mlsanityData to 7 Oct 2026

LASER: Script Execution by Autonomous Agents for On-demand Traffic Simulation

arXiv 2410.16197 ↗First posted 21 Oct 2024cs.RO cs.MAcited by 0

On the map: LLM agents and tool use → Multimodal and embodied agents

Abstract (arXiv)

Autonomous Driving Systems (ADS) require diverse and safety-critical traffic scenarios for effective training and testing, but the existing data generation methods struggle to provide flexibility and scalability. We propose LASER, a novel frame-work that leverage large language models (LLMs) to conduct traffic simulations based on natural language inputs. The framework operates in two stages: it first generates scripts from user-provided descriptions and then executes them using autonomous agents in real time. Validated in the CARLA simulator, LASER successfully generates complex, on-demand driving scenarios, significantly improving ADS training and testing data generation.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Multimodal and embodied agents, one of 16 directions in LLM agents and tool use. Builds on 32 of its 44 references that are in the map. First posted on arXiv 21 Oct 2024.

Builds on

32 of 44 references.

References · 32

  1. 2024The Llama 3 Herd of Modelscited by 14,192
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  12. 2023VoxFormer: Sparse Voxel Transformer for Camera-based 3D Semantic Scene Completioncited by 306
20 more
  1. 2023LMDrive: Closed-Loop End-to-End Driving with Large Language Modelscited by 301
  2. 2023DriveDreamer: Towards Real-world-driven World Models for Autonomous Drivingcited by 258
  3. 2023DriveMLM: Aligning Multi-Modal Large Language Models with Behavioral Planning States for Autonomous Drivingcited by 233
  4. 2023DiLu: A Knowledge-Driven Approach to Autonomous Driving with Large Language Modelscited by 204
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  6. 2021TrafficSim: Learning to Simulate Realistic Multi-Agent Behaviorscited by 192
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  9. 2024ChatScene: Knowledge-Enabled Safety-Critical Scenario Generation for Autonomous Vehiclescited by 92
  10. 2024Editable Scene Simulation for Autonomous Driving via Collaborative LLM-Agentscited by 91
  11. 2023Symphonize 3D Semantic Scene Completion with Contextual Instance Queriescited by 88
  12. 2022BITS: Bi-level Imitation for Traffic Simulationcited by 80
  13. 2023LLM-Assist: Enhancing Closed-Loop Planning with Language-Based Reasoningcited by 59
  14. 2024CarLLaVA: Vision language models for camera-only closed-loop drivingcited by 57
  15. 2023CAT: Closed-loop Adversarial Training for Safe End-to-End Drivingcited by 52
  16. 2023PaSCo: Urban 3D Panoptic Scene Completion with Uncertainty Awarenesscited by 42
  17. 2024Producing and Leveraging Online Map Uncertainty in Trajectory Predictioncited by 32
  18. 2023Data-driven Traffic Simulation: A Comprehensive Reviewcited by 29
  19. 2024T4P: Test-Time Training of Trajectory Prediction via Masked Autoencoder and Actor-specific Token Memorycited by 20
  20. 2024Panacea+: Panoramic and Controllable Video Generation for Autonomous Drivingcited by 5

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  2. 2024Traffic Scene Generation from Natural Language Description for Autonomous Vehicles with Large Language Model
  3. 2023LaMPilot: An Open Benchmark Dataset for Autonomous Driving with Language Model Programs
  4. 2024Large Language Models for Human-like Autonomous Driving: A Survey
  5. 2025AGENTS-LLM: Augmentative GENeration of Challenging Traffic Scenarios with an Agentic LLM Framework
  6. 2025Do LLM Modules Generalize? A Study on Motion Generation for Autonomous Driving
  7. 2025Text2Scenario: Text-Driven Scenario Generation for Autonomous Driving Test
  8. 2026Agent-driven Long-tail Simulation for Autonomous Driving