LLM-based Human-like Traffic Simulation for Self-driving Tests

• URL: http://arxiv.org/abs/2508.16962v1
• Date: Sat, 23 Aug 2025 09:30:55 GMT
• Title: LLM-based Human-like Traffic Simulation for Self-driving Tests
• Authors: Wendi Li, Hao Wu, Han Gao, Bing Mao, Fengyuan Xu, Sheng Zhong,
• Abstract summary: We introduce HDSim, an HD traffic generation framework that combines cognitive theory with large language model (LLM) assistance to produce realistic traffic scenarios.<n>Experiments reveal that embedding HDSim into simulation improves detection of safety-critical failures in self-driving systems by up to 68% and yields realism-consistent accident interpretability.
• Score: 16.752944112972667
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Ensuring realistic traffic dynamics is a prerequisite for simulation platforms to evaluate the reliability of self-driving systems before deployment in the real world. Because most road users are human drivers, reproducing their diverse behaviors within simulators is vital. Existing solutions, however, typically rely on either handcrafted heuristics or narrow data-driven models, which capture only fragments of real driving behaviors and offer limited driving style diversity and interpretability. To address this gap, we introduce HDSim, an HD traffic generation framework that combines cognitive theory with large language model (LLM) assistance to produce scalable and realistic traffic scenarios within simulation platforms. The framework advances the state of the art in two ways: (i) it introduces a hierarchical driver model that represents diverse driving style traits, and (ii) it develops a Perception-Mediated Behavior Influence strategy, where LLMs guide perception to indirectly shape driver actions. Experiments reveal that embedding HDSim into simulation improves detection of safety-critical failures in self-driving systems by up to 68% and yields realism-consistent accident interpretability.

Related papers

• Safety-Critical Traffic Simulation with Guided Latent Diffusion Model [8.011306318131458]
  Safety-critical traffic simulation plays a crucial role in evaluating autonomous driving systems.<n>We propose a guided latent diffusion model (LDM) capable of generating physically realistic and adversarial scenarios.<n>Our work provides an effective tool for realistic safety-critical scenario simulation, paving the way for more robust evaluation of autonomous driving systems.
  arXiv  Detail & Related papers  (2025-05-01T13:33:34Z)
• Adversarial Safety-Critical Scenario Generation using Naturalistic Human Driving Priors [2.773055342671194]
  We introduce a natural adversarial scenario generation solution using naturalistic human driving priors and reinforcement learning techniques. Our findings demonstrate that the proposed model can generate realistic safety-critical test scenarios covering both naturalness and adversariality.
  arXiv  Detail & Related papers  (2024-08-06T13:58:56Z)
• 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)
• Reinforcement Learning with Human Feedback for Realistic Traffic Simulation [53.85002640149283]
  Key element of effective simulation is the incorporation of realistic traffic models that align with human knowledge. This study identifies two main challenges: capturing the nuances of human preferences on realism and the unification of diverse traffic simulation models.
  arXiv  Detail & Related papers  (2023-09-01T19:29:53Z)
• TrafficBots: Towards World Models for Autonomous Driving Simulation and Motion Prediction [149.5716746789134]
  We show data-driven traffic simulation can be formulated as a world model. We present TrafficBots, a multi-agent policy built upon motion prediction and end-to-end driving. Experiments on the open motion dataset show TrafficBots can simulate realistic multi-agent behaviors.
  arXiv  Detail & Related papers  (2023-03-07T18:28:41Z)
• 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)
• BITS: Bi-level Imitation for Traffic Simulation [38.28736985320897]
  We take a data-driven approach and propose a method that can learn to generate traffic behaviors from real-world driving logs. We empirically validate our method, named Bi-level Imitation for Traffic Simulation (BITS), with scenarios from two large-scale driving datasets. As part of our core contributions, we develop and open source a software tool that unifies data formats across different driving datasets.
  arXiv  Detail & Related papers  (2022-08-26T02:17:54Z)
• Augmented Driver Behavior Models for High-Fidelity Simulation Study of Crash Detection Algorithms [2.064612766965483]
  We present a simulation platform for a hybrid transportation system that includes both human-driven and automated vehicles. We decompose the human driving task and offer a modular approach to simulating a large-scale traffic scenario. We analyze a large driving dataset to extract expressive parameters that would best describe different driving characteristics.
  arXiv  Detail & Related papers  (2022-08-10T19:59:16Z)
• Tackling Real-World Autonomous Driving using Deep Reinforcement Learning [63.3756530844707]
  In this work, we propose a model-free Deep Reinforcement Learning Planner training a neural network that predicts acceleration and steering angle. In order to deploy the system on board the real self-driving car, we also develop a module represented by a tiny neural network.
  arXiv  Detail & Related papers  (2022-07-05T16:33:20Z)
• TrafficSim: Learning to Simulate Realistic Multi-Agent Behaviors [74.67698916175614]
  We propose TrafficSim, a multi-agent behavior model for realistic traffic simulation. In particular, we leverage an implicit latent variable model to parameterize a joint actor policy. We show TrafficSim generates significantly more realistic and diverse traffic scenarios as compared to a diverse set of baselines.
  arXiv  Detail & Related papers  (2021-01-17T00:29:30Z)

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.