Realistic Corner Case Generation for Autonomous Vehicles with Multimodal Large Language Model

• URL: http://arxiv.org/abs/2412.00243v1
• Date: Fri, 29 Nov 2024 20:23:28 GMT
• Title: Realistic Corner Case Generation for Autonomous Vehicles with Multimodal Large Language Model
• Authors: Qiujing Lu, Meng Ma, Ximiao Dai, Xuanhan Wang, Shuo Feng,
• Abstract summary: AutoScenario is a framework for realistic corner case generation. It converts safety-critical real-world data from multiple sources into textual representations. It integrates tools from the Simulation of Urban Mobility (SUMO) and CARLA simulators.
• Score: 10.741225574706
• License:
• Abstract: To guarantee the safety and reliability of autonomous vehicle (AV) systems, corner cases play a crucial role in exploring the system's behavior under rare and challenging conditions within simulation environments. However, current approaches often fall short in meeting diverse testing needs and struggle to generalize to novel, high-risk scenarios that closely mirror real-world conditions. To tackle this challenge, we present AutoScenario, a multimodal Large Language Model (LLM)-based framework for realistic corner case generation. It converts safety-critical real-world data from multiple sources into textual representations, enabling the generalization of key risk factors while leveraging the extensive world knowledge and advanced reasoning capabilities of LLMs.Furthermore, it integrates tools from the Simulation of Urban Mobility (SUMO) and CARLA simulators to simplify and execute the code generated by LLMs. Our experiments demonstrate that AutoScenario can generate realistic and challenging test scenarios, precisely tailored to specific testing requirements or textual descriptions. Additionally, we validated its ability to produce diverse and novel scenarios derived from multimodal real-world data involving risky situations, harnessing the powerful generalization capabilities of LLMs to effectively simulate a wide range of corner cases.

Related papers

• Risk-Aware Driving Scenario Analysis with Large Language Models [7.093690352605479]
  Large Language Models (LLMs) can capture nuanced contextual relationships, reasoning, and complex problem-solving. This paper proposes a novel framework that leverages LLMs for risk-aware analysis of generated driving scenarios.
  arXiv  Detail & Related papers  (2025-02-04T09:19:13Z)
• Generating Out-Of-Distribution Scenarios Using Language Models [58.47597351184034]
  Large Language Models (LLMs) have shown promise in autonomous driving. This paper introduces a framework for generating diverse Out-Of-Distribution (OOD) driving scenarios. We evaluate our framework through extensive simulations and introduce a new "OOD-ness" metric.
  arXiv  Detail & Related papers  (2024-11-25T16:38:17Z)
• Multimodal Large Language Model Driven Scenario Testing for Autonomous Vehicles [6.836108615628114]
  We propose OmniTester: a framework that generates realistic and diverse scenarios within a simulation environment. In the experiments, we demonstrated the controllability and realism of our approaches in generating three types of challenging and complex scenarios.
  arXiv  Detail & Related papers  (2024-09-10T12:12:09Z)
• Using Multimodal Large Language Models for Automated Detection of Traffic Safety Critical Events [5.233512464561313]
  Multimodal Large Language Models (MLLMs) offer a novel approach by integrating textual, visual, and audio modalities. Our framework leverages the reasoning power of MLLMs, directing their output through context-specific prompts. Preliminary results demonstrate the framework's potential in zero-shot learning and accurate scenario analysis.
  arXiv  Detail & Related papers  (2024-06-19T23:50:41Z)
• Probing Multimodal LLMs as World Models for Driving [72.18727651074563]
  We look at the application of Multimodal Large Language Models (MLLMs) in autonomous driving. Despite advances in models like GPT-4o, their performance in complex driving environments remains largely unexplored.
  arXiv  Detail & Related papers  (2024-05-09T17:52:42Z)
• SAFE-SIM: Safety-Critical Closed-Loop Traffic Simulation with Diffusion-Controllable Adversaries [94.84458417662407]
  We introduce SAFE-SIM, a controllable closed-loop safety-critical simulation framework. Our approach yields two distinct advantages: 1) generating realistic long-tail safety-critical scenarios that closely reflect real-world conditions, and 2) providing controllable adversarial behavior for more comprehensive and interactive evaluations. We validate our framework empirically using the nuScenes and nuPlan datasets across multiple planners, demonstrating improvements in both realism and controllability.
  arXiv  Detail & Related papers  (2023-12-31T04:14:43Z)
• RealGen: Retrieval Augmented Generation for Controllable Traffic Scenarios [58.62407014256686]
  RealGen is a novel retrieval-based in-context learning framework for traffic scenario generation. RealGen synthesizes new scenarios by combining behaviors from multiple retrieved examples in a gradient-free way. This in-context learning framework endows versatile generative capabilities, including the ability to edit scenarios.
  arXiv  Detail & Related papers  (2023-12-19T23:11:06Z)
• Empowering Autonomous Driving with Large Language Models: A Safety Perspective [82.90376711290808]
  This paper explores the integration of Large Language Models (LLMs) into Autonomous Driving systems. LLMs are intelligent decision-makers in behavioral planning, augmented with a safety verifier shield for contextual safety learning. We present two key studies in a simulated environment: an adaptive LLM-conditioned Model Predictive Control (MPC) and an LLM-enabled interactive behavior planning scheme with a state machine.
  arXiv  Detail & Related papers  (2023-11-28T03:13:09Z)
• Generating and Characterizing Scenarios for Safety Testing of Autonomous Vehicles [86.9067793493874]
  We propose efficient mechanisms to characterize and generate testing scenarios using a state-of-the-art driving simulator. We use our method to characterize real driving data from the Next Generation Simulation (NGSIM) project. We rank the scenarios by defining metrics based on the complexity of avoiding accidents and provide insights into how the AV could have minimized the probability of incurring an accident.
  arXiv  Detail & Related papers  (2021-03-12T17:00:23Z)

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.