LGSVL Simulator: A High Fidelity Simulator for Autonomous Driving
- URL: http://arxiv.org/abs/2005.03778v3
- Date: Mon, 22 Jun 2020 00:47:14 GMT
- Title: LGSVL Simulator: A High Fidelity Simulator for Autonomous Driving
- Authors: Guodong Rong, Byung Hyun Shin, Hadi Tabatabaee, Qiang Lu, Steve Lemke,
M\=arti\c{n}\v{s} Mo\v{z}eiko, Eric Boise, Geehoon Uhm, Mark Gerow, Shalin
Mehta, Eugene Agafonov, Tae Hyung Kim, Eric Sterner, Keunhae Ushiroda,
Michael Reyes, Dmitry Zelenkovsky, Seonman Kim
- Abstract summary: We introduce the LGSVL Simulator which is a high fidelity simulator for autonomous driving.
The simulator engine provides end-to-end, full-stack simulation which is ready to be hooked up to Autoware and Apollo.
- Score: 1.1742615451804794
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Testing autonomous driving algorithms on real autonomous vehicles is
extremely costly and many researchers and developers in the field cannot afford
a real car and the corresponding sensors. Although several free and open-source
autonomous driving stacks, such as Autoware and Apollo are available, choices
of open-source simulators to use with them are limited. In this paper, we
introduce the LGSVL Simulator which is a high fidelity simulator for autonomous
driving. The simulator engine provides end-to-end, full-stack simulation which
is ready to be hooked up to Autoware and Apollo. In addition, simulator tools
are provided with the core simulation engine which allow users to easily
customize sensors, create new types of controllable objects, replace some
modules in the core simulator, and create digital twins of particular
environments.
Related papers
- GarchingSim: An Autonomous Driving Simulator with Photorealistic Scenes
and Minimalist Workflow [24.789118651720045]
We introduce an autonomous driving simulator with photorealistic scenes.
The simulator is able to communicate with external algorithms through ROS2 or Socket.IO.
We implement a highly accurate vehicle dynamics model within the simulator to enhance the realism of the vehicle's physical effects.
arXiv Detail & Related papers (2024-01-28T23:26:15Z) - Choose Your Simulator Wisely: A Review on Open-source Simulators for
Autonomous Driving [25.320362844415012]
There is a growing concern about the validity of algorithms developed and evaluated in simulators.
This paper analyzes the evolution of simulators and explains how the functionalities and utilities have developed.
Recommendations for select simulators are presented, considering factors such as accessibility, maintenance status, and quality.
arXiv Detail & Related papers (2023-11-18T12:30:41Z) - Waymax: An Accelerated, Data-Driven Simulator for Large-Scale Autonomous
Driving Research [76.93956925360638]
Waymax is a new data-driven simulator for autonomous driving in multi-agent scenes.
It runs entirely on hardware accelerators such as TPUs/GPUs and supports in-graph simulation for training.
We benchmark a suite of popular imitation and reinforcement learning algorithms with ablation studies on different design decisions.
arXiv Detail & Related papers (2023-10-12T20:49:15Z) - MARS: An Instance-aware, Modular and Realistic Simulator for Autonomous
Driving [13.571775151180923]
We propose an autonomous driving simulator based upon neural radiance fields (NeRFs)
Our simulator models the foreground instances and background environments separately with independent networks.
Our simulator set new state-of-the-art photo-realism results given the best module selection.
arXiv Detail & Related papers (2023-07-27T17:59:52Z) - Generative AI-empowered Simulation for Autonomous Driving in Vehicular
Mixed Reality Metaverses [130.15554653948897]
In vehicular mixed reality (MR) Metaverse, distance between physical and virtual entities can be overcome.
Large-scale traffic and driving simulation via realistic data collection and fusion from the physical world is difficult and costly.
We propose an autonomous driving architecture, where generative AI is leveraged to synthesize unlimited conditioned traffic and driving data in simulations.
arXiv Detail & Related papers (2023-02-16T16:54:10Z) - Autonomous Driving Simulator based on Neurorobotics Platform [11.25880077022107]
There are many artificial intelligence algorithms for autonomous driving, but directly installing these algorithms on vehicles is unrealistic and expensive.
At the same time, many of these algorithms need an environment to train and optimize.
This report will start with a little research on the Neurorobotics Platform and analyze the potential and possibility of developing a new simulator to achieve the true real-world simulation goal.
arXiv Detail & Related papers (2022-12-31T01:12:27Z) - Indy Autonomous Challenge -- Autonomous Race Cars at the Handling Limits [81.22616193933021]
The team TUM Auton-omous Motorsports will participate in the Indy Autonomous Challenge in Octo-ber 2021.
It will benchmark its self-driving software-stack by racing one out of ten autonomous Dallara AV-21 racecars at the Indianapolis Motor Speedway.
It is an ideal testing ground for the development of autonomous driving algorithms capable of mastering the most challenging and rare situations.
arXiv Detail & Related papers (2022-02-08T11:55:05Z) - VISTA 2.0: An Open, Data-driven Simulator for Multimodal Sensing and
Policy Learning for Autonomous Vehicles [131.2240621036954]
We present VISTA, an open source, data-driven simulator that integrates multiple types of sensors for autonomous vehicles.
Using high fidelity, real-world datasets, VISTA represents and simulates RGB cameras, 3D LiDAR, and event-based cameras.
We demonstrate the ability to train and test perception-to-control policies across each of the sensor types and showcase the power of this approach via deployment on a full scale autonomous vehicle.
arXiv Detail & Related papers (2021-11-23T18:58:10Z) - DriveGAN: Towards a Controllable High-Quality Neural Simulation [147.6822288981004]
We introduce a novel high-quality neural simulator referred to as DriveGAN.
DriveGAN achieves controllability by disentangling different components without supervision.
We train DriveGAN on multiple datasets, including 160 hours of real-world driving data.
arXiv Detail & Related papers (2021-04-30T15:30:05Z) - LiDARsim: Realistic LiDAR Simulation by Leveraging the Real World [84.57894492587053]
We develop a novel simulator that captures both the power of physics-based and learning-based simulation.
We first utilize ray casting over the 3D scene and then use a deep neural network to produce deviations from the physics-based simulation.
We showcase LiDARsim's usefulness for perception algorithms-testing on long-tail events and end-to-end closed-loop evaluation on safety-critical scenarios.
arXiv Detail & Related papers (2020-06-16T17:44:35Z) - A machine learning environment for evaluating autonomous driving
software [1.6516902135723865]
We present a machine learning environment for detecting autonomous vehicle corner case behavior.
Our environment is based on connecting the CARLA simulation software to machine learning framework and custom AI client software.
Our system can search for corner cases where the vehicle AI is unable to correctly understand the situation.
arXiv Detail & Related papers (2020-03-07T13:05:03Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.