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LGSVL Simulator: A High Fidelity Simulator for Autonomous Driving

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arxiv 2005.03778 v3 pith:BAOU4PTI submitted 2020-05-07 cs.RO cs.LGcs.SYeess.SY

Guodong Rong , Byung Hyun Shin , Hadi Tabatabaee , Qiang Lu , Steve Lemke , Mārtiņš Možeiko , Eric Boise , Geehoon Uhm
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Mark Gerow Shalin Mehta Eugene Agafonov Tae Hyung Kim Eric Sterner Keunhae Ushiroda Michael Reyes Dmitry Zelenkovsky Seonman Kim
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classification cs.ROcs.LGcs.SYeess.SY
keywords simulatorautonomousdrivingapolloautowarecorecreateengine
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Cosmos-Drive-Dreams: Scalable Synthetic Driving Data Generation with World Foundation Models

    cs.CV 2025-06 conditional novelty 6.0 of 10

    Post-trained Cosmos world models generate controllable multi-view driving videos and LiDAR; augmenting real AV training data with these synthetic clips improves downstream perception and policy metrics, especially in ...

  2. OpenCAMS: An Open-Source Connected and Automated Mobility Co-Simulation Platform for Advancing Next-Generation Intelligent Transportation Systems Research

    cs.SE 2025-07 conditional novelty 5.0 of 10

    OpenCAMS couples three simulators (SUMO, CARLA, OMNeT++) in a time-synchronized loop to enable integrated testing of connected and automated mobility scenarios.

  3. A Comprehensive Review of Reinforcement Learning for Autonomous Driving in the CARLA Simulator

    cs.RO 2025-09 conditional novelty 4.0 of 10

    A survey of roughly 100 CARLA reinforcement learning papers, mapping algorithm families, representations, rewards, evaluation metrics, towns, and open challenges.

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