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Autonomous vehicles typically follow a modular structure, organized into perception, planning, and control components. Unlike previous surveys, which often focus on specific modular system components or single driving environments, our review uniquely compares both settings, highlighting how deep learning and reinforcement learning methods address the challenges specific to each. We present an in-depth analysis of local and global planning methods, including the integration of benchmarks, simulations, and real-time platforms. Additionally, we compare various evaluation metrics and performance outcomes for current methodologies. Finally, we offer insights into emerging research directions based on the latest advancements, providing a roadmap for future innovation in autonomous driving.<\/jats:p>","DOI":"10.3390\/smartcities8030079","type":"journal-article","created":{"date-parts":[[2025,5,6]],"date-time":"2025-05-06T05:54:43Z","timestamp":1746510883000},"page":"79","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["A Comprehensive Literature Review on Modular Approaches to Autonomous Driving: Deep Learning for Road and Racing Scenarios"],"prefix":"10.3390","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3594-9059","authenticated-orcid":false,"given":"Kamal","family":"Hussain","sequence":"first","affiliation":[{"name":"Instituto de Engenharia de Sistemas e Computadores: Investiga\u00e7\u00e3o e Desenvolvimento, Instituto Superior Tecnico, University of Lisboa, 1000-029 Lisbon, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8826-5163","authenticated-orcid":false,"given":"Catarina","family":"Moreira","sequence":"additional","affiliation":[{"name":"Data Science Institute, University Technology Sydney, Ultimo, NSW 2007, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8120-7649","authenticated-orcid":false,"given":"Jo\u00e3o","family":"Pereira","sequence":"additional","affiliation":[{"name":"Instituto de Engenharia de Sistemas e Computadores: Investiga\u00e7\u00e3o e Desenvolvimento, Instituto Superior Tecnico, University of Lisboa, 1000-029 Lisbon, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9821-7424","authenticated-orcid":false,"given":"Sandra","family":"Jardim","sequence":"additional","affiliation":[{"name":"Smart Cities Research Center, Polytechnic Institute of Tomar, 2300-313 Tomar, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5441-4637","authenticated-orcid":false,"given":"Joaquim","family":"Jorge","sequence":"additional","affiliation":[{"name":"Instituto de Engenharia de Sistemas e Computadores: Investiga\u00e7\u00e3o e Desenvolvimento, Instituto Superior Tecnico, University of Lisboa, 1000-029 Lisbon, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2025,5,6]]},"reference":[{"key":"ref_1","unstructured":"World Health Organization (2024, September 10). Global Health Estimates 2019: Deaths by Cause, Age, Sex, by Country and by Region. Available online: https:\/\/injuryfacts.nsc.org\/international\/international-overview\/."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1186\/s13017-022-00412-4","article-title":"Motorized 2\u20133 wheelers death rates over a decade: A global study","volume":"17","author":"Yasin","year":"2022","journal-title":"World J. Emerg. Surg."},{"key":"ref_3","unstructured":"Francis, J., Chen, B., Ganju, S., Kathpal, S., Poonganam, J., Shivani, A., Vyas, V., Genc, S., Zhukov, I., and Kumskoy, M. (2022). Learn-to-Race Challenge 2022: Benchmarking Safe Learning and Cross-domain Generalisation in Autonomous Racing. arXiv."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"122836","DOI":"10.1016\/j.eswa.2023.122836","article-title":"Autonomous driving system: A comprehensive survey","volume":"242","author":"Zhao","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref_5","unstructured":"Wadekar, S.N., Schwartz, B., Kannan, S.S., Mar, M., Manna, R.K., Chellapandi, V., Gonzalez, D.J., and Gamal, A.E. (2021). Towards End-to-End Deep Learning for Autonomous Racing: On Data Collection and a Unified Architecture for Steering and Throttle Prediction. arXiv."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Bosello, M., Tse, R., and Pau, G. (2022, January 8\u201311). Train in Austria, Race in Montecarlo: Generalized RL for Cross-Track F1tenth LIDAR-Based Races. Proceedings of the 2022 IEEE 19th Annual Consumer Communications and Networking Conference (CCNC), Las Vegas, NV, USA.","DOI":"10.1109\/CCNC49033.2022.9700730"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"19817","DOI":"10.1109\/TITS.2022.3160673","article-title":"An End-to-End Curriculum Learning Approach for Autonomous Driving Scenarios","volume":"23","author":"Anzalone","year":"2022","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/j.inffus.2020.11.002","article-title":"Point-cloud based 3D object detection and classification methods for self-driving applications: A survey and taxonomy","volume":"68","author":"Fernandes","year":"2021","journal-title":"Inf. Fusion"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1016\/S1474-6670(17)55320-3","article-title":"Autonomous High Speed Road Vehicle Guidance by Computer Vision1","volume":"20","author":"Dickmanns","year":"1987","journal-title":"IFAC Proc. Vol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"661","DOI":"10.1002\/rob.20147","article-title":"Stanley: The robot that won the DARPA Grand Challenge","volume":"23","author":"Thrun","year":"2006","journal-title":"J. Field Robot."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Betz, J., Betz, T., Fent, F., Geisslinger, M., Heilmeier, A., Hermansdorfer, L., Herrmann, T., Huch, S., Karle, P., and Lienkamp, M. (2022). TUM Autonomous Motorsport: An Autonomous Racing Software for the Indy Autonomous Challenge. arXiv.","DOI":"10.1002\/rob.22153"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Huang, Y., and Chen, Y. (2020). Autonomous Driving with Deep Learning: A Survey of State-of-Art Technologies. arXiv.","DOI":"10.1109\/QRS-C51114.2020.00045"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"763","DOI":"10.3390\/make5030041","article-title":"Deep Learning and Autonomous Vehicles: Strategic Themes, Applications, and Research Agenda Using SciMAT and Content-Centric Analysis, a Systematic Review","volume":"5","author":"Morooka","year":"2023","journal-title":"Mach. Learn. Knowl. Extr."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Golroudbari, A.A., and Sabour, M.H. (2023). Recent Advancements in Deep Learning Applications and Methods for Autonomous Navigation: A Comprehensive Review. arXiv.","DOI":"10.22541\/au.168664884.43899660\/v1"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"10523","DOI":"10.1109\/ACCESS.2022.3144407","article-title":"Autonomous Vehicles Perception (AVP) Using Deep Learning: Modeling, Assessment, and Challenges","volume":"10","author":"Jebamikyous","year":"2022","journal-title":"IEEE Access"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Jiang, Y., and Hsiao, T. (2021, January 24\u201326). Deep Learning in Perception of Autonomous Vehicles. Proceedings of the 2021 International Conference on Public Art and Human Development (ICPAHD 2021), Kunming, China.","DOI":"10.2991\/assehr.k.220110.107"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Delecki, H., Itkina, M., Lange, B., Senanayake, R., and Kochenderfer, M.J. (2022). How Do We Fail? Stress Testing Perception in Autonomous Vehicles. arXiv.","DOI":"10.1109\/IROS47612.2022.9981724"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"100057","DOI":"10.1016\/j.array.2021.100057","article-title":"Deep learning for object detection and scene perception in self-driving cars: Survey, challenges, and open issues","volume":"10","author":"Gupta","year":"2021","journal-title":"Array"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"108796","DOI":"10.1016\/j.patcog.2022.108796","article-title":"3D Object Detection for Autonomous Driving: A Survey","volume":"130","author":"Qian","year":"2022","journal-title":"Pattern Recognit."},{"key":"ref_20","unstructured":"Mao, J., Shi, S., Wang, X., and Li, H. (2022). 3D Object Detection for Autonomous Driving: A Review and New Outlooks. arXiv."},{"key":"ref_21","unstructured":"Ma, X., Ouyang, W., Simonelli, A., and Ricci, E. (2022). 3D Object Detection from Images for Autonomous Driving: A Survey. arXiv."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1515\/teme-2021-0004","article-title":"Comparison of different SLAM approaches for a driverless race car","volume":"88","author":"Large","year":"2021","journal-title":"tm Tech. Mess."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"117734","DOI":"10.1016\/j.eswa.2022.117734","article-title":"A survey of state-of-the-art on visual SLAM","volume":"205","author":"Fitzgerald","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"3692","DOI":"10.1109\/TIV.2023.3274536","article-title":"Motion Planning for Autonomous Driving: The State of the Art and Future Perspectives","volume":"8","author":"Teng","year":"2023","journal-title":"IEEE Trans. Intell. Veh."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Abdallaoui, S., Aglzim, E.H., Chaibet, A., and Krib\u00e8che, A. (2022). Thorough Review Analysis of Safe Control of Autonomous Vehicles: Path Planning and Navigation Techniques. Energies, 15.","DOI":"10.3390\/en15041358"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1186\/s10033-021-00639-3","article-title":"Planning and Decision-making for Connected Autonomous Vehicles at Road Intersections: A Review","volume":"34","author":"Li","year":"2021","journal-title":"Chin. J. Mech. Eng."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"324","DOI":"10.1177\/03611981211035764","article-title":"Review of Learning-Based Longitudinal Motion Planning for Autonomous Vehicles: Research Gaps Between Self-Driving and Traffic Congestion","volume":"2676","author":"Zhou","year":"2022","journal-title":"Transp. Res. Rec."},{"key":"ref_28","first-page":"100164","article-title":"Autonomous Driving Architectures: Insights of Machine Learning and Deep Learning Algorithms","volume":"6","author":"Bachute","year":"2021","journal-title":"Mach. Learn. Appl."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"15321","DOI":"10.1109\/JSEN.2023.3280959","article-title":"Involvement of Deep Learning for Vision Sensor-Based Autonomous Driving Control: A Review","volume":"23","author":"Khanum","year":"2023","journal-title":"IEEE Sensors J."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Kalandyk, D. (2021, January 13\u201315). Reinforcement learning in car control: A brief survey. Proceedings of the 2021 Selected Issues of Electrical Engineering and Electronics (WZEE), Rzeszow, Poland.","DOI":"10.1109\/WZEE54157.2021.9576838"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1364","DOI":"10.1109\/TNNLS.2020.3043505","article-title":"A Survey of End-to-End Driving: Architectures and Training Methods","volume":"33","author":"Tampuu","year":"2022","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"14128","DOI":"10.1109\/TITS.2022.3144867","article-title":"A Survey on Imitation Learning Techniques for End-to-End Autonomous Vehicles","volume":"23","author":"Yi","year":"2022","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"75296","DOI":"10.1109\/ACCESS.2022.3192019","article-title":"A Review of End-to-End Autonomous Driving in Urban Environments","volume":"10","author":"Coelho","year":"2022","journal-title":"IEEE Access"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"56","DOI":"10.55708\/js0110008","article-title":"The Current Trends of Deep Learning in Autonomous Vehicles: A Review","volume":"1","author":"Huang","year":"2022","journal-title":"J. Eng. Res. Sci."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1049\/itr2.12257","article-title":"Deep learning serves traffic safety analysis: A forward-looking review","volume":"17","author":"Razi","year":"2023","journal-title":"IET Intell. Transp. Syst."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"4316","DOI":"10.1109\/TITS.2020.3032227","article-title":"Deep Learning for Safe Autonomous Driving: Current Challenges and Future Directions","volume":"22","author":"Muhammad","year":"2021","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Deng, Y., Zhang, T., Lou, G., Zheng, X., Jin, J., and Han, Q. (2021). Deep Learning-Based Autonomous Driving Systems: A Survey of Attacks and Defenses. arXiv.","DOI":"10.1109\/TII.2021.3071405"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Hou, L., Chen, H., Zhang, G.K., and Wang, X. (2021). Deep Learning-Based Applications for Safety Management in the AEC Industry: A Review. Appl. Sci., 11.","DOI":"10.3390\/app11020821"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Li, Y., Katsumata, K., Javanmardi, E., and Tsukada, M. (2024). Large Language Models for Human-like Autonomous Driving: A Survey. arXiv.","DOI":"10.1109\/ITSC58415.2024.10919629"},{"key":"ref_40","unstructured":"Yang, Z., Jia, X., Li, H., and Yan, J. (2024). LLM4Drive: A Survey of Large Language Models for Autonomous Driving. arXiv."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Cui, C., Ma, Y., Cao, X., Ye, W., Zhou, Y., Liang, K., Chen, J., Lu, J., Yang, Z., and Liao, K.D. (2023). A Survey on Multimodal Large Language Models for Autonomous Driving. arXiv.","DOI":"10.1109\/WACVW60836.2024.00106"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Zhou, X., Liu, M., Yurtsever, E., Zagar, B.L., Zimmer, W., Cao, H., and Knoll, A.C. (2024). Vision Language Models in Autonomous Driving: A Survey and Outlook. arXiv.","DOI":"10.1109\/TIV.2024.3402136"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1450","DOI":"10.1109\/TIV.2023.3327715","article-title":"DriveLLM: Charting the Path Toward Full Autonomous Driving With Large Language Models","volume":"9","author":"Cui","year":"2024","journal-title":"IEEE Trans. Intell. Veh."},{"key":"ref_44","unstructured":"Zheng, P., Zhao, Y., Gong, Z., Zhu, H., and Wu, S. (2024). SimpleLLM4AD: An End-to-End Vision-Language Model with Graph Visual Question Answering for Autonomous Driving. arXiv."},{"key":"ref_45","first-page":"1043","article-title":"A survey on simulators for testing self-driving cars","volume":"66","author":"Kaur","year":"2021","journal-title":"Comput. Mater. Contin."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Zhang, T., Liu, H., Wang, W., and Wang, X. (2024). Virtual Tools for Testing Autonomous Driving: A Survey and Benchmark of Simulators, Datasets, and Competitions. Electronics, 13.","DOI":"10.3390\/electronics13173486"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"8192","DOI":"10.1109\/LRA.2023.3325689","article-title":"Sim-on-Wheels: Physical World in the Loop Simulation for Self-Driving","volume":"8","author":"Shen","year":"2023","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_48","first-page":"170","article-title":"Small-scale self-driving cars: A systematic literature review","volume":"11","author":"Caleffi","year":"2024","journal-title":"J. Traffic Transp. Eng."},{"key":"ref_49","unstructured":"Fremont, D., Kim, E., Pant, Y., Seshia, S., Acharya, A., Bruso, X., Wells, P., Lemke, S., Lu, Q., and Mehta, S. (2023, January 20\u201323). Formal Scenario-Based Testing of Autonomous Vehicles: From Simulation to the Real World. Proceedings of the 23rd International Conference on Intelligent Transportation, Rhodes, Greece."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"n71","DOI":"10.1136\/bmj.n71","article-title":"The PRISMA 2020 statement: An updated guideline for reporting systematic reviews","volume":"372","author":"Page","year":"2021","journal-title":"BMJ"},{"key":"ref_51","unstructured":"Howe, M., Bockman, J., Orenstein, A., Podgorski, S., Bahrami, S., and Reid, I. (2022). The Edge of Disaster: A Battle Between Autonomous Racing and Safety. arXiv."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Zhou, Y., Wen, S., Wang, D., Meng, J., Mu, J., and Irampaye, R. (2022). MobileYOLO: Real-Time Object Detection Algorithm in Autonomous Driving Scenarios. Sensors, 22.","DOI":"10.3390\/s22093349"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"3065438","DOI":"10.1109\/TIM.2021.3065438","article-title":"YOLOv4-5D: An Effective and Efficient Object Detector for Autonomous Driving","volume":"70","author":"Cai","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"121036","DOI":"10.1016\/j.eswa.2023.121036","article-title":"An improved lightweight small object detection framework applied to real-time autonomous driving","volume":"234","author":"Mahaur","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref_55","first-page":"1","article-title":"YOLOv8-Lite: A Lightweight Object Detection Model for Real-time Autonomous Driving Systems","volume":"1","author":"Yang","year":"2024","journal-title":"IECE Trans. Emerg. Top. Artif. Intell."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Jia, X., Tong, Y., Qiao, H., Li, M., Tong, J., and Liang, B. (2023). Fast and accurate object detector for autonomous driving based on improved YOLOv5. Sci. Rep., 13.","DOI":"10.1038\/s41598-023-36868-w"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Ranasinghe, P., Muthukuda, D., Morapitiya, P., Dissanayake, M.B., and Lakmal, H. (2023, January 25\u201326). Deep Learning Based Low Light Enhancements for Advanced Driver-Assistance Systems at Night. Proceedings of the 2023 IEEE 17th International Conference on Industrial and Information Systems (ICIIS), Peradeniya, Sri Lanka.","DOI":"10.1109\/ICIIS58898.2023.10253533"},{"key":"ref_58","unstructured":"Karagounis, A. (2024). Leveraging Large Language Models for Enhancing Autonomous Vehicle Perception. arXiv."},{"key":"ref_59","unstructured":"Ananthajothi, K., Satyaa Sudarshan, G.S., and Saran, J.U. (2023, January 4\u20135). LLM\u2019s for Autonomous Driving: A New Way to Teach Machines to Drive. Proceedings of the 2023 3rd International Conference on Mobile Networks and Wireless Communications (ICMNWC), Tumkur, India."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"1282","DOI":"10.3390\/electronics14071282","article-title":"Vision-Language Models for Autonomous Driving: CLIP-Based Dynamic Scene Understanding","volume":"14","author":"Elhenawy","year":"2025","journal-title":"Electronics"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Guo, Z., Yagudin, Z., Lykov, A., Konenkov, M., and Tsetserukou, D. (2024). VLM-Auto: VLM-based Autonomous Driving Assistant with Human-like Behavior and Understanding for Complex Road Scenes. arXiv.","DOI":"10.1109\/FLLM63129.2024.10852498"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Mohapatra, S., Yogamani, S., Gotzig, H., Milz, S., and Mader, P. (2021, January 19\u201322). BEVDetNet: Bird\u2019s Eye View LiDAR Point Cloud based Real-time 3D Object Detection for Autonomous Driving. Proceedings of the 2021 IEEE International Intelligent Transportation Systems Conference (ITSC), Indianapolis, IN, USA.","DOI":"10.1109\/ITSC48978.2021.9564490"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Zhou, Y., Wen, S., Wang, D., Mu, J., and Richard, I. (2021). Object Detection in Autonomous Driving Scenarios Based on an Improved Faster-RCNN. Appl. Sci., 11.","DOI":"10.3390\/app112411630"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1016\/j.neucom.2021.11.048","article-title":"Stereo CenterNet-based 3D object detection for autonomous driving","volume":"471","author":"Shi","year":"2022","journal-title":"Neurocomputing"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"An, K., Chen, Y., Wang, S., and Xiao, Z. (2021). RCBi-CenterNet: An Absolute Pose Policy for 3D Object Detection in Autonomous Driving. Appl. Sci., 11.","DOI":"10.3390\/app11125621"},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Chen, Y.N., Dai, H., and Ding, Y. (2022, January 18\u201324). Pseudo-Stereo for Monocular 3D Object Detection in Autonomous Driving. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.00096"},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Nobis, F., Geisslinger, M., Weber, M., Betz, J., and Lienkamp, M. (2020). A Deep Learning-based Radar and Camera Sensor Fusion Architecture for Object Detection. arXiv.","DOI":"10.1109\/SDF.2019.8916629"},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Zhang, J., Cao, J., Chang, J., Li, X., Liu, H., and Li, Z. (2024). Research on the Application of Computer Vision Based on Deep Learning in Autonomous Driving Technology. arXiv.","DOI":"10.1007\/978-981-96-2409-6_9"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"191","DOI":"10.54254\/2755-2721\/33\/20230265","article-title":"Deep learning based visual perception and decision-making technology for autonomous vehicles","volume":"33","author":"Zhao","year":"2024","journal-title":"Appl. Comput. Eng."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"784","DOI":"10.1109\/TGRS.2020.2996617","article-title":"A Dense Feature Pyramid Network-Based Deep Learning Model for Road Marking Instance Segmentation Using MLS Point Clouds","volume":"59","author":"Chen","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Shao, X., Wang, Q., Yang, W., Chen, Y., Xie, Y., Shen, Y., and Wang, Z. (2021). Multi-Scale Feature Pyramid Network: A Heavily Occluded Pedestrian Detection Network Based on ResNet. Sensors, 21.","DOI":"10.3390\/s21051820"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.neucom.2022.07.041","article-title":"GCNet: Grid-like context-aware network for RGB-thermal semantic segmentation","volume":"506","author":"Liu","year":"2022","journal-title":"Neurocomputing"},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Bai, J., Zhu, J., Song, Y., Zhao, L., Hou, Z., Du, R., and Li, H. (2021). A3t-gcn: Attention temporal graph convolutional network for traffic forecasting. ISPRS Int. J. Geo-Inf., 10.","DOI":"10.3390\/ijgi10070485"},{"key":"ref_74","unstructured":"Mseddi, W., Sedrine, M.A., and Attia, R. (2021, January 23\u201327). YOLOv5 Based Visual Localization For Autonomous Vehicles. Proceedings of the 2021 29th European Signal Processing Conference (EUSIPCO), Dublin, Ireland."},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Akata, Z., Geiger, A., and Sattler, T. (October, January 28). 4Seasons: A Cross-Season Dataset for Multi-Weather SLAM in Autonomous Driving. Proceedings of the Pattern Recognition: 42nd DAGM German Conference, DAGM GCPR 2020, T\u00fcbingen, Germany.","DOI":"10.1007\/978-3-030-71278-5"},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Gallagher, L., Ravi Kumar, V., Yogamani, S., and McDonald, J.B. (September, January 31). A Hybrid Sparse-Dense Monocular SLAM System for Autonomous Driving. Proceedings of the 2021 European Conference on Mobile Robots (ECMR), Bonn, Germany.","DOI":"10.1109\/ECMR50962.2021.9568797"},{"key":"ref_77","unstructured":"Zhang, T. (2024, July 16). Perception Stack for Indy Autonomous Challenge and Reinforcement Learning in Simulation Autonomous Racing. Technical Report No. UCB\/EECS-2023-187, 2 May 2023. Available online: https:\/\/www2.eecs.berkeley.edu\/Pubs\/TechRpts\/2023\/EECS-2023-187.pdf."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"5543720","DOI":"10.1155\/2021\/5543720","article-title":"Multimedia Concepts on Object Detection and Recognition with F1 Car Simulation Using Convolutional Layers","volume":"2021","author":"Balakrishnan","year":"2021","journal-title":"Wirel. Commun. Mob. Comput."},{"key":"ref_79","unstructured":"Teeti, I., Musat, V., Khan, S., Rast, A., Cuzzolin, F., and Bradley, A. (2022). Vision in adverse weather: Augmentation using CycleGANs with various object detectors for robust perception in autonomous racing. arXiv."},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Katsamenis, I., Karolou, E.E., Davradou, A., Protopapadakis, E., Doulamis, A., Doulamis, N., and Kalogeras, D. (2022). TraCon: A novel dataset for real-time traffic cones detection using deep learning. arXiv.","DOI":"10.1007\/978-3-031-17601-2_37"},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Strobel, K., Zhu, S., Chang, R., and Koppula, S. (2020). Accurate, Low-Latency Visual Perception for Autonomous Racing: Challenges, Mechanisms, and Practical Solutions. arXiv.","DOI":"10.1109\/IROS45743.2020.9341683"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1007\/s10846-021-01329-x","article-title":"Vehicle Odometry with Camera-Lidar-IMU Information Fusion and Factor-Graph Optimization","volume":"101","author":"Peng","year":"2021","journal-title":"J. Intell. Robotic Syst."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"3871","DOI":"10.1109\/TIV.2023.3271624","article-title":"Multi-Modal Sensor Fusion and Object Tracking for Autonomous Racing","volume":"8","author":"Karle","year":"2023","journal-title":"IEEE Trans. Intell. Veh."},{"key":"ref_84","doi-asserted-by":"crossref","unstructured":"Carranza-Garc\u00eda, M., Torres-Mateo, J., Lara-Ben\u00edtez, P., and Garc\u00eda-Guti\u00e9rrez, J. (2021). On the Performance of One-Stage and Two-Stage Object Detectors in Autonomous Vehicles Using Camera Data. Remote Sens., 13.","DOI":"10.3390\/rs13010089"},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Liu, Y., and Diao, S. (2024). An automatic driving trajectory planning approach in complex traffic scenarios based on integrated driver style inference and deep reinforcement learning. PLoS ONE, 19.","DOI":"10.1371\/journal.pone.0297192"},{"key":"ref_86","doi-asserted-by":"crossref","unstructured":"Gupta, P., Isele, D., and Bae, S. (2024). Towards Scalable and Efficient Interaction-Aware Planning in Autonomous Vehicles using Knowledge Distillation. arXiv.","DOI":"10.1109\/IV55156.2024.10588713"},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"126869","DOI":"10.1016\/j.physa.2022.126869","article-title":"Deep encoder\u2013decoder-NN: A deep learning-based autonomous vehicle trajectory prediction and correction model","volume":"593","author":"Hui","year":"2022","journal-title":"Phys. A Stat. Mech. Appl."},{"key":"ref_88","doi-asserted-by":"crossref","unstructured":"Zhang, Z. (2021, January 15\u201317). ResNet-Based Model for Autonomous Vehicles Trajectory Prediction. Proceedings of the 2021 IEEE International Conference on Consumer Electronics and Computer Engineering (ICCECE), Guangzhou, China.","DOI":"10.1109\/ICCECE51280.2021.9342418"},{"key":"ref_89","unstructured":"Ang, M.H., Asama, H., Lin, W., and Foong, S. TridentNet: A Conditional Generative Model for Dynamic Trajectory Generation. Proceedings of the Intelligent Autonomous Systems 16."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"419","DOI":"10.1109\/TIV.2020.3033878","article-title":"VTGNet: A Vision-Based Trajectory Generation Network for Autonomous Vehicles in Urban Environments","volume":"6","author":"Cai","year":"2021","journal-title":"IEEE Trans. Intell. Veh."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"8720","DOI":"10.1109\/TVT.2021.3098429","article-title":"Trajectory Prediction of Preceding Target Vehicles Based on Lane Crossing and Final Points Generation Model Considering Driving Styles","volume":"70","author":"Liu","year":"2021","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_92","doi-asserted-by":"crossref","unstructured":"Zhang, Q., Hu, S., Sun, J., Chen, Q.A., and Mao, Z.M. (2022, January 18\u201324). On Adversarial Robustness of Trajectory Prediction for Autonomous Vehicles. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.01473"},{"key":"ref_93","doi-asserted-by":"crossref","unstructured":"Wang, J., Wang, P., Zhang, C., Su, K., and Li, J. (2021, January 6\u201311). F-Net: Fusion Neural Network for Vehicle Trajectory Prediction in Autonomous Driving. Proceedings of the ICASSP 2021\u20142021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Toronto, ON, Canada.","DOI":"10.1109\/ICASSP39728.2021.9413881"},{"key":"ref_94","doi-asserted-by":"crossref","unstructured":"Qu, L., and Dailey, M.N. (2021). Vehicle Trajectory Estimation Based on Fusion of Visual Motion Features and Deep Learning. Sensors, 21.","DOI":"10.3390\/s21237969"},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1109\/MITS.2021.3049404","article-title":"Vehicle Trajectory Prediction Using LSTMs with Spatial-Temporal Attention Mechanisms","volume":"14","author":"Lin","year":"2022","journal-title":"IEEE Intell. Transp. Syst. Mag."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"4907","DOI":"10.1109\/LRA.2021.3068919","article-title":"Trajectory Prediction in Autonomous Driving With a Lane Heading Auxiliary Loss","volume":"6","author":"Greer","year":"2021","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_97","unstructured":"Faust, A., Hsu, D., and Neumann, G. (2021, January 8\u201311). Learning to Predict Vehicle Trajectories with Model-based Planning. Proceedings of the 5th Conference on Robot Learning, PMLR, London, UK."},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"332","DOI":"10.1016\/j.neucom.2020.03.120","article-title":"TrajVAE: A Variational AutoEncoder model for trajectory generation","volume":"428","author":"Chen","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"3459","DOI":"10.1109\/LRA.2021.3062807","article-title":"Vehicle Trajectory Prediction Using Generative Adversarial Network With Temporal Logic Syntax Tree Features","volume":"6","author":"Li","year":"2021","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1109\/TIV.2022.3155236","article-title":"AI-TP: Attention-based Interaction-aware Trajectory Prediction for Autonomous Driving","volume":"8","author":"Zhang","year":"2023","journal-title":"IEEE Trans. Intell. Veh."},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"17654","DOI":"10.1109\/TITS.2022.3155749","article-title":"Graph-Based Spatial-Temporal Convolutional Network for Vehicle Trajectory Prediction in Autonomous Driving","volume":"23","author":"Sheng","year":"2022","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_102","doi-asserted-by":"crossref","unstructured":"Carrasco, S., Llorca, D., Fern, E., and Sotelo, M.A. (2021, January 11\u201317). SCOUT: Socially-COnsistent and UndersTandable Graph Attention Network for Trajectory Prediction of Vehicles and VRUs. Proceedings of the 2021 IEEE Intelligent Vehicles Symposium (IV), Nagoya, Japan.","DOI":"10.1109\/IV48863.2021.9575874"},{"key":"ref_103","doi-asserted-by":"crossref","unstructured":"Jo, E., Sunwoo, M., and Lee, M. (2021). Vehicle Trajectory Prediction Using Hierarchical Graph Neural Network for Considering Interaction among Multimodal Maneuvers. Sensors, 21.","DOI":"10.3390\/s21165354"},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"412","DOI":"10.1007\/s10489-025-06319-2","article-title":"Robust decision-making for autonomous vehicles via deep reinforcement learning and expert guidance","volume":"55","author":"Li","year":"2025","journal-title":"Appl. Intell."},{"key":"ref_105","doi-asserted-by":"crossref","unstructured":"Liao, H., Li, Z., Shen, H., Zeng, W., Liao, D., Li, G., Li, S.E., and Xu, C. (2023). BAT: Behavior-Aware Human-Like Trajectory Prediction for Autonomous Driving. arXiv.","DOI":"10.24963\/ijcai.2024\/657"},{"key":"ref_106","doi-asserted-by":"crossref","unstructured":"Zhai, F., Xu, H., Chen, C., and Zhang, G. (2023, January 22\u201324). Deep Learning Based Approach for Human-like Driving Trajectory Planning. Proceedings of the 2023 3rd International Conference on Robotics, Automation and Intelligent Control (ICRAIC), Los Alamitos, CA, USA.","DOI":"10.1109\/ICRAIC61978.2023.00075"},{"key":"ref_107","unstructured":"Cai, L., Guan, H., Xu, Q.H., Jia, X., and Zhan, J. (2025). A novel behavior planning for human-like autonomous driving. Proc. Inst. Mech. Eng. Part D J. Automob. Eng., 09544070241310648."},{"key":"ref_108","doi-asserted-by":"crossref","unstructured":"Pan, C., Yaman, B., Nesti, T., Mallik, A., Allievi, A.G., Velipasalar, S., and Ren, L. (2024). VLP: Vision Language Planning for Autonomous Driving. arXiv.","DOI":"10.1109\/CVPR52733.2024.01398"},{"key":"ref_109","doi-asserted-by":"crossref","unstructured":"Cui, C., Ma, Y., Cao, X., Ye, W., and Wang, Z. (2023). Drive as You Speak: Enabling Human-Like Interaction with Large Language Models in Autonomous Vehicles. arXiv.","DOI":"10.1109\/WACVW60836.2024.00101"},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"107052","DOI":"10.1016\/j.ast.2021.107052","article-title":"Explainable Deep Reinforcement Learning for UAV autonomous path planning","volume":"118","author":"He","year":"2021","journal-title":"Aerosp. Sci. Technol."},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"984","DOI":"10.1007\/s11431-020-1729-2","article-title":"An actor-critic based learning method for decision-making and planning of autonomous vehicles","volume":"64","author":"Xu","year":"2021","journal-title":"Sci. China E Technol. Sci."},{"key":"ref_112","doi-asserted-by":"crossref","first-page":"396","DOI":"10.1080\/15472450.2022.2046472","article-title":"Online longitudinal trajectory planning for connected and autonomous vehicles in mixed traffic flow with deep reinforcement learning approach","volume":"27","author":"Cheng","year":"2023","journal-title":"J. Intell. Transp. Syst."},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"17084","DOI":"10.1109\/ACCESS.2021.3053348","article-title":"A Multiagent Deep Reinforcement Learning Approach for Path Planning in Autonomous Surface Vehicles: The Ypacara\u00ed Lake Patrolling Case","volume":"9","author":"Luis","year":"2021","journal-title":"IEEE Access"},{"key":"ref_114","doi-asserted-by":"crossref","unstructured":"Naveed, K.B., Qiao, Z., and Dolan, J.M. (2021, January 19\u201322). Trajectory Planning for Autonomous Vehicles Using Hierarchical Reinforcement Learning. Proceedings of the 2021 IEEE International Intelligent Transportation Systems Conference (ITSC), Indianapolis, IN, USA.","DOI":"10.1109\/ITSC48978.2021.9564634"},{"key":"ref_115","doi-asserted-by":"crossref","first-page":"1142","DOI":"10.1049\/itr2.12081","article-title":"Automatic generation of optimal road trajectory for the rescue vehicle in case of emergency on mountain freeway using reinforcement learning approach","volume":"15","author":"Yang","year":"2021","journal-title":"IET Intell. Transp. Syst."},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"1171","DOI":"10.1109\/OJCOMS.2021.3081996","article-title":"Multi-UAV Path Planning for Wireless Data Harvesting With Deep Reinforcement Learning","volume":"2","author":"Bayerlein","year":"2021","journal-title":"IEEE Open J. Commun. Soc."},{"key":"ref_117","doi-asserted-by":"crossref","unstructured":"Jain, A., and Morari, M. (2020). Computing the racing line using Bayesian optimization. arXiv.","DOI":"10.1109\/CDC42340.2020.9304147"},{"key":"ref_118","doi-asserted-by":"crossref","unstructured":"\u00d6gretmen, L., Chen, M., Pitschi, P., and Lohmann, B. (2024). Trajectory Planning Using Reinforcement Learning for Interactive Overtaking Maneuvers in Autonomous Racing Scenarios. arXiv.","DOI":"10.1109\/ITSC58415.2024.10919549"},{"key":"ref_119","unstructured":"Cleac\u2019h, S.L., Schwager, M., and Manchester, Z. (2020). LUCIDGames: Online Unscented Inverse Dynamic Games for Adaptive Trajectory Prediction and Planning. arXiv."},{"key":"ref_120","doi-asserted-by":"crossref","unstructured":"Karle, P., T\u00f6r\u00f6k, F., Geisslinger, M., and Lienkamp, M. (2022). MixNet: Structured Deep Neural Motion Prediction for Autonomous Racing. arXiv.","DOI":"10.1109\/ACCESS.2023.3303841"},{"key":"ref_121","unstructured":"Ghignone, E., Baumann, N., Boss, M., and Magno, M. (2022). TC-Driver: Trajectory Conditioned Driving for Robust Autonomous Racing\u2014A Reinforcement Learning Approach. arXiv."},{"key":"ref_122","doi-asserted-by":"crossref","unstructured":"Weaver, C., Capobianco, R., Wurman, P.R., Stone, P., and Tomizuka, M. (2024, January 8\u20139). Real-time Trajectory Generation via Dynamic Movement Primitives for Autonomous Racing. Proceedings of the 2024 American Control Conference (ACC), Toronto, ON, Canada.","DOI":"10.23919\/ACC60939.2024.10644244"},{"key":"ref_123","unstructured":"Evans, B., Jordaan, H.W., and Engelbrecht, H.A. (2021). Autonomous Obstacle Avoidance by Learning Policies for Reference Modification. arXiv."},{"key":"ref_124","unstructured":"Thakkar, R.S., Samyal, A.S., Fridovich-Keil, D., Xu, Z., and Topcu, U. (2022). Hierarchical Control for Cooperative Teams in Competitive Autonomous Racing. arXiv."},{"key":"ref_125","doi-asserted-by":"crossref","unstructured":"Trumpp, R., Javanmardi, E., Nakazato, J., Tsukada, M., and Caccamo, M. (2024). RaceMOP: Mapless Online Path Planning for Multi-Agent Autonomous Racing using Residual Policy Learning. arXiv.","DOI":"10.1109\/IROS58592.2024.10801657"},{"key":"ref_126","doi-asserted-by":"crossref","unstructured":"Garlick, S., and Bradley, A. (2021). Real-Time Optimal Trajectory Planning for Autonomous Vehicles and Lap Time Simulation Using Machine Learning. arXiv.","DOI":"10.1080\/00423114.2021.2011929"},{"key":"ref_127","doi-asserted-by":"crossref","unstructured":"Kim, T., Lee, H., Hong, S., and Lee, W. (2022). TOAST: Trajectory Optimization and Simultaneous Tracking using Shared Neural Network Dynamics. arXiv.","DOI":"10.1109\/LRA.2022.3184769"},{"key":"ref_128","doi-asserted-by":"crossref","unstructured":"Chisari, E., Liniger, A., Rupenyan, A., Gool, L.V., and Lygeros, J. (2021). Learning from Simulation, Racing in Reality. arXiv.","DOI":"10.1109\/ICRA48506.2021.9562079"},{"key":"ref_129","doi-asserted-by":"crossref","first-page":"4257","DOI":"10.1109\/LRA.2021.3064284","article-title":"Super-Human Performance in Gran Turismo Sport Using Deep Reinforcement Learning","volume":"6","author":"Fuchs","year":"2021","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_130","doi-asserted-by":"crossref","first-page":"11625","DOI":"10.1109\/LRA.2022.3192770","article-title":"Residual Policy Learning Facilitates Efficient Model-Free Autonomous Racing","volume":"7","author":"Zhang","year":"2022","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_131","unstructured":"Weiss, T., Chrosniak, J., and Behl, M. (August, January 31). Towards multi-agent autonomous racing with the Deepracing framework. Proceedings of the International Conference on Robotics and Automation, Virtual Conference."},{"key":"ref_132","unstructured":"Busch, F.L., Johnson, J., Zhu, E.L., and Borrelli, F. (2022). A Gaussian Process Model for Opponent Prediction in Autonomous Racing. arXiv."},{"key":"ref_133","unstructured":"Br\u00fcdigam, T., Capone, A., Hirche, S., Wollherr, D., and Leibold, M. (2021). Gaussian Process-based Stochastic Model Predictive Control for Overtaking in Autonomous Racing. arXiv."},{"key":"ref_134","unstructured":"Abate, A., Cannon, M., Margellos, K., and Papachristodoulou, A. (2024, January 15\u201317). Balanced reward-inspired reinforcement learning for autonomous vehicle racing. Proceedings of the 6th Annual Learning for Dynamics & Control Conference, PMLR, Oxford, UK."},{"key":"ref_135","unstructured":"Trent Weiss, V.S., and Behl, M. (2020). DeepRacing AI: Agile Trajectory Synthesis for Autonomous Racing. arXiv."},{"key":"ref_136","doi-asserted-by":"crossref","first-page":"108060","DOI":"10.1016\/j.engappai.2024.108060","article-title":"Human-like mechanism deep learning model for longitudinal motion control of autonomous vehicles","volume":"133","author":"Gao","year":"2024","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_137","unstructured":"Renz, K., Chen, L., Marcu, A.M., H\u00fcnermann, J., Hanotte, B., Karnsund, A., Shotton, J., Arani, E., and Sinavski, O. (2024). CarLLaVA: Vision language models for camera-only closed-loop driving. arXiv."},{"key":"ref_138","unstructured":"Elallid, B.B., Bagaa, M., Benamar, N., and Mrani, N. (J. Intell. Transp. Syst., 2024). A reinforcement learning based autonomous vehicle control in diverse daytime and weather scenarios, J. Intell. Transp. Syst., in press."},{"key":"ref_139","doi-asserted-by":"crossref","unstructured":"Yin, Y. (2022, January 14\u201316). Design of Deep Learning Based Autonomous Driving Control Algorithm. Proceedings of the 2022 2nd International Conference on Consumer Electronics and Computer Engineering (ICCECE), Guangzhou, China.","DOI":"10.1109\/ICCECE54139.2022.9712792"},{"key":"ref_140","doi-asserted-by":"crossref","first-page":"20798","DOI":"10.1109\/TITS.2022.3176970","article-title":"Robust Adaptive Learning-Based Path Tracking Control of Autonomous Vehicles Under Uncertain Driving Environments","volume":"23","author":"Li","year":"2022","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_141","doi-asserted-by":"crossref","unstructured":"Xue, H., Zhu, E.L., Dolan, J.M., and Borrelli, F. (2024, January 13\u201317). Learning Model Predictive Control with Error Dynamics Regression for Autonomous Racing. Proceedings of the 2024 IEEE International Conference on Robotics and Automation (ICRA), Yokohama, Japan.","DOI":"10.1109\/ICRA57147.2024.10611628"},{"key":"ref_142","unstructured":"Brunnbauer, A., Berducci, L., Brandst\u00e4tter, A., Lechner, M., Hasani, R.M., Rus, D., and Grosu, R. (2021). Model-based versus Model-free Deep Reinforcement Learning for Autonomous Racing Cars. arXiv."},{"key":"ref_143","doi-asserted-by":"crossref","first-page":"103489","DOI":"10.1016\/j.trc.2021.103489","article-title":"Comfortable and energy-efficient speed control of autonomous vehicles on rough pavements using deep reinforcement learning","volume":"134","author":"Du","year":"2022","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_144","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1109\/TIV.2022.3180337","article-title":"Deep Neural Networks with Koopman Operators for Modeling and Control of Autonomous Vehicles","volume":"8","author":"Xiao","year":"2022","journal-title":"IEEE Trans. Intell. Veh."},{"key":"ref_145","doi-asserted-by":"crossref","unstructured":"F\u00e9nyes, D., N\u00e9meth, B., and G\u00e1sp\u00e1r, P. (2021). A Novel Data-Driven Modeling and Control Design Method for Autonomous Vehicles. Energies, 14.","DOI":"10.3390\/en14020517"},{"key":"ref_146","doi-asserted-by":"crossref","first-page":"651","DOI":"10.1016\/j.neucom.2021.12.056","article-title":"Vision-based neural formation tracking control of multiple autonomous vehicles with visibility and performance constraints","volume":"492","author":"He","year":"2022","journal-title":"Neurocomputing"},{"key":"ref_147","doi-asserted-by":"crossref","first-page":"8493","DOI":"10.1007\/s11227-021-04186-5","article-title":"Energy Optimization for CAN Bus and Media Controls in Electric Vehicles Using Deep Learning Algorithms","volume":"78","author":"Salunkhe","year":"2022","journal-title":"J. Supercomput."},{"key":"ref_148","unstructured":"Wang, W., Xie, J., Hu, C., Zou, H., Fan, J., Tong, W., Wen, Y., Wu, S., Deng, H., and Li, Z. (2023). DriveMLM: Aligning Multi-Modal Large Language Models with Behavioral Planning States for Autonomous Driving. arXiv."},{"key":"ref_149","doi-asserted-by":"crossref","first-page":"13340","DOI":"10.1109\/TVT.2021.3122257","article-title":"A Hybrid Deep Reinforcement Learning For Autonomous Vehicles Smart-Platooning","volume":"70","author":"Prathiba","year":"2021","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_150","doi-asserted-by":"crossref","first-page":"51005","DOI":"10.1109\/ACCESS.2021.3063463","article-title":"Traffic Flow Management of Autonomous Vehicles Using Deep Reinforcement Learning and Smart Rerouting","volume":"9","author":"Mushtaq","year":"2021","journal-title":"IEEE Access"},{"key":"ref_151","doi-asserted-by":"crossref","first-page":"3553","DOI":"10.1007\/s11042-021-11437-3","article-title":"Deep Reinforcement Learning Based Control for Autonomous Vehicles in CARLA","volume":"81","author":"Barea","year":"2022","journal-title":"Multimed. Tools Appl."},{"key":"ref_152","doi-asserted-by":"crossref","first-page":"103192","DOI":"10.1016\/j.trc.2021.103192","article-title":"Space-weighted information fusion using deep reinforcement learning: The context of tactical control of lane-changing autonomous vehicles and connectivity range assessment","volume":"128","author":"Dong","year":"2021","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_153","doi-asserted-by":"crossref","first-page":"100017","DOI":"10.1016\/j.commtr.2021.100017","article-title":"Connected autonomous vehicles for improving mixed traffic efficiency in unsignalized intersections with deep reinforcement learning","volume":"1","author":"Peng","year":"2021","journal-title":"Commun. Transp. Res."},{"key":"ref_154","doi-asserted-by":"crossref","first-page":"43303","DOI":"10.1109\/ACCESS.2022.3167812","article-title":"Deep Reinforcement Learning-Based Driving Strategy for Avoidance of Chain Collisions and Its Safety Efficiency Analysis in Autonomous Vehicles","volume":"10","author":"Muzahid","year":"2022","journal-title":"IEEE Access"},{"key":"ref_155","doi-asserted-by":"crossref","unstructured":"Zheng, L., Son, S., and Lin, M.C. (2023). Traffic-Aware Autonomous Driving with Differentiable Traffic Simulation. arXiv.","DOI":"10.1109\/ICRA48891.2023.10161408"},{"key":"ref_156","unstructured":"Folkestad, C., Wei, S.X., and Burdick, J.W. (2021). Quadrotor Trajectory Tracking with Learned Dynamics: Joint Koopman-based Learning of System Models and Function Dictionaries. arXiv."},{"key":"ref_157","unstructured":"Jain, A., O\u2019Kelly, M., Chaudhari, P., and Morari, M. (2020). BayesRace: Learning to race autonomously using prior experience. arXiv."},{"key":"ref_158","doi-asserted-by":"crossref","unstructured":"Evans, B., Engelbrecht, H.A., and Jordaan, H.W. (2021). From Navigation to Racing: Reward Signal Design for Autonomous Racing. arXiv.","DOI":"10.1109\/ICAR53236.2021.9659438"},{"key":"ref_159","unstructured":"Salvaji, A., Taylor, H., Valencia, D., Gee, T., and Williams, H. (2023). Racing Towards Reinforcement Learning based control of an Autonomous Formula SAE Car. arXiv."},{"key":"ref_160","doi-asserted-by":"crossref","unstructured":"Betz, J., Zheng, H., Liniger, A., Rosolia, U., Karle, P., Behl, M., Krovi, V., and Mangharam, R. (2022). Autonomous Vehicles on the Edge: A Survey on Autonomous Vehicle Racing. arXiv.","DOI":"10.1109\/OJITS.2022.3181510"},{"key":"ref_161","doi-asserted-by":"crossref","unstructured":"Fu, D., Li, X., Wen, L., Dou, M., Cai, P., Shi, B., and Qiao, Y. (2023). Drive Like a Human: Rethinking Autonomous Driving with Large Language Models. arXiv.","DOI":"10.1109\/WACVW60836.2024.00102"},{"key":"ref_162","doi-asserted-by":"crossref","unstructured":"Lee, D., and Liu, J. (2021). End-to-End Deep Learning of Lane Detection and Path Prediction for Real-Time Autonomous Driving. arXiv.","DOI":"10.1007\/s11760-022-02222-2"},{"key":"ref_163","unstructured":"Hwang, J.J., Xu, R., Lin, H., Hung, W.C., Ji, J., Choi, K., Huang, D., He, T., Covington, P., and Sapp, B. (2024). EMMA: End-to-End Multimodal Model for Autonomous Driving. arXiv."},{"key":"ref_164","doi-asserted-by":"crossref","first-page":"6488","DOI":"10.1109\/TTE.2023.3347278","article-title":"An Explainable and Robust Motion Planning and Control Approach for Autonomous Vehicle On-Ramping Merging Task Using Deep Reinforcement Learning","volume":"10","author":"Hu","year":"2024","journal-title":"IEEE Trans. Transp. Electrif."},{"key":"ref_165","doi-asserted-by":"crossref","unstructured":"Kalaria, D., Lin, Q., and Dolan, J.M. (2023). Adaptive Planning and Control with Time-Varying Tire Models for Autonomous Racing Using Extreme Learning Machine. arXiv.","DOI":"10.1109\/ICRA57147.2024.10610848"},{"key":"ref_166","unstructured":"Mammadov, M. (2023). End-to-end Lidar-Driven Reinforcement Learning for Autonomous Racing. arXiv."},{"key":"ref_167","doi-asserted-by":"crossref","unstructured":"Cosner, R.K., Yue, Y., and Ames, A.D. (2022, January 6\u20139). End-to-End Imitation Learning with Safety Guarantees using Control Barrier Functions. Proceedings of the CDC, Cancun, Mexico.","DOI":"10.1109\/CDC51059.2022.9993193"},{"key":"ref_168","doi-asserted-by":"crossref","first-page":"557","DOI":"10.1109\/TIV.2022.3185303","article-title":"End-to-end Autonomous Driving with Semantic Depth Cloud Mapping and Multi-agent","volume":"8","author":"Natan","year":"2023","journal-title":"IEEE Trans. Intell. Veh."},{"key":"ref_169","first-page":"3361","article-title":"Probabilistically Guaranteeing End-to-end Latencies in Autonomous Vehicle Computing Systems","volume":"71","author":"Lee","year":"2022","journal-title":"IEEE Trans. Comput."},{"key":"ref_170","unstructured":"Nair, U.R., Sharma, S., Parihar, U.S., Menon, M.S., and Vidapanakal, S. (2022). Bridging Sim2Real Gap Using Image Gradients for the Task of End-to-End Autonomous Driving. arXiv."},{"key":"ref_171","doi-asserted-by":"crossref","first-page":"7033","DOI":"10.1109\/TVT.2022.3169907","article-title":"Multi-Agent Deep Reinforcement Learning to Manage Connected Autonomous Vehicles at Tomorrow\u2019s Intersections","volume":"71","author":"Antonio","year":"2022","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_172","doi-asserted-by":"crossref","unstructured":"Agarwal, T., Arora, H., and Schneider, J. (2021, January 19\u201322). Learning Urban Driving Policies Using Deep Reinforcement Learning. Proceedings of the 2021 IEEE International Intelligent Transportation Systems Conference (ITSC), Indianapolis, IN, USA.","DOI":"10.1109\/ITSC48978.2021.9564412"},{"key":"ref_173","doi-asserted-by":"crossref","unstructured":"Cui, J., Qiu, H., Chen, D., Stone, P., and Zhu, Y. (2022, January 18\u201324). Coopernaut: End-to-End Driving With Cooperative Perception for Networked Vehicles. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.01674"},{"key":"ref_174","doi-asserted-by":"crossref","first-page":"33771","DOI":"10.1109\/ACCESS.2022.3160655","article-title":"Incremental End-to-End Learning for Lateral Control in Autonomous Driving","volume":"10","author":"Kwon","year":"2022","journal-title":"IEEE Access"},{"key":"ref_175","unstructured":"Schwarting, W., Seyde, T., Gilitschenski, I., Liebenwein, L., Sander, R., Karaman, S., and Rus, D. (2021). Deep Latent Competition: Learning to Race Using Visual Control Policies in Latent Space. arXiv."},{"key":"ref_176","unstructured":"Hsu, B.J., Cao, H.G., Lee, I., Kao, C.Y., Huang, J.B., and Wu, I.C. (2022). Image-Based Conditioning for Action Policy Smoothness in Autonomous Miniature Car Racing with Reinforcement Learning. arXiv."},{"key":"ref_177","doi-asserted-by":"crossref","unstructured":"Cota, J.L., Rodr\u00edguez, J.A.T., Alonso, B.G., and Hurtado, C.V. (2022, January 28\u201331). Roadmap for development of skills in Artificial Intelligence by means of a Reinforcement Learning model using a DeepRacer autonomous vehicle. Proceedings of the 2022 IEEE Global Engineering Education Conference (EDUCON), Tunis, Tunisia.","DOI":"10.1109\/EDUCON52537.2022.9766659"},{"key":"ref_178","doi-asserted-by":"crossref","unstructured":"Huch, S., Sauerbeck, F., and Betz, J. (2023). DeepSTEP\u2013Deep Learning-Based Spatio-Temporal End-To-End Perception for Autonomous Vehicles. arXiv.","DOI":"10.1109\/IV55152.2023.10186768"},{"key":"ref_179","doi-asserted-by":"crossref","unstructured":"Aoki, S., Yamamoto, I., Shiotsuka, D., Inoue, Y., Tokuhiro, K., and Miwa, K. (2023). SuperDriverAI: Towards Design and Implementation for End-to-End Learning-based Autonomous Driving. arXiv.","DOI":"10.1109\/VNC57357.2023.10136277"},{"key":"ref_180","unstructured":"Tian, X., Gu, J., Li, B., Liu, Y., Wang, Y., Zhao, Z., Zhan, K., Jia, P., Lang, X., and Zhao, H. (2024). DriveVLM: The Convergence of Autonomous Driving and Large Vision-Language Models. arXiv."},{"key":"ref_181","unstructured":"Xu, Y., Hu, Y., Zhang, Z., Meyer, G.P., Mustikovela, S.K., Srinivasa, S., Wolff, E.M., and Huang, X. (2024). VLM-AD: End-to-End Autonomous Driving through Vision-Language Model Supervision. arXiv."},{"key":"ref_182","doi-asserted-by":"crossref","first-page":"012006","DOI":"10.1088\/1742-6596\/2284\/1\/012006","article-title":"LIDAR\u2013camera deep fusion for end-to-end trajectory planning of autonomous vehicle","volume":"2284","author":"Zhang","year":"2022","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_183","doi-asserted-by":"crossref","first-page":"2966","DOI":"10.1109\/TITS.2020.3025671","article-title":"Conditional DQN-Based Motion Planning With Fuzzy Logic for Autonomous Driving","volume":"23","author":"Chen","year":"2022","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_184","doi-asserted-by":"crossref","unstructured":"Prakash, A., Chitta, K., and Geiger, A. (2021). Multi-Modal Fusion Transformer for End-to-End Autonomous Driving. arXiv.","DOI":"10.1109\/CVPR46437.2021.00700"},{"key":"ref_185","doi-asserted-by":"crossref","unstructured":"Weiss, T., and Behl, M. (2020, January 9\u201313). DeepRacing: A Framework for Autonomous Racing. Proceedings of the 2020 Design, Automation & Test in Europe Conference & Exhibition (DATE), Grenoble, France.","DOI":"10.23919\/DATE48585.2020.9116486"},{"key":"ref_186","unstructured":"Zheng, H., Betz, J., and Mangharam, R. (2022). Gradient-free Multi-domain Optimization for Autonomous Systems. arXiv."},{"key":"ref_187","doi-asserted-by":"crossref","unstructured":"Song, Y., Lin, H., Kaufmann, E., Duerr, P., and Scaramuzza, D. (2021). Autonomous Overtaking in Gran Turismo Sport Using Curriculum Reinforcement Learning. arXiv.","DOI":"10.1109\/ICRA48506.2021.9561049"},{"key":"ref_188","doi-asserted-by":"crossref","unstructured":"Abrecht, S., Hirsch, A., Raafatnia, S., and Woehrle, M. (IEEE Trans. Intell. Veh., 2024). Deep Learning Safety Concerns in Automated Driving Perception, IEEE Trans. Intell. Veh., early access.","DOI":"10.1109\/TIV.2024.3428415"},{"key":"ref_189","unstructured":"Wang, Y., Jiao, R., Zhan, S.S., Lang, C., Huang, C., Wang, Z., Yang, Z., and Zhu, Q. (2024). Empowering Autonomous Driving with Large Language Models: A Safety Perspective. arXiv."},{"key":"ref_190","doi-asserted-by":"crossref","unstructured":"Chen, H., Cao, X., Guvenc, L., and Aksun-Guvenc, B. (2024). Deep-Reinforcement-Learning-Based Collision Avoidance of Autonomous Driving System for Vulnerable Road User Safety. Electronics, 13.","DOI":"10.3390\/electronics13101952"},{"key":"ref_191","doi-asserted-by":"crossref","first-page":"4337","DOI":"10.1109\/TITS.2020.3042504","article-title":"Deep Learning-Based Traffic Safety Solution for a Mixture of Autonomous and Manual Vehicles in a 5G-Enabled Intelligent Transportation System","volume":"22","author":"Yu","year":"2021","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_192","doi-asserted-by":"crossref","first-page":"4301","DOI":"10.1109\/TITS.2020.3009223","article-title":"Deep Learning Based Autonomous Vehicle Super Resolution DOA Estimation for Safety Driving","volume":"22","author":"Wan","year":"2021","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_193","doi-asserted-by":"crossref","first-page":"4457","DOI":"10.1109\/TITS.2021.3059261","article-title":"Assessing Trust Level of a Driverless Car Using Deep Learning","volume":"22","author":"Karmakar","year":"2021","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_194","doi-asserted-by":"crossref","first-page":"105479","DOI":"10.1016\/j.ssci.2021.105479","article-title":"Deep learning for autonomous vehicle and pedestrian interaction safety","volume":"145","author":"Zhu","year":"2022","journal-title":"Saf. Sci."},{"key":"ref_195","doi-asserted-by":"crossref","first-page":"4267","DOI":"10.1109\/TITS.2021.3052786","article-title":"Toward Safe and Smart Mobility: Energy-Aware Deep Learning for Driving Behavior Analysis and Prediction of Connected Vehicles","volume":"22","author":"Xing","year":"2021","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_196","unstructured":"Chen, B., Francis, J., Herman, J., Oh, J., Nyberg, E., and Herbert, S.L. (2021). Safety-aware Policy Optimisation for Autonomous Racing. arXiv."},{"key":"ref_197","doi-asserted-by":"crossref","first-page":"7262","DOI":"10.1109\/LRA.2021.3097345","article-title":"Vision-Based Autonomous Car Racing Using Deep Imitative Reinforcement Learning","volume":"6","author":"Cai","year":"2021","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_198","doi-asserted-by":"crossref","first-page":"7635","DOI":"10.1109\/LRA.2021.3097073","article-title":"A Predictive Safety Filter for Learning-Based Racing Control","volume":"6","author":"Tearle","year":"2021","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_199","doi-asserted-by":"crossref","unstructured":"Tranzatto, M., Dharmadhikari, M., Bernreiter, L., Camurri, M., Khattak, S., Mascarich, F., Pfreundschuh, P., Wisth, D., Zimmermann, S., and Kulkarni, M. (2022). Team CERBERUS Wins the DARPA Subterranean Challenge: Technical Overview and Lessons Learned. arXiv.","DOI":"10.1126\/scirobotics.abp9742"}],"container-title":["Smart Cities"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2624-6511\/8\/3\/79\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:27:32Z","timestamp":1760030852000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2624-6511\/8\/3\/79"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,6]]},"references-count":199,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2025,6]]}},"alternative-id":["smartcities8030079"],"URL":"https:\/\/doi.org\/10.3390\/smartcities8030079","relation":{},"ISSN":["2624-6511"],"issn-type":[{"value":"2624-6511","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,6]]}}}