Distributed Simulation Platform for Autonomous Driving

TL;DR

This paper presents a distributed simulation platform for autonomous driving that combines Spark for distributed computing and ROS for data playback, enabling efficient large-scale testing of autonomous vehicle algorithms.

Contribution

It introduces a novel integration of Spark and ROS to create a scalable, high-performance simulation environment for autonomous vehicle testing.

Findings

Efficient handling of massive simulation data sets.

Scalable simulation platform built on Spark and ROS.

Improved testing throughput for autonomous driving algorithms.

Abstract

Autonomous vehicle safety and reliability are the paramount requirements when developing autonomous vehicles. These requirements are guaranteed by massive functional and performance tests. Conducting these tests on real vehicles is extremely expensive and time consuming, and thus it is imperative to develop a simulation platform to perform these tasks. For simulation, we can utilize the Robot Operating System (ROS) for data playback to test newly developed algorithms. However, due to the massive amount of simulation data, performing simulation on single machines is not practical. Hence, a high-performance distributed simulation platform is a critical piece in autonomous driving development. In this paper we present our experiences of building a production distributed autonomous driving simulation platform. This platform is built upon Spark distributed framework, for distributed computing management, and ROS, for data playback simulations.