicle Recruitment for HD Map Crowdsourcing

Wentao Ye, Yuan Luo, Bo Liu, Jianwei Huang

Abstract

The high-definition (HD) map is a cornerstone of autonomous driving. The crowdsourcing paradigm is a cost-effective way to keep an HD map up-to-date. Current HD map crowdsourcing mechanisms aim to enhance HD map freshness within recruitment budgets. However, many overlook unique and critical traits of crowdsourcing vehicles, such as random arrival and heterogeneity, leading to either compromised map freshness or excessive recruitment costs. Furthermore, these characteristics complicate the characterization of the feasible space of the optimal recruitment policy, necessitating a method to compute it efficiently in dynamic transportation scenarios.To overcome these challenges, we propose an efficient and cost-effective vehicle recruitment (ENTER) mechanism. Specifically, the ENTER mechanism has a threshold structure and balances freshness with recruitment costs while accounting for the vehicles' random arrival and heterogeneity. It also integrates the bound-based relative value iteration (RVI) algorithm, which utilizes the threshold-type structure and upper bounds of thresholds to reduce the feasible space and expedite convergence. Numerical results show that the proposed ENTER mechanism increases the HD map company's payoff by 23.40% and 43.91% compared to state-of-the-art mechanisms that do not account for vehicle heterogeneity and random arrivals, respectively. Furthermore, the bound-based RVI algorithm in the ENTER mechanism reduces computation time by an average of 18.91% compared to the leading RVI-based algorithm.

Efficient and Cost-effective Vehicle Recruitment for HD Map Crowdsourcing

Abstract

The high-definition (HD) map is a cornerstone of autonomous driving. The crowdsourcing paradigm is a cost-effective way to keep an HD map up-to-date. Current HD map crowdsourcing mechanisms aim to enhance HD map freshness within recruitment budgets. However, many overlook unique and critical traits of crowdsourcing vehicles, such as random arrival and heterogeneity, leading to either compromised map freshness or excessive recruitment costs. Furthermore, these characteristics complicate the characterization of the feasible space of the optimal recruitment policy, necessitating a method to compute it efficiently in dynamic transportation scenarios.To overcome these challenges, we propose an efficient and cost-effective vehicle recruitment (ENTER) mechanism. Specifically, the ENTER mechanism has a threshold structure and balances freshness with recruitment costs while accounting for the vehicles' random arrival and heterogeneity. It also integrates the bound-based relative value iteration (RVI) algorithm, which utilizes the threshold-type structure and upper bounds of thresholds to reduce the feasible space and expedite convergence. Numerical results show that the proposed ENTER mechanism increases the HD map company's payoff by 23.40% and 43.91% compared to state-of-the-art mechanisms that do not account for vehicle heterogeneity and random arrivals, respectively. Furthermore, the bound-based RVI algorithm in the ENTER mechanism reduces computation time by an average of 18.91% compared to the leading RVI-based algorithm.

Paper Structure

This paper contains 28 sections, 6 theorems, 17 equations, 11 figures, 3 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Related Works
  3. Mechanism Design for AoI-Related Mobile Crowdsensing
  4. Mechanism Design for Crowdsourcing-Based HD Map Updates
  5. System Model
  6. Overview of HD Map Crowdsourcing
  7. Company's Payoff Function
  8. AoI loss
  9. Payoff function
  10. Problem Formulation
  11. ENTER Mechanism Design
  12. Characterization of the Optimal Policy Property
  13. Structure type
  14. Action order
  15. Upper bounds of thresholds
  16. ...and 13 more sections

Key Result

Lemma 1

There exists an optimal policy of MDP $\Lambda$ that is deterministic stationary.

Figures (11)

  • Figure 1: The crowdsourcing workflow for the HD map.
  • Figure 2: The company's payoff vs. the weighted factor $\beta$ under different mechanisms.
  • Figure 3: The average AoI vs. the weighted factor $\beta$ under different mechanisms.
  • Figure 4: The average recruitment cost vs. the weighted factor $\beta$ under different mechanisms.
  • Figure 5: The company's payoff vs. the number of vehicle types under different mechanisms.
  • ...and 6 more figures

Theorems & Definitions (6)

  • Lemma 1
  • Proposition 1
  • Proposition 2
  • Lemma 2
  • Lemma 3
  • Corollary 1