Please wait a minute...
浙江大学学报(工学版)
Journal of ZheJiang University (Engineering Science)  2026, Vol. 60 Issue (3): 513-526    DOI: 10.3785/j.issn.1008-973X.2026.03.007
    
Current status and future prospect of integrated simulation platform for autonomous driving
Juntao LV1(),Jueyu QI1,Haochen YU1,Lei MA2,Huimin MA1,Tianyu HU1,*()
1. School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China
2. College of Future Technology, Peking University, Beijing 100080, China
Download: HTML     PDF(3261KB) HTML
Export: BibTeX | EndNote (RIS)      

Abstract  

Autonomous driving simulation platforms play a vital role in the development, testing and validation of autonomous driving systems. A systematic review of the classification and key technical pathways of mainstream simulation platforms was presented, covering aspects such as environment modeling, sensor simulation, vehicle dynamics modeling, perception algorithm evaluation, V2X communication and cloud-based simulation. Core challenges and research progress related to synthetic data generation, cost-effective algorithm training, cross-domain generalization and platform scalability were analyzed, emphasizing simulation technologies based on computer vision and artificial intelligence. Future development trends were discussed. Simulation platforms are evolving toward higher realism, interactivity and closed-loop validation with the advancement of emerging technologies such as generative AI, neural rendering and multimodal learning, gradually forming a comprehensive pipeline that integrates data generation, algorithm training and performance evaluation. Simulation platforms will continue to play an essential role in enhancing the generalization capability of autonomous driving system, accelerating product deployment, and establishing standard testing and validation framework in the future.

Key words： autonomous driving      computer vision      algorithm testing      data generation     
Received: 14 July 2025      Published: 04 February 2026
CLC:  TP 393  
Fund:  国家自然科学基金资助项目(62172036); 科技创新2030-“新一代人工智能”重大资助项目(2022ZD0116305).
Corresponding Authors: Tianyu HU     E-mail: 17721026029@163.com;tianyu@ustb.edu.cn
Service
E-mail this article
Add to my bookshelf
Add to citation manager
E-mail Alert
RSS
Articles by authors
Juntao LV
Jueyu QI
Haochen YU
Lei MA
Huimin MA
Tianyu HU
Cite this article:

Juntao LV,Jueyu QI,Haochen YU,Lei MA,Huimin MA,Tianyu HU. Current status and future prospect of integrated simulation platform for autonomous driving. Journal of ZheJiang University (Engineering Science), 2026, 60(3): 513-526.

URL:

https://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2026.03.007     OR     https://www.zjujournals.com/eng/Y2026/V60/I3/513


自动驾驶综合仿真平台的现状与展望

自动驾驶仿真平台在自动驾驶系统的研发、测试与验证过程中发挥着至关重要的作用. 系统综述了当前主流自动驾驶仿真平台的分类与关键技术路径, 包括环境建模、传感器模拟、车辆动力学建模、感知算法测试、V2X通信、云仿真等方面, 重点分析合成数据生成、低成本算法训练、跨域迁移能力、平台可扩展性等核心挑战与研究进展, 分析具体的基于计算机视觉和人工智能的自动驾驶仿真技术，展望了该技术的发展趋势. 随着生成式AI、神经渲染、多模态学习等新兴技术的发展, 仿真平台正向高真实感、可交互、闭环式验证方向演进, 逐步形成数据生成、算法训练、性能评估的全流程闭环. 未来, 仿真平台将在提升自动驾驶系统的泛化能力、加速产品部署进程以及构建标准化测试验证体系等方面持续发挥支撑作用.


关键词： 自动驾驶,  计算机视觉,  算法测试,  数据生成 
Fig.1 Development history of generative model and AIGC
Fig.2 Relationship between simulation platform and component in ADS mission
Fig.3 Fundamental process of autonomous driving simulation platform
Fig.4 Interface of Carla-Apollo Bridge[55]
Fig.5 Relationship between existing autonomous driving simulation platform and technology
平台开源状况建模技术接口特点
CarSim闭源VehicleSim SolversMATLAB, Simulink, Python, C++支持多种车型的建模, 扩展性和兼容性强, 于1996年发布
Carmaker闭源UnigineMATLAB, Simulink, Python, C++支持 27自由度多体动力学模型及多种极端情况模拟, 于1999年发布, 目前已更新至14.0
Prescan闭源Unreal EngineMATLAB, Simulink支持与多方第三款软件集成, 精准呈现驾驶员的操控, 2002年由TASS发布, 后被Siemens收购, 持续更新中
VI-Grade闭源Unreal EngineMATLAB, Simulink提供从静态桌面解决方案到全尺寸动态模拟器的多种配置, 支持模块化组装和快速原型开发, 发布于2005年, 近期推出了云功能
CARLA开源Unreal EngineROS, Python, ApolloClient-Server架构, 支持多种传感器仿真, 发布于2017年, 持续更新中
Airsim开源Unreal Engine
/Unity		Python, C++, ROS, Simulink, Apollo能实时处理深度、实例分割, 支持软件在环仿真, 适用于多个下游任务, 发布于2017年, 但已停止更新
DeepDrive开源Unreal EngineROS, Python环境交互灵活, 集成深度学习框架, 发布于2018年, 已停止更新
TAD Sim开源Unreal EngineROS, Python高真实度场景仿真, 高精度车辆动力学模型, 高效的测试工具, 支持云仿真, 发布于2018年
Panosim闭源UnityMATLAB, Simulink高精度建模, 一体化仿真工具链, 2019年发布正式版本5.0
LGSVL开源UnityROS, Apollo, Autoware, Python, C++支持多种传感器仿真、多种高精度地图格式、V2X通信仿真, 发布于2019年, 2022年已停止更新
MetaDrive开源Panda3DPython轻量化, 提供精确的物理模拟, 支持多种传感器仿真, 2021年发布
DriveSim闭源OmniverseROS, Python, C++结合RTX技术和可微渲染, 支持多种传感器仿真, 发布于2021年
SimOne闭源SimOne-
Bus/Car/Truck		ROS, Simulink, Python, C++全链闭环仿真, 支持多种常用数据接口, 发布于2021年
Tab.1 Comparison of existing autonomous driving simulation platform
Fig.6 Simulation interface of AirSim
Fig.7 User interface of Carla simulation platform
Fig.8 Control interface of LGSVL
Fig.9 Scenario simulation in Prescan
Fig.10 UI of SimOne autonomous driving simulation platform
Fig.11 Drivesim for training perception algorithm
[1]   HO J, JAIN A, ABBEEL P Denoising diffusion probabilistic models[J]. Advances in Neural Information Processing Systems, 2020, 33: 6840- 6851
[2]   SONG Y, ERMON S. Generative modeling by estimating gradients of the data distribution [EB/OL]. (2020-10-10)[2025-04-30]. https://arxiv.org/abs/1907.05600.
[3]   SONG Y, SOHL-DICKSTEIN J, KINGMA D P, et al. Score-based generative modeling through stochastic differential equations [EB/OL]. (2021-02-10)[2025-04-30]. https://arxiv.org/abs/2011.13456.
[4]   DONG R, HAN C, PENG Y, et al. DreamLLM: synergistic multimodal comprehension and creation [EB/OL]. (2024-03-15)[2025-04-30]. https://arxiv.org/abs/2309.11499.
[5]   PANAGOPOULOU A, XUE L, YU N, et al. X-InstructBLIP: a framework for aligning X-modal instruction-aware representations to LLMs and emergent cross-modal reasoning [EB/OL]. (2024-09-09)[2025-04-30]. https://arxiv.org/abs/2311.18799.
[6]   ZHAO W X, ZHOU K, LI J, et al. A survey of large language models [EB/OL]. (2025-03-11)[2025-04-30]. https://arxiv.org/abs/2303.18223.
[7]   FENG T, WANG W, YANG Y. A survey of world models for autonomous driving [EB/OL]. (2025-02-16) [2025-04-30]. https://arxiv.org/abs/2501.11260.
[8]   FENG T, WANG W, YANG Y. A survey on visual traffic simulation: Models, evaluations, and applications in autonomous driving [J]. Computer Graphics Forum, 2020, 39(1): 287-308.
[9]   SARKER S, MAPLES B, ISLAM I, et al. A comprehensive review on traffic datasets and simulators for autonomous vehicles [EB/OL]. (2025-04-14)[2025-04-30]. https://arxiv.org/abs/2412.14207.
[10]   KINGMA D P, WELLING M. Auto-encoding variational Bayes [EB/OL]. (2022-12-10)[2025-04-30]. https://arxiv.org/abs/1312.6114
[11]   GOODFELLOW I, POUGET-ABADIE J, MIRZA M, et al Generative adversarial networks[J]. Communications of the ACM, 2020, 63 (11): 139- 144
doi: 10.1145/3422622
[12]   RADFORD A, METZ L, CHINTALA S. Unsupervised representation learning with deep convolutional generative adversarial networks [EB/OL]. (2016-01-07) [2025-04-30]. https://arxiv.org/abs/1511.06434.
[13]   KARRAS T, LAINE S, AILA T. A style-based generator architecture for generative adversarial networks [C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach: IEEE, 2020: 4396–4405.
[14]   RAMESH A, PAVLOV M, GOH G, et al. Zero-shot text-to-image generation [C]//International Conference on Machine Learning. [S. l. ]: PMLR, 2021: 8821-8831.
[15]   POOLE B, JAIN A, BARRON J T, et al. DreamFusion: text-to-3D using 2D diffusion [EB/OL]. (2022-09-29) [2025-04-30]. https://arxiv.org/abs/2209.14988.
[16]   LIN C H, GAO J, TANG L, et al. Magic3D: high-resolution text-to-3D content creation [C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Vancouver: IEEE, 2023: 300–309.
[17]   ZHANG L, RAO A, AGRAWALA M. Adding conditional control to text-to-image diffusion models [C]//Proceedings of the IEEE/CVF International Conference on Computer Vision. Paris: IEEE, 2024: 3813–3824.
[18]   LIU Y, ZHANG K, LI Y, et al. Sora: a review on background, technology, limitations, and opportunities of large vision models [EB/OL]. (2024-04-17) [2025-04-30]. https://arxiv.org/abs/2402.17177.
[19]   WANG X, MALEKI M A, AZHAR M W, et al. Moving forward: a review of autonomous driving software and hardware systems [EB/OL]. (2024-11-15) [2025-04-30]. https://arxiv.org/abs/2411.10291.
[20]   CHU Y J R, WEI T H, HUANG J B, et al. Sim-to-real transfer for miniature autonomous car racing [EB/OL]. (2020-11-11)[2025-04-30]. https://arxiv.org/abs/2011.05617.
[21]   ZHANG C, LIU Y, ZHAO D, et al. RoadView: a traffic scene simulator for autonomous vehicle simulation testing [C]//Proceedings of the 17th International IEEE Conference on Intelligent Transportation Systems. Qingdao: IEEE, 2014: 1160–1165.
[22]   TAN S, WONG K, WANG S, et al. SceneGen: learning to generate realistic traffic scenes [C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Nashville: IEEE, 2021: 892–901.
[23]   WEI Y, WANG Z, LU Y, et al. Editable scene simulation for autonomous driving via collaborative LLM-agents [C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle: IEEE, 2024: 15077–15087.
[24]   HU A, RUSSELL L, YEO H, et al. Gaia-1: a generative world model for autonomous driving [EB/OL]. (2023-09-29)[2025-04-30]. https://arxiv.org/abs/2309.17080.
[25]   ZHAO G, NI C, WANG X, et al. DriveDreamer4D: world models are effective data machines for 4D driving scene representation [C]// Proceedings of the Computer Vision and Pattern Recognition Conference. Los Angeles: IEEE, 2025: 12015-12026.
[26]   SUN S, GU Z, SUN T, et al DriveSceneGen: generating diverse and realistic driving scenarios from scratch[J]. IEEE Robotics and Automation Letters, 2024, 9 (8): 7007- 7014
doi: 10.1109/LRA.2024.3416792
[27]   PRONOVOST E, GANESINA M R, HENDY N, et al Scenario diffusion: controllable driving scenario generation with diffusion[J]. Advances in Neural Information Processing Systems, 2023, 36: 68873- 68894
[28]   LI X, ZHANG Y, YE X. DrivingDiffusion: layout-guided multi-view driving scenarios video generation with latent diffusion model [C]// European Conference on Computer Vision. Cham: Springer, 2024: 469-485.
[29]   MÜLLER N, SIMONELLI A, PORZI L, et al. AutoRF: learning 3D object radiance fields from single view observations [C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. New Orleans: IEEE, 2022: 3961–3970.
[30]   KERBL B, KOPANAS G, LEIMKUEHLER T, et al 3D Gaussian splatting for real-time radiance field rendering[J]. ACM Transactions on Graphics, 2023, 42 (4): 1- 14
[31]   YAN Y, LIN H, ZHOU C, et al. Street Gaussians: modeling dynamic urban scenes with Gaussian splatting [C]//European Conference on Computer Vision. Milan: Springer, 2024: 156-173.
[32]   ZHOU X, LIN Z, SHAN X, et al. DrivingGaussian: composite Gaussian splatting for surrounding dynamic autonomous driving scenes [C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle: IEEE, 2024: 21634-21643.
[33]   KHAN M, FAZLALI H, SHARMA D, et al. AutoSplat: constrained Gaussian splatting for autonomous driving scene reconstruction [C]//2025 IEEE International Conference on Robotics and Automation. Atlanta: IEEE, 2025: 8315-8321.
[34]   HILLAIRE S. A scalable and production ready sky and atmosphere rendering technique [J]. Computer Graphics Forum, 2020, 39(4): 13-22.
[35]   ZYRIANOV V, CHE H, LIU Z, et al. LidarDM: generative LiDAR simulation in a generated world [C]//2025 IEEE International Conference on Robotics and Automation. Seattle: IEEE, 2025: 6055-6062.
[36]   LI Y, LIN Z H, FORSYTH D, et al. Climatenerf: extreme weather synthesis in neural radiance field [C]//Proceedings of the IEEE/CVF International Conference on Computer Vision. Paris: IEEE, 2023: 3227-3238.
[37]   MÜLLER T, EVANS A, SCHIED C, et al Instant neural graphics primitives with a multiresolution hash encoding[J]. ACM Transactions on Graphics, 2022, 41 (4): 1- 15
[38]   DAI Q, NI X, SHEN Q, et al. RainyGS: efficient rain synthesis with physically-based Gaussian splatting [C]//Proceedings of the Computer Vision and Pattern Recognition Conference. Los Angeles: IEEE, 2025: 16153-16162.
[39]   XIE Y, ZHANG M, HAO Q. ClimateGS: real-time climate simulation with 3D Gaussian style transfer [EB/OL]. (2025-03-19)[2025-04-30]. https://arxiv.org/abs/2503.14845.
[40]   BENEKOHAL R F, TREITERER J CARSIM: car-following model for simulation of traffic in normal and stop-and-go conditions[J]. Transportation Research Record, 1988, 1194: 99- 111
[41]   NVIDIA Corporation. Sensor simulation [EB/OL]. (2025-05-02) [2025-05-02]. https://www.nvidia.cn/ glossary/sensor-simulation.
[42]   CABON Y, MURRAY N, HUMENBERGER M. Virtual KITTI 2 [EB/OL]. (2020-01-29)[2025-04-30]. https:// arxiv.org /abs/2001.10773.
[43]   GEIGER A, LENZ P, STILLER C, et al Vision meets robotics: the kitti dataset[J]. International Journal of Robotics Research, 2013, 32 (11): 1231- 1237
doi: 10.1177/0278364913491297
[44]   CAESAR H, BANKITI V, LANG A H, et al. nuScenes: a multimodal dataset for autonomous driving [C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle: IEEE, 2020: 11618-11628.
[45]   SUN P, KRETZSCHMAR H, DOTIWALLA X, et al. Scalability in perception for autonomous driving: waymo open dataset [C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle: IEEE, 2020: 2443–2451.
[46]   IŞLER H. Comparison of electric (EV) and fossil fuel (gasoline-diesel) vehicles in terms of torque and power [EB/OL]. (2023-05-31) [2025-05-02]. https://www.researchgate.net/publication/370553199.
[47]   SURESHKUMAR P, RAJASEKAR C, DEEPA R, et al Modeling and simulation of electric vehicle drive with evaluating forces[J]. Journal of Data Acquisition and Processing, 2024, 39 (1): 759- 769
[48]   PREVIATI G, MASTINU G, GOBBI M Mass, centre of gravity location and inertia tensor of electric vehicles: measured data for accurate accident reconstruction[J]. World Electric Vehicle Journal, 2024, 15 (6): 266
doi: 10.3390/wevj15060266
[49]   JHA S, TSAI T, HARI S, et al. Kayotee: a fault injection-based system to assess the safety and reliability of autonomous vehicles to faults and errors [EB/OL]. (2019-07-01)[2025-04-30]. https://arxiv.org/abs/1907.01024.
[50]   JHA S, BANERJEE S S, CYRIAC J, et al. AVFI: fault injection for autonomous vehicles [C]//Proceedings of the 48th Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops. Luxembourg: IEEE, 2018: 55–56.
[51]   AMYAN A, ABBOUSH M, KNIEKE C, et al Automating fault test cases generation and execution for automotive safety validation via NLP and HIL simulation[J]. Sensors, 2024, 24 (10): 3145
doi: 10.3390/s24103145
[52]   QUIGLEY M, CONLEY K, GERKEY B, et al. ROS: an open-source robot operating system [C]//Open-Source Software Workshop of the International Conference on Robotics and Automation. Kobe: IEEE, 2009: 5-6.
[53]   RONG G, SHIN B H, TABATABAEE H, et al. LGSVL simulator: a high fidelity simulator for autonomous driving [C]//Proceedings of the IEEE 23rd International Conference on Intelligent Transportation Systems. Rhodes: IEEE, 2020: 1–6.
[54]   DOSOVITSKIY A, ROS G, CODEVILLA F, et al. CARLA: an open urban driving simulator [C] // Proceedings of the Conference on Robot Learning. Sydney: PMLR, 2017: 1-16.
[55]   Synkrotron. Carla_Apollo_Bridge [EB/OL]. (2024-09-09)[2025-05-02]. https://github.com/guardstrikelab/carla_apollo_bridge.
[56]   XU X, LIU K, DAI P, et al. Enabling digital twin in vehicular edge computing: a multi-agent multi-objective deep reinforcement learning solution [EB/OL]. (2023-01-27)[2025-04-30]. https://arxiv.org/abs/2210.17386v1.
[57]   GRIGORESCU S, COCIAS T, TRASNEA B, et al Cloud2edge elastic AI framework for prototyping and deployment of AI inference engines in autonomous vehicles[J]. Sensors, 2020, 20 (19): 5450
doi: 10.3390/s20195450
[58]   YANG X, WEN L, MA Y, et al. DriveArena: a closed-loop generative simulation platform for autonomous driving [EB/OL]. (2024-08-01)[2025-04-30]. https://arxiv.org/abs/2408.00415.
[59]   HUANG Z, SHENG Z, WAN Z, et al. Sky-drive: a distributed multi-agent simulation platform for human-AI collaborative and socially-aware future transportation [EB/OL]. (2025-04-25)[2025-04-30]. https://arxiv.org/abs/2504.18010.
[60]   Alphadrive. AlphaDrive cloud simulation platform [EB/OL]. (2025-05-02)[2025-05-02]. https://alphadrive.ai.
[61]   Unreal Engine. Unreal engine 5 [EB/OL]. (2022-04-05) [2025-04-30]. https://www.unrealengine.com/en-US/what-is-unreal-engine-4, 2018.
[62]   HUSSAIN A, SHAKEEL H, HUSSAIN F, et al Unity game development engine: a technical survey[J]. University of Sindh Journal of Information and Communication Technology, 2020, 4 (2): 73- 81
[63]   JUNG H Y, PAEK D H, KONG S H. Open-source autonomous driving software platforms: comparison of autoware and Apollo [EB/OL]. (2025-01-31) [2025-04-30]. https://arxiv.org/abs/2501.18942.
[64]   EREZ T, TASSA Y, TODOROV E. Simulation tools for model-based robotics: comparison of bullet, havok, MuJoCo, ODE and PhysX [C]//Proceedings of the IEEE International Conference on Robotics and Automation. Seattle: IEEE, 2015: 4397–4404.
[65]   GOSLIN M, MINE M R The Panda3D graphics engine[J]. Computer, 2004, 37 (10): 112- 114
[66]   RAO V, JAIN N, RANA P, et al. Game development using Panda 3D game engine [J]. International Journal on Recent and Innovation Trends in Computing and Communication, 3(2): 534-536.
[67]   HENRICH V, REUTER T. CarDriver: using Python and Panda3D to construct a virtual environment for teaching driving [EB/OL]. (2008-12-31) [2025-04-30]. https://api.semanticscholar.org/CorpusID:109635468.
[68]   SHAH S, DEY D, LOVETT C, et al. Airsim: high-fidelity visual and physical simulation for autonomous vehicles [C] // Conference on Field and Service Robotics. Zurich: Springer, 2017: 621-635.
[69]   REDMON J, DIVVALA S, GIRSHICK R, et al. You only look once: unified, real-time object detection [C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas: IEEE, 2016: 779-788.
[70]   WU D, LIAO M W, ZHANG W T, et al Yolop: you only look once for panoptic driving perception[J]. Machine Intelligence Research, 2022, 19 (6): 550- 562
doi: 10.1007/s11633-022-1339-y
[71]   SARDA A, DIXIT S, BHAN A. Object detection for autonomous driving using YOLO [you only look once] algorithm [C]//Proceedings of the 3rd International Conference on Intelligent Communication Technologies and Virtual Mobile Networks. Tirunelveli: IEEE, 2021: 1370–1374.
[72]   CAO Y, LI C, PENG Y, et al MCS-YOLO: a multiscale object detection method for autonomous driving road environment recognition[J]. IEEE Access, 2023, 11: 22342- 22354
doi: 10.1109/ACCESS.2023.3252021
[73]   WEI Y, ZHAO L, ZHENG W, et al. Surroundocc: multi-camera 3d occupancy prediction for autonomous driving [C]//Proceedings of the IEEE/CVF International Conference on Computer Vision. Paris: IEEE, 2023: 21729-21740.
[74]   TIAN X, JIANG T, YUN L, et al Occ3D: a large-scale 3D occupancy prediction benchmark for autonomous driving[J]. Advances in Neural Information Processing Systems, 2023, 36: 64318- 64330
[75]   ZHENG W, CHEN W, HUANG Y, et al. Occworld: learning a 3d occupancy world model for autonomous driving [C]//European Conference on Computer Vision. Milan: Springer, 2024: 55-72.
[76]   ACUNA D, PHILION J, FIDLER S Towards optimal strategies for training self-driving perception models in simulation[J]. Advances in Neural Information Processing Systems, 2021, 34: 1686- 1699
[77]   CAO M, RAMEZANI R Data generation using simulation technology to improve perception mechanism of autonomous vehicles[J]. Journal of Physics: Conference Series, 2023, 2547 (1): 012006
[78]   YANG Z, CHEN Y, WANG J, et al. Unisim: a neural closed-loop sensor simulator [C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Vancouver: IEEE, 2023: 1389- 1399.
[79]   MILDENHALL B, SRINIVASAN P P, TANCIK M, et al. NeRF: representing scenes as neural radiance fields for view synthesis [C]//European Conference on Computer Vision. [S. l. ]: Springer, 2020: 405-421.
[80]   WU Z, LIU T, LUO L, et al. Mars: an instance-aware, modular and realistic simulator for autonomous driving [C]// International Conference on Artificial Intelligence. Singapore: Springer, 2023: 3-15.
[81]   YANG J, IVANOVIC B, LITANY O, et al. EmerNeRF: emergent spatial-temporal scene decomposition via self-supervision [EB/OL]. (2023-11-03)[2025-04-30]. https://arxiv.org/abs/2311.02077.
[82]   ZHAO W, QUERALTA J P, WESTERLUND T. Sim-to-real transfer in deep reinforcement learning for robotics: a survey [C]//2020 IEEE Symposium Series on Computational Intelligence. Canberra: IEEE, 2020: 737-744.
[83]   DAZA I G, IZQUIERDO R, MARTÍNEZ L M, et al Sim-to-real transfer and reality gap modeling in model predictive control for autonomous driving[J]. Applied Intelligence, 2023, 53 (10): 12719- 12735
doi: 10.1007/s10489-022-04148-1
[84]   HU X, LI S, HUANG T, et al How simulation helps autonomous driving: a survey of Sim2real, digital twins, and parallel intelligence[J]. IEEE Transactions on Intelligent Vehicles, 2024, 9 (1): 593- 612
doi: 10.1109/TIV.2023.3312777
[85]   AKHAURI S, ZHENG L, LIN M C. Enhanced transfer learning for autonomous driving with systematic accident simulation [C]//IEEE/RSJ International Conference on Intelligent Robots and Systems. Las Vegas: IEEE, 2020: 5986–5993.
[86]   AKHAURI S, ZHENG L, GOLDSTEIN T, et al. Improving generalization of transfer learning across domains using spatio-temporal features in autonomous driving [EB/OL]. (2021-09-23)[2025-04-30]. https://arxiv.org/abs/2103.08116.
[1] Wenqiang CHEN,Linyue FENG,Dongdan WANG,Yulei GU,Xuan ZHAO. Vehicle trajectory prediction model integrating dynamic risk map and multivariate attention mechanism[J]. Journal of ZheJiang University (Engineering Science), 2026, 60(3): 455-467.
[2] Wenqiang CHEN,Dongdan WANG,Wenying ZHU,Yongjie WANG,Tao WANG. Vehicle multimodal trajectory prediction model based on spatio-temporal graph attention network[J]. Journal of ZheJiang University (Engineering Science), 2025, 59(3): 443-450.
[3] Dengfeng LIU,Wenjing GUO,Shihai CHEN. Content-guided attention-based lane detection network[J]. Journal of ZheJiang University (Engineering Science), 2025, 59(3): 451-459.
[4] Hongzhao DONG,Shaoxuan LIN,Yini SHE. Research progress of YOLO detection technology for traffic object[J]. Journal of ZheJiang University (Engineering Science), 2025, 59(2): 249-260.
[5] Zhaolong DONG,He HUANG,Zhanyi LI,Lan YANG,Huifeng WANG. Snowy scene integration construction method in autonomous driving sample library[J]. Journal of ZheJiang University (Engineering Science), 2025, 59(10): 2078-2085.
[6] Bo WANG,Changhe LIU,Chi ZHANG,Min ZHANG,Guidong WU. Crash risk early warning in highway work zone based on road surveillance camera[J]. Journal of ZheJiang University (Engineering Science), 2024, 58(6): 1221-1232.
[7] Hao-yuan WANG,Yu LIANG,Wei ZHANG. Real-time smoke segmentation algorithm fused with multi-resolution representation[J]. Journal of ZheJiang University (Engineering Science), 2021, 55(12): 2334-2341.
[8] Ying LI,Fang CHENG,Zhi-lin ZHAO. Machining precision online measurement of large span pin hole using structured light[J]. Journal of ZheJiang University (Engineering Science), 2020, 54(3): 557-565.
[9] Yao JIN,Wei ZHANG. Real-time fire detection algorithm with Anchor-Free network architecture[J]. Journal of ZheJiang University (Engineering Science), 2020, 54(12): 2430-2436.
[10] WANG Zhi, ZHU Shi qiang, BU Yan, GUO Zhen min. Stereo matching algorithm using improved guided filtering[J]. Journal of ZheJiang University (Engineering Science), 2016, 50(12): 2262-2269.
[11] TIAN Tian,GONG Dun-wei. Evolutionary generation of test data for path coverage through selecting target paths based on coverage difficulty[J]. Journal of ZheJiang University (Engineering Science), 2014, 48(5): 948-994.