Work

SIM-LDM: Local Dynamic Map Generation Framework using Autonomous Driving Simulator

Year: 2025

Type: conference-paper

Abstract: The widespread adoption of autonomous driving systems underscores their potential to solve societal challenges, such as the shortage of drivers for cargo transport. However, ensuring safety calls for data sharing through infrastructure sensors, including traffic signals, by employing the Local Dynamic Map (LDM). Using infrastructure sensors requires official permits, which makes conducting unrestricted research on LDM employing these sensors difficult for everyone. This paper proposes SIM-LDM, an automated LDM generation approach using AWSIM, an autonomous driving simulator. By replacing real sensor data with simulator-derived data, research utilizing LDM can be conducted without the need for real-world environments, and simulator customization allows straightforward adjustment of experimental conditions. The DSMS, developed on the Confluent Platform, allows data retrieval through HTTP or HTTPS and SQL-based queries, standardizing JSON outputs for broad system compatibility. Performance evaluations reveal data from up to 120 vehicles can be processed within 50 ms intervals on a single PC. Integration of the proposed framework with Autoware, an autonomous driving software, confirms its effectiveness, demonstrating a simulation-driven option for the LDM deployment in autonomous driving research.

DOI: 10.1145/3722567.3727846

Authors: Shunsuke Ito, Ryo Okamura, Chaoran Zhao, Takuya Azumi

Institution: Saitama University

Language: English

Topic: Autonomous Vehicle Technology and Safety

Subfield: Automotive Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Sustainable cities and communities

Open Access status: gold

Funder: Universitas Brawijaya

Award: G6046697300