SceneGen: Learning to Generate Realistic Traffic Scenes
Open accessAbstract
We consider the problem of generating realistic traffic scenes automatically. Existing methods typically insert actors into the scene according to a set of hand-crafted heuristics and are limited in their ability to model the true complexity and diversity of real traffic scenes, thus inducing a content gap between synthesized traffic scenes versus real ones. As a result, existing simulators lack the fidelity necessary to train and test self-driving vehicles. To address this limitation, we present SceneGen—a neural autoregressive model of traffic scenes that eschews the need for rules and heuristics. In particular, given the ego-vehicle state and a high definition map of surrounding area, SceneGen inserts actors of various classes into the scene and synthesizes their sizes, orientations, and velocities. We demonstrate on two large-scale datasets SceneGen’s ability to faithfully model distributions of real traffic scenes. Moreover, we show that SceneGen coupled with sensor simulation can be used to train perception models that generalize to the real world.
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Generating large synthetic datasets has been a problem of great interest in other domains as well, and has also been used as a tool for benchmarking motion planning.
“Generating large synthetic datasets has been a problem of great interest in other domains as well [71-77], and has also been used as a tool for benchmarking motion planning [78].”
From MimicGen: A Data Generation System for Scalable Robot Learning using Human Demonstrations (2023) · cites this paper as [77]
Citation [78] is attached to the claim about benchmarking motion planning. The Introduction discusses using simulated traffic scenes to test self-driving vehicles generally, but the Sim2Real Evaluation (§4.4; page unavailable) describes generating scenes to train and evaluate a perception model. Searches for motion-planning benchmarks found no passage establishing that use. (LLM, conf=0.72)
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Cites
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Feng, L., Li, Q., Peng, Z., Tan, S., & Zhou, B. (2023) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48891.2023.10160296 · Cited by 116 · see citing works
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BackgroundMethodInfluentialSelf-citationSynthetic Datasets for Autonomous Driving: A Survey
Song, Z., He, Z., Li, X., Ma, Q., Ming, R., Mao, Z., Pei, H., Peng, L., Hu, J., Yao, D., et al. (2023) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2023.3331024 · Cited by 109 · see citing works
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BackgroundMethodV2XP-ASG: Generating Adversarial Scenes for Vehicle-to-Everything Perception
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Ding, W., Cao, Y., Zhao, D. X., Xiao, C., & Pavone, M. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73033-7_6 · Cited by 31 · see citing works
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1001 Ways of Scenario Generation for Testing of Self-driving Cars: A Survey
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BackgroundSelf-Supervised Real-to-Sim Scene Generation
Prakash, A., Debnath, S., Lafleche, J.-F., Cameracci, E., State, G., Birchfield, S. T., & Law, M. T. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01574 · Cited by 23 · see citing works
“SceneGen [52] learns to generate traffic scenes by modeling the attributes of all objects.”
BackgroundMethodGINA-3D: Learning to Generate Implicit Neural Assets in the Wild
Shen, B., Yan, X., Qi, C. R., Najibi, M., Deng, B., Guibas, L. J., Zhou, Y., & Anguelov, D. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.00476 · Cited by 19 · see citing works
“Many recent works explored different aspects of data-driven simulation, including image synthesis [114–117], assets modeling [47,48,118–121], scene generation [49,122,123], and scene rendering [1,124–126].”
BackgroundA Survey of Scenario Generation for Automated Vehicle Testing and Validation
Wang, Z., Ma, J., & Lai, E. M.-K. (2024) · Future Internet · doi:10.3390/fi16120480 · Cited by 18 · see citing works
Solving Motion Planning Tasks with a Scalable Generative Model
Hu, Y., Chai, S., Yang, Z., Qian, J., Li, K., Shao, W., Zhang, H., Xu, W., & Liu, Q. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72946-1_22 · Cited by 17 · see citing works
LidarDM: Generative LiDAR Simulation in a Generated World
Zyrianov, V., Che, H., Liu, Z., & Wang, S. (2025) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra55743.2025.11128001 · Cited by 15 · see citing works
“More recently, they have shown great promise in robotics by generating realistic scenarios and sensory data for training and validating embodied agents, significantly reducing the cost of real-world experiments [12], [13], [14], [15], [16], [17], [18], [19], [20], [21], [22], [23].”
Self-citationDriveSceneGen: Generating Diverse and Realistic Driving Scenarios From Scratch
Sun, S., Gu, Z., Sun, T., Sun, J., Yuan, C.-M., Han, Y., Li, D., & Ang, M. H. (2024) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2024.3416792 · Cited by 15 · see citing works
“Works including [16]–[18] focused on sampling agent states from existing scenarios, then erasing and respawning the agents based on agent state distribution.”
BackgroundSceneControl: Diffusion for Controllable Traffic Scene Generation
Lu, J., Wong, K., Zhang, W., et al. (2024) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra57147.2024.10610324 · Cited by 14 · see citing works
“Most also focus on generating scenes unconditionally [2], [37], [29], [8].”
BackgroundMethodInfluentialSelf-citationLong Short Term Memory Based Traffic Prediction Using Multi-Source Data
Leinonen, M., Al-Tachmeesschi, A., Turkmen, B., Atashi, N., & Ruotsalainen, L. (2024) · International Journal of Intelligent Transportation Systems Research · doi:10.1007/s13177-024-00451-y · Cited by 14 · see citing works
“On the other hand, microscopic traffic simulations aim to simulate the behaviour of each individual vehicle [11].”
Liu, X., Huang, H., Bian, J., Zhou, R., Wei, Z., & Zhou, H. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.111995 · Cited by 13 · see citing works
RSG-GCN: Predicting Semantic Relationships in Urban Traffic Scene With Map Geometric Prior
Tian, Y., Carballo, A., Li, R., & Takeda, K. (2023) · IEEE Open Journal of Intelligent Transportation Systems · doi:10.1109/ojits.2023.3260624 · Cited by 14 · see citing works
“…parameterize actor node α i ∈ A with an eight-dimensional vector containing its class label c i ∈ C , its position according to the ego-centric bird’s-eye-view coordinates ( x i , y i ) ∈ R 2 , its bounding box b i ∈ B , and its velocity ( v ix , v iy ) ∈ R 2 , based on the method proposed in [44].”
MethodInfluentialGOOSE: Goal-Conditioned Reinforcement Learning for Safety-Critical Scenario Generation
Ransiek, J., Plaum, J., Langner, J., et al. (2024) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc58415.2024.10919498 · Cited by 11 · see citing works
“[22] propose a neural autoregressive model to insert actors of different classes into traffic scenes.”
Text-to-Drive: Diverse Driving Behavior Synthesis via Large Language Models
Nguyen, P., Wang, T.-H. J., Hong, Z.-W., Karaman, S., & Rus, D. L. (2024) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros58592.2024.10801754 · Cited by 10 · see citing works
“Then, to evaluate the alignment of driving policies with desired behaviors, we introduce the primary reward function (subsection III-D), which, together with our auxiliary function, provides reward guidance to the driving policy (subsection III-E).”
Zhang, G., Huang, H., Zhou, R., et al. (2025) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2025.3576236 · Cited by 10 · see citing works
“Tan et al. modeled trajectory features using LSTM and compared the effectiveness of GAN and VAE for synthesizing trajectories [41].”
Lift3D: Synthesize 3D Training Data by Lifting 2D GAN to 3D Generative Radiance Field
Li, L., Lian, Q., Wang, L., Ma, N., & Chen, Y.-C. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.00040 · Cited by 10 · see citing works
“Based on the proposed framework, we can enhance original datasets both in quantity and diversity.”
SYGNet: A SVD-YOLO based GhostNet for Real-time Driving Scene Parsing
Wang, H., Zhu, B., Li, Y., et al. (2022) · Proceedings - International Conference on Image Processing · doi:10.1109/icip46576.2022.9897534 · Cited by 9 · see citing works
“Autonomous driving field consists of scenario generation [1,2], scene recognition [3,4], navigation [5], simulation [6], scene parsing [7–9], prediction [10, 11] and motion estimation [12, 13].”
BackgroundSelf-citationSemantically Adversarial Scene Generation With Explicit Knowledge Guidance
Ding, W., Lin, H., Li, B., & Zhao, D. X. (2025) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2024.3510515 · Cited by 16 · see citing works
Quantile-based scenario generation for automated vehicle safety evaluation
Zhou, H., Ma, C., Ma, K., et al. (2025) · Accident Analysis & Prevention · doi:10.1016/j.aap.2025.108043 · Cited by 9 · see citing works
Sim-on-Wheels: Physical World in the Loop Simulation for Self-Driving
Shen, Y., Chandaka, B., Lin, Z.-H., Zhai, A. J., Cui, H., Forsyth, D. A., & Wang, S. (2023) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2023.3325689 · Cited by 9 · see citing works
“data-driven simulation in [11], [12], [14], [15]; dynamic models in [33], [34], [35] ; environments in [13], [36], [37], [38]).”
Self-citationDreamCar: Leveraging Car-Specific Prior for In-the-Wild 3D Car Reconstruction
Du, X., Sun, H., Lu, M., Zhu, T., & Yu, X. (2024) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2024.3523231 · Cited by 7 · see citing works
“They must deal with infrequent dangerous situations, but collecting such dangerous data is challenging in the real-world setting [1].”
BackgroundHAD-Gen: Human-like and diverse driving behavior modeling for controllable scenario generation
Wang, C., Kong, L., Tamborski, M., et al. (2025) · Accident Analysis & Prevention · doi:10.1016/j.aap.2025.108270 · Cited by 7 · see citing works
“An example is the SceneGen proposed by Tan et al. [33], which aims to generate realistic traffic scenes by learning from large-scale real-world datasets.”
LLM-Powered Synthetic Environments for Self-Driving Scenarios
Adekanye, O. A. M. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i21.30540 · Cited by 7 · see citing works
EYOLOX: An Efficient One-Stage Object Detection Network Based on YOLOX
Tang, R., Sun, H., Liu, D., Xu, H. F., Qi, M., & Kong, J. (2023) · Applied Sciences · doi:10.3390/app13031506 · Cited by 7 · see citing works
MJTG: A Multi-Vehicle Joint Trajectory Generator for Complex and Rare Scenarios
Tian, Y., Zheng, W., Shao, Y., et al. (2025) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2025.3567636 · Cited by 6 · see citing works
“Tan, et al. generated realistic scenarios by inputting vehicle states and map information into a Long Short Term Memory network (LSTM) [43].”
Realistic Corner Case Generation for Autonomous Vehicles with Multimodal Large Language Model
Lu, Q., Ma, M., Wang, Z., Lu, W., Ma, M., Zhao, G., & Feng, S. (2025) · Tsinghua Science & Technology · doi:10.26599/tst.2025.9010178 · Cited by 6 · see citing works
Mei, Y., Nie, T., Sun, J., et al. (2025) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc60802.2025.11423790 · Cited by 6 · see citing works
“To explicitly target high-risk cases, some methods formulate scenario generation as a search or optimization problem.”
Scalable Modular Synthetic Data Generation for Advancing Aerial Autonomy
Sabet, M., Palanisamy, P., & Mishra, S. (2022) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2023.104464 · Cited by 6 · see citing works
“To support DR through the manual procedural model to generate realistic and diverse scenes requires significant time and expertise to tune the parameters [14, 18, 19, 22].”
BackgroundMethodReal-to-Synthetic: Generating Simulator Friendly Traffic Scenes from Graph Representation
Tian, Y., Carballo, A., Li, R., & Takeda, K. (2022) · IEEE Intelligent Vehicles Symposium · doi:10.1109/iv51971.2022.9827441 · Cited by 6 · see citing works
MimicGen: A Data Generation System for Scalable Robot Learning using Human Demonstrations
Mandlekar, A., Nasiriany, S., Wen, B., Akinola, I., Narang, Y., Fan, L., Zhu, Y., & Fox, D. (2023) · arXiv · doi:10.48550/arxiv.2310.17596 · Cited by 5 · see citing works
Knowledge guided controllable diffusion for enhanced autonomous driving scenarios generation
Shan, C., Guo, L., & Chen, H. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.130616 · Cited by 5 · see citing works
Realistic Urban Traffic Generator Using Decentralized Federated Learning for the SUMO Simulator
Bazán-Guillén, A., Beis-Penedo, C., Cajaraville-Aboy, D., Bautista, P. B., Redondo, R. P. D., Llopis, L. J. D. L. C., Vilas, A. F., Igartua, M. A., & Fernández‐Veiga, M. (2025) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2025.3597019 · Cited by 5 · see citing works
“Similarly, SceneGen [29] employs auto-regressive neural networks to generate realistic traffic scenarios without relying on rigid rule-based systems, offering a scalable approach to simulate behaviorally rich urban environments for autonomous vehicles.”
AGENTS-LLM: Augmentative GENeration of Challenging Traffic Scenarios with an Agentic LLM Framework
Yao, Y., Bhatnagar, S., Mazzola, M., et al. (2025) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros60139.2025.11246348 · Cited by 5 · see citing works
“However, they often lack fine-grained control over the output and generating novel scenarios [7], [8] from scratch can introduce a distributional shift from the original training scenes which undermines the validity of evaluations especially for learning-based planners.”
DragTraffic: Interactive and Controllable Traffic Scene Generation for Autonomous Driving
Wang, S., Sun, G., Ma, F., Hu, T., Qin, Q., Song, Y., Zhu, L., & Liang, J. (2024) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros58592.2024.10801623 · Cited by 5 · see citing works
“Other researchers have looked at generating challenging scenes in a more flexible way, such as SceneGen [7] and TrafficGen [8], which pro-pose building scenes in two stages: vehicle placement and trajectory generation.”
Self-citationMahjourian, R., Mu, R., Likhosherstov, V., Mougin, P., Huang, X., Messias, J. V., & Whiteson, S. (2024) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra57147.2024.10611115 · Cited by 6 · see citing works
“Most learning-based scene generation methods mainly generate the initial states for the agents [5], [6].”
BackgroundMethodHuang, P., Ding, W., Stoler, B., et al. (2025) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra55743.2025.11127319 · Cited by 5 · see citing works
WorldGen: A Large Scale Generative Simulator
Singh, C. D., Kumari, R., Fermuller, C. M., Sanket, N. J., & Aloimonos, Y. (2023) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48891.2023.10160861 · Cited by 5 · see citing works
“Since WorldGen has the ability to export these parameters, neural autoregressive models such as [58] can be deployed for realistic traffic distribution.”
Sheng, Z., Huang, Z., Qu, Y., Leng, Y., & Chen, S. (2025) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw67362.2025.00364 · Cited by 4 · see citing works
“For instance, SceneGen [36] introduces a neural autoregressive model that sequentially generates the initial states of traffic agents to compose a static scene.”
Modelling Competitive Behaviors in Autonomous Driving Under Generative World Model
Qiao, G., Quan, G., Qu, R., & Liu, G. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72761-0_2 · Cited by 4 · see citing works
“To improve realism, learning-based approaches [21,39] predict or simulate traffic behavior by learning from a realistic traffic dataset.”
Vectorized Scenario Description and Motion Prediction for Scenario-Based Testing
Winkelmann, M., Vasconi, C., & Müller, S. (2023) · IEEE Intelligent Vehicles Symposium · doi:10.1109/iv55152.2023.10186626 · Cited by 4 · see citing works
Learning from Demonstrations of Critical Driving Behaviours Using Driver's Risk Field
Du, Y., Acerbo, F. S., Kober, J., et al. (2023) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2023.10.1376 · Cited by 4 · see citing works
Continuous Locomotive Crowd Behavior Generation
Bae, I., Lee, J., & Jeon, H. (2025) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52734.2025.02088 · Cited by 3 · see citing works
“Recent advances in generative models have enabled the synthesis of realistic traffic scenes [30, 60, 93, 161, 162].”
Robust Fast Adaptation from Adversarially Explicit Task Distribution Generation
Wang, Q., Lv, Y., Mao, Y., Qu, Y. P., Xu, Y., & Ji, X. (2025) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3690624.3709337 · Cited by 4 · see citing works
Deep Learning Approach for Realistic Traffic Video Changes Across Lighting and Weather Conditions
Alam, M. S., Parmar, S. H., Martens, M., & Bazilinskyy, P. (2025) · International Conference on Information and Computer Technologies (ICICT) · doi:10.1109/icict64582.2025.00034 · Cited by 3 · see citing works
“Similarly, Tan et al. [21] attempted to create realistic scenes using GANs.”
Enhancing Autonomous Driving Safety with Collision Scenario Integration
Wang, Z., Lan, S., Sun, X., Chang, N., Li, Z., Yu, Z., & Álvarez, J. M. (2025) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros60139.2025.11246713 · Cited by 5 · see citing works
“SceneGen [39] uses auto-regressive models for scenario generation, while TrafficGen [23] applies modules for agent initialization and motion prediction.”
Safety-Critical Traffic Simulation with Adversarial Transfer of Driving Intentions
Huang, Z., Gao, X., Zheng, G., Wen, L., Yang, X., & Sun, X. L. (2025) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra55743.2025.11128569 · Cited by 2 · see citing works
“Some pi-oneering research [11]–[13] employs end-to-end deep neural networks, like GNNs [14] and Transformer [15], to learn to capture complex interactions in traffic scenes and generate realistic agent trajectories from large-scale real-world data.”
Testing Scenario Identification for Automated Vehicles Based on Deep Unsupervised Learning
Liu, S., Ren, F., Li, P., et al. (2023) · World Electric Vehicle Journal · doi:10.3390/wevj14080208 · Cited by 2 · see citing works
Meng, Q., Zhang, L., Zhao, C., et al. (2024) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2024.3385792 · Cited by 2 · see citing works
Eroglu, S., Voigt, A., Weyers, B., et al. (2024) · IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW) · doi:10.1109/vrw62533.2024.00040 · Cited by 2 · see citing works
“Real public data sources [12,15,26], procedural generation [28,34], learning-based [13,21,33] or user-driven [6,18,38] methods are used to address these needs.”
MethodTowards Interactive Autonomous Vehicle Testing: Vehicle-Under-Test-Centered Traffic Simulation
Liu, Y., Zhao, X., & Sun, J. (2024) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc58415.2024.10920277 · Cited by 2 · see citing works
“Deep learning (DL) models emerge as a viable methodology in addressing these challenges, which can incorporate implicit interactive relationships among traffic participants [4], [16]–[18].”
AutoSceCraft: Generate Various Driving Scenarios from Scratch for Autonomous Driving Systems
Lan, W., Liu, J., Yuan, B., & Yao, X. S. (2025) · Tsinghua Science & Technology · doi:10.26599/tst.2025.9010045 · Cited by 2 · see citing works
Semantically Controllable Scene Generation with Guidance of Explicit Knowledge.Preprint
Ding, W., Li, B., Eun, K. J., et al. (2021) · arXiv (Cornell University) · Cited by 2 · see citing works
CausalAF: Causal Autoregressive Flow for Goal-Directed Safety-Critical Scenes Generation.Preprint
Ding, W., Lin, H., Li, B., et al. (2021) · arXiv (Cornell University) · Cited by 2 · see citing works
Self-driving simulation scene reconstruction using self-supervised depth completion
Zhang, Y., Zheng, X., & Liang, H. (2022) · International Conference on Automation, Control and Robotics Engineering (CACRE) · doi:10.1109/cacre54574.2022.9834190 · Cited by 1 · see citing works
Li, Y., Hou, Z., & Liu, H. (2024) · IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) · doi:10.1109/trustcom63139.2024.00243 · Cited by 1 · see citing works
“For example, [7] utilizes LSTM models to generate vehicle trajectories.”
SDOA: A Synthetic Dataset for Occlusion-Aware and Amodal Perception in Autonomous Driving
Xia, Y. (2025) · International Conference on Advanced Algorithms and Control Engineering (ICAACE) · doi:10.1109/icaace65325.2025.11019488 · Cited by 1 · see citing works
“Recent research integrates machine learning and rule-based methods to automate scene generation [8]–[10], but challenges remain in controllability and interpretability.”
Chen, Y., Jiang, H., & Sun, T. (2025) · Symmetry · doi:10.3390/sym17091503 · Cited by 1 · see citing works
“Considering that the GAN and VAE struggle with the problem of temporal interaction, Tan [79] used an autoregressive model to characterize the dependence features between historical data, and by recursively learning the conditional probability of historical trajectories, Tan generated hazard…”
SceneDM: Consistent Diffusion Models for Coherent Multi-agent Trajectory Generation
Guo, Z., Gao, X., Zhou, J.-L., et al. (2025) · 2022 IEEE 18th International Conference on Automation Science and Engineering (CASE) · doi:10.1109/case58245.2025.11163744 · Cited by 1 · see citing works
SAGA: Generating Extreme Scenarios for Autonomous Driving via Adversarial Perturbations
Wang, Y., Wu, Q., Shi, X., Li, Y. X., & Li, K. (2025) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2025.3640578 · Cited by 1 · see citing works
“Recent learning-based approaches, including SceneGen [14], Tra ffi cSim [4], Tra ffi cGen [17], and Di ff Scene [18], excel at capturing realistic tra ffi c behaviors from real-world data.”
STGT-Gen: Spatio-Temporal Graph Transformer for Multi-Vehicle Traffic Scenario Generation
Qin, X., Lu, C., Wei, Y., Fan, S., Song, Z., & Gong, J. (2025) · SAE technical papers on CD-ROM/SAE technical paper series · doi:10.4271/2025-01-7316 · Cited by 1 · see citing works
Adversarial and Reactive Traffic Entities for Behavior-Realistic Driving Simulation: A Review
Ransiek, J., Reis, P., Schürmann, T., et al. (2025) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc58881.2025.11342478 · Cited by 1 · see citing works
“…[92], [99], [100], [101] Scene-centric [6], [13], [67], [69], [70], [71], [73], [78], [79], [80], [81] Scene-centric Scene-centric [14], [102], [103], [104], [105], [106], [107] sampling framework for controlled trajectory generation, while [83] uses a conditional diffusion model for…”
IntersectioNDE: Learning Complex Urban Traffic Dynamics Based on Interaction Decoupling Strategy
Lin, E., Yang, Z., Lu, Q., et al. (2025) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc60802.2025.11423034 · Cited by 1 · see citing works
“From the perspective of generative model architecture, this includes deep Autoregressive Models [22], [23], Variational Autoen-coders (VAE) [24], [25], Generative Adversarial Networks (GAN) [26], [27], and Diffusion Models [28]–[30].”
ARISE – Adaptive Refinement and Iterative Scenario Engineering
Poddubnyy, K., Vozniak, I., Бурмистров, И., et al. (2026) · IEEE Intelligent Vehicles Symposium · doi:10.1109/iv66570.2026.11623826 · Cited by 1 · see citing works
“SceneGen [17] learns to sample realistic but abstract traffic scenes from real data, and TrafficGen [18] applies a diffusion model to generate diverse traffic configurations conditioned on map context.”
NeuralFloors: Conditional Street-Level Scene Generation From BEV Semantic Maps via Neural Fields
Muşat, V., Martini, D. D., Gadd, M., et al. (2024) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2024.3356793 · Cited by 1 · see citing works
“As a result, research in this area has gained popularity in both academia [1] and industry, where there has been much focus on building digital twins endowed with the diversity and high fidelity of the real world they are emulating.”
BackgroundScene-Extrapolation: Generating Interactive Traffic Scenarios
Zipfl, M., Schütt, B., & Zöllner, J. M. (2024) · IEEE Intelligent Vehicles Symposium · doi:10.1109/iv55156.2024.10588703 · Cited by 1 · see citing works
“Tan et al. propose an autoregressive approach, SceneGen [15], to address the limitations of manual and rule-based methods in representing weld complexity.”
MethodTian, H., Wang, X., & Ma, X. (2024) · International Conference on Electronic Information Engineering, Big Data and Computer Technology (EIBDCT) · doi:10.1117/12.3031014 · Cited by 1 · see citing works
“SimNet [6] enhances the generation of diverse simulations by initializing states through a generative model conditioned on semantic maps.”
A Survey of Learning Techniques for Virtual Scene Generation
Ayyildiz, D. V., Alnaser, A. J., Taj, S., et al. (2024) · SAE International Journal of Connected and Automated Vehicles · doi:10.4271/12-08-02-0017 · Cited by 1 · see citing works
Abouelazm, A., Weinstein, T., Joseph, T., Schörner, P., & Zöllner, J. M. (2025) · IEEE Intelligent Vehicles Symposium · doi:10.1109/iv64158.2025.11097458 · Cited by 1 · see citing works
“Restricting nodes to valid road positions [37], [38] ensures that all generated scenarios remain feasible by enabling a proper placement of road-bound NPCs, such as vehicles and motorcycles, as well as roadside obstacles like parked cars or trash containers, without requiring additional masking.”
A Modular Co-Simulation Framework with Open Source Software and Automotive Standards
Salles, D., Lang, L., Kehrer, M., & Reuss, H.-C. (2022) · Proceedings · doi:10.1007/978-3-658-37011-4_18 · Cited by 1 · see citing works
A map-based model-driven testing framework for automated driving systems
Tang, Y. (2022) · doi:10.32657/10356/164257 · Cited by 1 · see citing works
Semantically Controllable Generation of Physical Scenes with Explicit Knowledge.Preprint
Ding, W., Li, B., Eun, K. J., et al. (2021) · arXiv (Cornell University)
Accident Scenario Generation using Driver Behavior Model
Jawad, A., & Whitehead, J. (2024) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc58415.2024.10920274
“Data-driven methods utilize techniques such as direct sampling [34], [6] and density estimation [35], [5], [36], [37], [38], [39] to generate scenarios by re-simulating, perturbing, or sampling from distributions learned from collected datasets.”
Riegl, P., Sekaran, K. C., & Botsch, M. (2024) · IEEE International Conference on Vehicular Electronics and Safety (ICVES) · doi:10.1109/icves61986.2024.10927934
“The works in [13] and [14] use an autoregressive model to generate starting configurations of traffic scenarios.”
Cao, M., Duan, W., Huo, C., et al. (2025) · AIP Advances · doi:10.1063/5.0272210
The Application of Generative Adversarial Network on Data Augmentation for Target Selection
Liu, Y., Wang, Z., Xu, Q., et al. (2025) · International Journal of Automotive Technology · doi:10.1007/s12239-025-00261-5
“Tan et al. (2021) generated realistic traffic scenes without relying on heuristics, accurately modeling real-world distributions.”
HiFiSim:Learning for High-Frequency Traffic Scenario Simulation with Dual-Domain FidelityPreprint
Su, Q., Tang, X., Chen, Z., et al. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5472614
Mammen, M. M., Kayatas, Z., & Bestle, D. (2025) · Future Transportation · doi:10.3390/futuretransp5040159
“To overcome these limitations, recent research turned to deep generative models for data-driven scenario generation, which learn directly from real-world or simulated data [13–21].”
Xie, D., Li, D., Zhang, Y., et al. (2025) · SAE technical papers on CD-ROM/SAE technical paper series · doi:10.4271/2025-99-0133
Benchmarking Generative Models for Traffic Scene Image Synthesis A Comparative Study
Geng, Y., Chen, L., Qiu, B., et al. (2025) · International Conference on Algorithms, Computing and Artificial Intelligence (ACAI) · doi:10.1109/acai68217.2025.11406369
“…images[19]; • Physical implausibility: Unrealistic vehicle interactions (e.g., overlapping cars or floating pedestrians) degrade simulation utility[3]; • Context misunderstanding: Models often fail to interpret specialized terms (e.g., "roundabout right-of-way" or "zebra crossing")[3][12].”
Path Diffuser: Diffusion Model for Data-Driven Traffic Simulator
Lee, D., Karthikeyan, A., Pant, Y. V., & Fischmeister, S. (2025) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc60802.2025.11423013
“SceneControl outperforms popular autoregressive baselines such as those presented in [22].”
Simulation Module of Autonomous Driving
Huang, Y., & Yang, Z. (2026) · Springer eBooks · doi:10.1007/978-981-95-3725-9_10
HABIT: Human Action Benchmark for Interactive Traffic in CARLA
Ramesh, M., Azer, M., & Flohr, F. B. (2026) · IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv61042.2026.00690
“Although they emphasize addressing both appearance and content gaps, particularly in actor behavior generation, the focus has predominantly been on vehicle traffic and sensor realism, not pedestrian-driven dynamics [11].”
Large Language Model-Based Realistic Safety-Critical Driving Video Generation
Fu, Y., Zha, R., Tian, P., & Di, X. (2026) · Transportation Research Record Journal of the Transportation Research Board · doi:10.1177/03611981261445426
Zhou, R., Li, S. W., Liu, X., Huang, H., Bian, J., & Wei, Z. (2026) · Transportmetrica A Transport Science · doi:10.1080/23249935.2026.2694539
- Authors
- Tan, S., Wong, K., Wang, S., Manivasagam, S., Ren, M., & Urtasun, R.
- Affiliations
- Sun Yat-sen University (CN)
- University of Toronto (CA)
- Venue
- 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2021-06
- Pages
- 892-901
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W3173280621
- Semantic Scholar
- 8b817a28d880e4688956d6e1769475a9f22c7411
- Cited by
- 104
- References
- 64
- Citations by year
- 2026: 10 · 2025: 36 · 2024: 29 · 2023: 17 · 2022: 8 · 2021: 4
- FWCI
- 14.34
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by)
- arXiv (Cornell University) (cc-by)
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
- Physical Sciences › Engineering › Control and Systems Engineering › Simulation and Modeling Applications
- Keywords
- autonomous driving simulation, high-definition maps, sensor simulation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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