DriveGen: Towards Infinite Diverse Traffic Scenarios with Large Models
Fuente:
arXiv
Saved in:
| Main Authors: | Zhang, Shenyu, Tian, Jiaguo, Zhu, Zhengbang, Huang, Shan, Yang, Jucheng, Zhang, Weinan |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
SocialDriveGen: Generating Diverse Traffic Scenarios with Controllable Social Interactions
by: Tian, Jiaguo, et al.
Published: (2025)
by: Tian, Jiaguo, et al.
Published: (2025)
Adversarial Safety-Critical Scenario Generation using Naturalistic Human Driving Priors
by: Hao, Kunkun, et al.
Published: (2024)
by: Hao, Kunkun, et al.
Published: (2024)
HAD-Gen: Human-like and Diverse Driving Behavior Modeling for Controllable Scenario Generation
by: Wang, Cheng, et al.
Published: (2025)
by: Wang, Cheng, et al.
Published: (2025)
Diffusion Models for Reinforcement Learning: A Survey
by: Zhu, Zhengbang, et al.
Published: (2023)
by: Zhu, Zhengbang, et al.
Published: (2023)
A Trajectory Generator for High-Density Traffic and Diverse Agent-Interaction Scenarios
by: Yang, Ruining, et al.
Published: (2025)
by: Yang, Ruining, et al.
Published: (2025)
DiffStitch: Boosting Offline Reinforcement Learning with Diffusion-based Trajectory Stitching
by: Li, Guanghe, et al.
Published: (2024)
by: Li, Guanghe, et al.
Published: (2024)
Distributional Soft Actor-Critic with Harmonic Gradient for Safe and Efficient Autonomous Driving in Multi-lane Scenarios
by: Zhang, Feihong, et al.
Published: (2025)
by: Zhang, Feihong, et al.
Published: (2025)
Fusing Driver Perceived and Physical Risk for Safety Critical Scenario Screening in Autonomous Driving
by: Xiong, Chen, et al.
Published: (2026)
by: Xiong, Chen, et al.
Published: (2026)
SEAL: Towards Safe Autonomous Driving via Skill-Enabled Adversary Learning for Closed-Loop Scenario Generation
by: Stoler, Benjamin, et al.
Published: (2024)
by: Stoler, Benjamin, et al.
Published: (2024)
Learning to Model Diverse Driving Behaviors in Highly Interactive Autonomous Driving Scenarios with Multi-Agent Reinforcement Learning
by: Weiwei, Liu, et al.
Published: (2024)
by: Weiwei, Liu, et al.
Published: (2024)
LD-Scene: LLM-Guided Diffusion for Controllable Generation of Adversarial Safety-Critical Driving Scenarios
by: Peng, Mingxing, et al.
Published: (2025)
by: Peng, Mingxing, et al.
Published: (2025)
Scenario-Based Hierarchical Reinforcement Learning for Automated Driving Decision Making
by: Abdelhamid, M. Youssef, et al.
Published: (2025)
by: Abdelhamid, M. Youssef, et al.
Published: (2025)
Seeking to Collide: Online Safety-Critical Scenario Generation for Autonomous Driving with Retrieval Augmented Large Language Models
by: Mei, Yuewen, et al.
Published: (2025)
by: Mei, Yuewen, et al.
Published: (2025)
AuthSim: Towards Authentic and Effective Safety-critical Scenario Generation for Autonomous Driving Tests
by: Yang, Yukuan, et al.
Published: (2025)
by: Yang, Yukuan, et al.
Published: (2025)
Causal Composition Diffusion Model for Closed-loop Traffic Generation
by: Lin, Haohong, et al.
Published: (2024)
by: Lin, Haohong, et al.
Published: (2024)
RoboGen: Towards Unleashing Infinite Data for Automated Robot Learning via Generative Simulation
by: Wang, Yufei, et al.
Published: (2023)
by: Wang, Yufei, et al.
Published: (2023)
Foundation Models in Autonomous Driving: A Survey on Scenario Generation and Scenario Analysis
by: Gao, Yuan, et al.
Published: (2025)
by: Gao, Yuan, et al.
Published: (2025)
DiffRoad: Realistic and Diverse Road Scenario Generation for Autonomous Vehicle Testing
by: Zhou, Junjie, et al.
Published: (2024)
by: Zhou, Junjie, et al.
Published: (2024)
An Uncertainty-Weighted Decision Transformer for Navigation in Dense, Complex Driving Scenarios
by: Zhang, Zhihao, et al.
Published: (2025)
by: Zhang, Zhihao, et al.
Published: (2025)
Enhancing Autonomous Vehicle Training with Language Model Integration and Critical Scenario Generation
by: Tian, Hanlin, et al.
Published: (2024)
by: Tian, Hanlin, et al.
Published: (2024)
Towards Benchmarking and Assessing the Safety and Robustness of Autonomous Driving on Safety-critical Scenarios
by: Li, Jingzheng, et al.
Published: (2025)
by: Li, Jingzheng, et al.
Published: (2025)
Unicorn: A Universal and Collaborative Reinforcement Learning Approach Towards Generalizable Network-Wide Traffic Signal Control
by: Zhang, Yifeng, et al.
Published: (2025)
by: Zhang, Yifeng, et al.
Published: (2025)
Unleashing the Potential of Diffusion Models for End-to-End Autonomous Driving
by: Zheng, Yinan, et al.
Published: (2026)
by: Zheng, Yinan, et al.
Published: (2026)
From Imitation to Exploration: End-to-end Autonomous Driving based on World Model
by: Li, Yueyuan, et al.
Published: (2024)
by: Li, Yueyuan, et al.
Published: (2024)
LORD: Large Models based Opposite Reward Design for Autonomous Driving
by: Ye, Xin, et al.
Published: (2024)
by: Ye, Xin, et al.
Published: (2024)
BIDA: A Bi-level Interaction Decision-making Algorithm for Autonomous Vehicles in Dynamic Traffic Scenarios
by: Yu, Liyang, et al.
Published: (2025)
by: Yu, Liyang, et al.
Published: (2025)
AffordGen: Generating Diverse Demonstrations for Generalizable Object Manipulation with Afford Correspondence
by: Zhang, Jiawei, et al.
Published: (2026)
by: Zhang, Jiawei, et al.
Published: (2026)
Large Multimodal Models for Embodied Intelligent Driving: The Next Frontier in Self-Driving?
by: Zhang, Long, et al.
Published: (2026)
by: Zhang, Long, et al.
Published: (2026)
Deep Reinforcement Learning for Advanced Longitudinal Control and Collision Avoidance in High-Risk Driving Scenarios
by: Chen, Dianwei, et al.
Published: (2024)
by: Chen, Dianwei, et al.
Published: (2024)
GenSim2: Scaling Robot Data Generation with Multi-modal and Reasoning LLMs
by: Hua, Pu, et al.
Published: (2024)
by: Hua, Pu, et al.
Published: (2024)
World Models for Autonomous Driving: An Initial Survey
by: Guan, Yanchen, et al.
Published: (2024)
by: Guan, Yanchen, et al.
Published: (2024)
BeamDojo: Learning Agile Humanoid Locomotion on Sparse Footholds
by: Wang, Huayi, et al.
Published: (2025)
by: Wang, Huayi, et al.
Published: (2025)
Embodiment-Aware Generalist Specialist Distillation for Unified Humanoid Whole-Body Control
by: Peng, Quanquan, et al.
Published: (2026)
by: Peng, Quanquan, et al.
Published: (2026)
DreamGen: Unlocking Generalization in Robot Learning through Video World Models
by: Jang, Joel, et al.
Published: (2025)
by: Jang, Joel, et al.
Published: (2025)
PEPA: a Persistently Autonomous Embodied Agent with Personalities
by: Liu, Kaige, et al.
Published: (2026)
by: Liu, Kaige, et al.
Published: (2026)
Automatic Curriculum Learning for Driving Scenarios: Towards Robust and Efficient Reinforcement Learning
by: Abouelazm, Ahmed, et al.
Published: (2025)
by: Abouelazm, Ahmed, et al.
Published: (2025)
LearningFlow: Automated Policy Learning Workflow for Urban Driving with Large Language Models
by: Peng, Zengqi, et al.
Published: (2025)
by: Peng, Zengqi, et al.
Published: (2025)
V2X-VLM: End-to-End V2X Cooperative Autonomous Driving Through Large Vision-Language Models
by: You, Junwei, et al.
Published: (2024)
by: You, Junwei, et al.
Published: (2024)
Persistent Autoregressive Mapping with Traffic Rules for Autonomous Driving
by: Liang, Shiyi, et al.
Published: (2025)
by: Liang, Shiyi, et al.
Published: (2025)
TorchDriveEnv: A Reinforcement Learning Benchmark for Autonomous Driving with Reactive, Realistic, and Diverse Non-Playable Characters
by: Lavington, Jonathan Wilder, et al.
Published: (2024)
by: Lavington, Jonathan Wilder, et al.
Published: (2024)
Similar Items
-
SocialDriveGen: Generating Diverse Traffic Scenarios with Controllable Social Interactions
by: Tian, Jiaguo, et al.
Published: (2025) -
Adversarial Safety-Critical Scenario Generation using Naturalistic Human Driving Priors
by: Hao, Kunkun, et al.
Published: (2024) -
HAD-Gen: Human-like and Diverse Driving Behavior Modeling for Controllable Scenario Generation
by: Wang, Cheng, et al.
Published: (2025) -
Diffusion Models for Reinforcement Learning: A Survey
by: Zhu, Zhengbang, et al.
Published: (2023) -
A Trajectory Generator for High-Density Traffic and Diverse Agent-Interaction Scenarios
by: Yang, Ruining, et al.
Published: (2025)