{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T04:00:42Z","timestamp":1783569642955,"version":"3.55.0"},"reference-count":131,"publisher":"Association for Computing Machinery (ACM)","issue":"8","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Comput. Surv."],"published-print":{"date-parts":[[2026,6,30]]},"abstract":"<jats:p>The rapid advancement of Intelligent Transportation Systems (ITS) has led to a paradigm shift toward the adoption of Connected Autonomous Vehicles (CAVs). In recent years, CAVs have emerged as a prominent research focus due to their potential to reduce road traffic accidents caused by human error, optimize traffic flow, create new economic opportunities, and enhance travel convenience. However, the increasing demand for compute and delay-sensitive applications, such as real-time navigation and sensor data processing, exceeds the capabilities of current onboard vehicle resources. Consequently, task offloading has gained significant attention, allowing certain computational tasks generated by CAVs operations to be offloaded to external cloud or edge servers. The existing review literature has been limited in its focus on task offloading techniques specifically for CAVs architecture. Therefore, this study aims at presenting a comprehensive survey on task offloading in CAVs through a systematic review guided by key research questions. We first provide a technical background and then propose a broad coverage taxonomy of existing literature, analyzing promising solutions such as Machine Learning (ML) and heuristic-based techniques. In addition, we present a taxonomy of execution environments, metrics, and datasets. Finally, we highlight key research challenges and future trends, providing valuable insights for advancing task offloading in CAVs architecture.<\/jats:p>","DOI":"10.1145\/3783984","type":"journal-article","created":{"date-parts":[[2025,12,15]],"date-time":"2025-12-15T11:07:56Z","timestamp":1765796876000},"page":"1-35","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Task Offloading for CAVs Edge Computing Environment: Taxonomy, Critical Review, and Future Road Map"],"prefix":"10.1145","volume":"58","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-8639-6314","authenticated-orcid":false,"given":"Bhoopendra","family":"Kumar","sequence":"first","affiliation":[{"name":"School of Computer Science Engineering and Technology (SCSET), Bennett University","place":["Greater Noida, India"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2488-0092","authenticated-orcid":false,"given":"Aditya","family":"Bhardwaj","sequence":"additional","affiliation":[{"name":"School of Computer Science Engineering and Technology (SCSET), Bennett University","place":["Greater Noida, India"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8666-3961","authenticated-orcid":false,"given":"Dinesh","family":"Prasad Sahu","sequence":"additional","affiliation":[{"name":"School of Computer Science Engineering and Technology (SCSET), Bennett University","place":["Greater Noida, India"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,2,4]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.3030072"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.3390\/smartcities5010022"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jksuci.2022.05.016"},{"key":"e_1_3_1_5_2","doi-asserted-by":"crossref","DOI":"10.1016\/j.future.2024.01.030","article-title":"Road to efficiency: Mobility-driven joint task offloading and resource utilization protocol for connected vehicle networks","author":"Aky\u0131ld\u0131z O\u011fuzhan","year":"2024","unstructured":"O\u011fuzhan Aky\u0131ld\u0131z, Feyza Y\u0131ld\u0131r\u0131m Okay, \u0130brahim K\u00f6k, and Suat \u00d6zdemir. 2024. Road to efficiency: Mobility-driven joint task offloading and resource utilization protocol for connected vehicle networks. Future Generation Computer Systems 156 (2024), 157\u2013167.","journal-title":"Future Generation Computer Systems"},{"issue":"4","key":"e_1_3_1_6_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3625544","article-title":"A survey on collaborative learning for intelligent autonomous systems","volume":"56","author":"Anjos Julio C. S. Dos","year":"2023","unstructured":"Julio C. S. Dos Anjos, Kassiano J. Matteussi, Fernanda C. Orlandi, Jorge L. V. Barbosa, Jorge S\u00e1 Silva, Luiz F. Bittencourt, and Cl\u00e1udio F. R. Geyer. 2023. A survey on collaborative learning for intelligent autonomous systems. Computing Surveys 56, 4 (2023), 1\u201337.","journal-title":"Computing Surveys"},{"key":"e_1_3_1_7_2","volume-title":"Rapid Development of an Autonomous Vehicle for the SAE AutoDrive Challenge II Competition","author":"Ashokkumar Sriram","year":"2024","unstructured":"Sriram Ashokkumar, Anirudh Jayendra, Sam Tobin, Ariel Leykin, Robert Stegeman, Abhiraj Dashora, Bryan Look, Joseph Koenig, Brian Hu, Mason Crooks, et\u00a0al. 2024. Rapid Development of an Autonomous Vehicle for the SAE AutoDrive Challenge II Competition. Technical Report. SAE Technical Paper."},{"key":"e_1_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.vehcom.2025.100880"},{"key":"e_1_3_1_9_2","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1201\/9781003190691-9","volume-title":"Proceedings of the Deep Learning and Its Applications for Vehicle Networks","author":"Aziz HM Abdul","year":"2023","unstructured":"HM Abdul Aziz and Sanjoy Das. 2023. Deep reinforcement learning applications in connected-automated transportation systems. In Proceedings of the Deep Learning and Its Applications for Vehicle Networks. CRC Press, 133\u2013164."},{"key":"e_1_3_1_10_2","article-title":"Adversarial attacks and countermeasures on image classification-based deep learning models in autonomous driving systems: A systematic review","author":"Badjie Bakary","year":"2024","unstructured":"Bakary Badjie, Jos\u00e9 Cec\u00edlio, and Antonio Casimiro. 2024. Adversarial attacks and countermeasures on image classification-based deep learning models in autonomous driving systems: A systematic review. Computing Surveys 57, 1 (2024), 1\u201352.","journal-title":"Computing Surveys"},{"key":"e_1_3_1_11_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.vehcom.2014.05.004"},{"key":"e_1_3_1_12_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3023263"},{"key":"e_1_3_1_13_2","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2018.2821664"},{"key":"e_1_3_1_14_2","article-title":"Reinforcement learning for optimizing delay-sensitive task offloading in vehicular edge-cloud computing","author":"Binh Ta Huu","year":"2023","unstructured":"Ta Huu Binh, Hiep Vo, Binh Minh Nguyen, and Huynh Thi Thanh Binh. 2023. Reinforcement learning for optimizing delay-sensitive task offloading in vehicular edge-cloud computing. IEEE Internet of Things Journal 11, 2 (2023), 2058\u20132069.","journal-title":"IEEE Internet of Things Journal"},{"issue":"3","key":"e_1_3_1_15_2","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1007\/s10664-023-10286-y","article-title":"Machine learning-based test selection for simulation-based testing of self-driving cars software","volume":"28","author":"Birchler Christian","year":"2023","unstructured":"Christian Birchler, Sajad Khatiri, Bill Bosshard, Alessio Gambi, and Sebastiano Panichella. 2023. Machine learning-based test selection for simulation-based testing of self-driving cars software. Empirical Software Engineering 28, 3 (2023), 71.","journal-title":"Empirical Software Engineering"},{"key":"e_1_3_1_16_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3217213"},{"key":"e_1_3_1_17_2","article-title":"Computation offloading and retrieval for vehicular edge computing","volume":"6","author":"Boukerche Azzedine","year":"2020","unstructured":"Azzedine Boukerche and Victor Soto. 2020. Computation offloading and retrieval for vehicular edge computing. ACM Computing Surveys 6 (2020).","journal-title":"ACM Computing Surveys"},{"key":"e_1_3_1_18_2","doi-asserted-by":"publisher","DOI":"10.1109\/TGCN.2022.3159034"},{"issue":"11","key":"e_1_3_1_19_2","doi-asserted-by":"crossref","first-page":"13296","DOI":"10.1109\/TITS.2022.3215523","article-title":"Latency-energy tradeoff in connected autonomous vehicles: A deep reinforcement learning scheme","volume":"24","author":"Budhiraja Ishan","year":"2022","unstructured":"Ishan Budhiraja, Neeraj Kumar, Himanshu Sharma, Mohamed Elhoseny, Yahya Lakys, and Joel J. P. C. Rodrigues. 2022. Latency-energy tradeoff in connected autonomous vehicles: A deep reinforcement learning scheme. IEEE Transactions on Intelligent Transportation Systems 24, 11 (2022), 13296\u201313308.","journal-title":"IEEE Transactions on Intelligent Transportation Systems"},{"key":"e_1_3_1_20_2","doi-asserted-by":"publisher","DOI":"10.1145\/3241737"},{"key":"e_1_3_1_21_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.artint.2023.103923"},{"key":"e_1_3_1_22_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIV.2023.3332675"},{"key":"e_1_3_1_23_2","volume-title":"Adaptive Safety and Cyber Security for Connected and Automated Vehicle System","author":"Chen Hanlin","year":"2021","unstructured":"Hanlin Chen. 2021. Adaptive Safety and Cyber Security for Connected and Automated Vehicle System. Ph. D. Dissertation. Purdue University Graduate School."},{"key":"e_1_3_1_24_2","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2018.2815360"},{"key":"e_1_3_1_25_2","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2021.3050804"},{"issue":"1","key":"e_1_3_1_26_2","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1186\/s13638-021-01984-6","article-title":"Research on task-offloading decision mechanism in mobile edge computing-based internet of vehicle","volume":"2021","author":"Cheng Jun","year":"2021","unstructured":"Jun Cheng and Dejun Guan. 2021. Research on task-offloading decision mechanism in mobile edge computing-based internet of vehicle. EURASIP Journal on Wireless Communications and Networking 2021, 1 (2021), 101.","journal-title":"EURASIP Journal on Wireless Communications and Networking"},{"key":"e_1_3_1_27_2","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.3001218"},{"issue":"22","key":"e_1_3_1_28_2","doi-asserted-by":"crossref","first-page":"4974","DOI":"10.3390\/s19224974","article-title":"Task offloading based on lyapunov optimization for mec-assisted vehicular platooning networks","volume":"19","author":"Cui Taiping","year":"2019","unstructured":"Taiping Cui, Yuyu Hu, Bin Shen, and Qianbin Chen. 2019. Task offloading based on lyapunov optimization for mec-assisted vehicular platooning networks. Sensors 19, 22 (2019), 4974.","journal-title":"Sensors"},{"key":"e_1_3_1_29_2","article-title":"Intelligent delay-aware partial computing task offloading for multi-user industrial Internet of Things through edge computing","author":"Deng Xiaoheng","year":"2021","unstructured":"Xiaoheng Deng, Jian Yin, Peiyuan Guan, Neal N. Xiong, Lan Zhang, and Shahid Mumtaz. 2021. Intelligent delay-aware partial computing task offloading for multi-user industrial Internet of Things through edge computing. IEEE Internet of Things Journal 10, 4 (2021), 2954\u20132966.","journal-title":"IEEE Internet of Things Journal"},{"key":"e_1_3_1_30_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2024.108350"},{"key":"e_1_3_1_31_2","article-title":"Deep reinforcement learning-based task offloading and resource allocation for industrial IoT in MEC federation system","author":"Do Huong Mai","year":"2023","unstructured":"Huong Mai Do, Tuan Phong Tran, and Myungsik Yoo. 2023. Deep reinforcement learning-based task offloading and resource allocation for industrial IoT in MEC federation system. IEEE Access 11 (2023), 83150\u201383170.","journal-title":"IEEE Access"},{"issue":"9","key":"e_1_3_1_32_2","doi-asserted-by":"crossref","first-page":"2914","DOI":"10.3390\/s21092914","article-title":"An algorithm to minimize energy consumption and elapsed time for iot workloads in a hybrid architecture","volume":"21","author":"Anjos Julio C. S. Dos","year":"2021","unstructured":"Julio C. S. Dos Anjos, Jo\u00e3o L. G. Gross, Kassiano J. Matteussi, Gabriel V. Gonz\u00e1lez, Valderi R. Q. Leithardt, and Claudio F. R. Geyer. 2021. An algorithm to minimize energy consumption and elapsed time for iot workloads in a hybrid architecture. Sensors 21, 9 (2021), 2914.","journal-title":"Sensors"},{"key":"e_1_3_1_33_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.simpat.2024.103001"},{"key":"e_1_3_1_34_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2021.108228"},{"key":"e_1_3_1_35_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11276-021-02554-w"},{"key":"e_1_3_1_36_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00957"},{"key":"e_1_3_1_37_2","volume-title":"Self-driving Cars: The New Way Forward","author":"Fallon Michael","year":"2018","unstructured":"Michael Fallon. 2018. Self-driving Cars: The New Way Forward. Twenty-First Century Books\u2122."},{"key":"e_1_3_1_38_2","article-title":"Towards interactive and learnable cooperative driving automation: A large language model-driven decision-making framework","author":"Fang Shiyu","year":"2025","unstructured":"Shiyu Fang, Jiaqi Liu, Mingyu Ding, Yiming Cui, Chen Lv, Peng Hang, and Jian Sun. 2025. Towards interactive and learnable cooperative driving automation: A large language model-driven decision-making framework. IEEE Transactions on Vehicular Technology 74, 8 (2025), 11894\u201311905.","journal-title":"IEEE Transactions on Vehicular Technology"},{"key":"e_1_3_1_39_2","article-title":"Cooperative control model using reinforcement learning for connected and automated vehicles and traffic signal light at signalized intersections","author":"Fang Shan","year":"2025","unstructured":"Shan Fang, Lan Yang, Wen-long Shang, Xiangmo Zhao, Fengze Li, and Washington Ochieng. 2025. Cooperative control model using reinforcement learning for connected and automated vehicles and traffic signal light at signalized intersections. IEEE Internet of Things Journal 12, 21 (2025), 44037\u201344050.","journal-title":"IEEE Internet of Things Journal"},{"key":"e_1_3_1_40_2","doi-asserted-by":"publisher","DOI":"10.1109\/MCOM.002.2200266"},{"key":"e_1_3_1_41_2","article-title":"Ppo2: Location privacy-oriented task offloading to edge computing using reinforcement learning for intelligent autonomous transport systems","author":"Gao Honghao","year":"2022","unstructured":"Honghao Gao, Wanqiu Huang, Tong Liu, Yuyu Yin, and Youhuizi Li. 2022. Ppo2: Location privacy-oriented task offloading to edge computing using reinforcement learning for intelligent autonomous transport systems. IEEE Transactions on Intelligent Transportation Systems 24, 7 (2022), 7599\u20137612.","journal-title":"IEEE Transactions on Intelligent Transportation Systems"},{"key":"e_1_3_1_42_2","article-title":"Com-DDPG: Task offloading based on multiagent reinforcement learning for information-communication-enhanced mobile edge computing in the internet of vehicles","author":"Gao Honghao","year":"2023","unstructured":"Honghao Gao, Xuejie Wang, Wei Wei, Anwer Al-Dulaimi, and Yueshen Xu. 2023. Com-DDPG: Task offloading based on multiagent reinforcement learning for information-communication-enhanced mobile edge computing in the internet of vehicles. IEEE Transactions on Vehicular Technology 73, 1 (2023), 348\u2013361.","journal-title":"IEEE Transactions on Vehicular Technology"},{"key":"e_1_3_1_43_2","doi-asserted-by":"publisher","DOI":"10.1145\/3098208.3098210"},{"key":"e_1_3_1_44_2","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1007\/978-3-030-65310-1_8","volume-title":"Service-Oriented Computing: 18th International Conference, ICSOC 2020, Dubai, United Arab Emirates, December 14\u201317, 2020, Proceedings 18","author":"Gross Jo\u00e3o Luiz Grave","year":"2020","unstructured":"Jo\u00e3o Luiz Grave Gross, Kassiano Jos\u00e9 Matteussi, Julio C. S. dos Anjos, and Cl\u00e1udio Fernando Resin Geyer. 2020. A dynamic cost model to minimize energy consumption and processing time for iot tasks in a mobile edge computing environment. In Service-Oriented Computing: 18th International Conference, ICSOC 2020, Dubai, United Arab Emirates, December 14\u201317, 2020, Proceedings 18. Springer, 101\u2013109."},{"key":"e_1_3_1_45_2","article-title":"Advances in autonomous vehicle testing: The state of the art and future outlook on driving datasets, simulators, and proving grounds","author":"Guo Ao","year":"2024","unstructured":"Ao Guo, Jun Huang, Chen Lv, Long Chen, and Fei-Yue Wang. 2024. Advances in autonomous vehicle testing: The state of the art and future outlook on driving datasets, simulators, and proving grounds. Authorea Preprints (2024), 1\u201364.","journal-title":"Authorea Preprints"},{"key":"e_1_3_1_46_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.vehcom.2022.100551"},{"key":"e_1_3_1_47_2","doi-asserted-by":"publisher","DOI":"10.1145\/3321408.3321586"},{"key":"e_1_3_1_48_2","doi-asserted-by":"publisher","DOI":"10.1145\/3555802"},{"key":"e_1_3_1_49_2","first-page":"1","volume-title":"Proceedings of the 2020 IEEE International Conference on Edge Computing","author":"Huang Xinyu","year":"2020","unstructured":"Xinyu Huang, Lijun He, and Wanyue Zhang. 2020. Vehicle speed aware computing task offloading and resource allocation based on multi-agent reinforcement learning in a vehicular edge computing network. In Proceedings of the 2020 IEEE International Conference on Edge Computing. IEEE, 1\u20138."},{"key":"e_1_3_1_50_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3174548"},{"key":"e_1_3_1_51_2","doi-asserted-by":"crossref","first-page":"100156","DOI":"10.1016\/j.geits.2024.100156","article-title":"A review on reinforcement learning-based highway autonomous vehicle control","author":"Irshayyid Ali","year":"2024","unstructured":"Ali Irshayyid, Jun Chen, and Guojiang Xiong. 2024. A review on reinforcement learning-based highway autonomous vehicle control. Green Energy and Intelligent Transportation 3, 4 (2024), 100156.","journal-title":"Green Energy and Intelligent Transportation"},{"key":"e_1_3_1_52_2","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2021.3098022"},{"key":"e_1_3_1_53_2","doi-asserted-by":"publisher","DOI":"10.1145\/3659099"},{"key":"e_1_3_1_54_2","unstructured":"Roger Kalliom\u00e4ki. 2019. Real-time object detection for autonomous vehicles using deep learning."},{"issue":"1","key":"e_1_3_1_55_2","first-page":"41","article-title":"Evaluation of toyota\u2019s strategy for connected cars\u2013based on the theory of dynamic managerial capabilities\u2013","volume":"12","author":"Kawai Tadahiko","year":"2020","unstructured":"Tadahiko Kawai. 2020. Evaluation of toyota\u2019s strategy for connected cars\u2013based on the theory of dynamic managerial capabilities\u2013. Journal of Strategic Management Studies 12, 1 (2020), 41\u201357.","journal-title":"Journal of Strategic Management Studies"},{"issue":"2","key":"e_1_3_1_56_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3485767","article-title":"Level-5 autonomous driving\u2013are we there yet? a review of research literature","volume":"55","author":"Khan Manzoor Ahmed","year":"2022","unstructured":"Manzoor Ahmed Khan, Hesham El Sayed, Sumbal Malik, Talha Zia, Jalal Khan, Najla Alkaabi, and Henry Ignatious. 2022. Level-5 autonomous driving\u2013are we there yet? a review of research literature. ACM Computing Surveys 55, 2 (2022), 1\u201338.","journal-title":"ACM Computing Surveys"},{"key":"e_1_3_1_57_2","first-page":"400","volume-title":"Proceedings of the 2024 IEEE 3rd International Conference on Power Electronics, Intelligent Control and Energy Systems","author":"Kumar Bhoopendra","year":"2024","unstructured":"Bhoopendra Kumar, Aditya Bhardwaj, and Dinesh Sahu. 2024. Machine learning techniques for task offloading in connected autonomous vehicles. In Proceedings of the 2024 IEEE 3rd International Conference on Power Electronics, Intelligent Control and Energy Systems. IEEE, 400\u2013405."},{"key":"e_1_3_1_58_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.techfore.2022.122080"},{"key":"e_1_3_1_59_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.phycom.2021.101446"},{"issue":"3","key":"e_1_3_1_60_2","first-page":"3360","article-title":"A federated learning-based edge caching approach for mobile edge computing-enabled intelligent connected vehicles","volume":"24","author":"Li Chunlin","year":"2022","unstructured":"Chunlin Li, Yong Zhang, and Youlong Luo. 2022. A federated learning-based edge caching approach for mobile edge computing-enabled intelligent connected vehicles. IEEE Transactions on Intelligent Transportation Systems 24, 3 (2022), 3360\u20133369.","journal-title":"IEEE Transactions on Intelligent Transportation Systems"},{"key":"e_1_3_1_61_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCCN.2020.3003036"},{"key":"e_1_3_1_62_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2025.126493"},{"key":"e_1_3_1_63_2","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2020.2975749"},{"key":"e_1_3_1_64_2","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2022.3158253"},{"issue":"1","key":"e_1_3_1_65_2","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1186\/s13677-021-00246-6","article-title":"Computation offloading strategy based on deep reinforcement learning for connected and autonomous vehicle in vehicular edge computing","volume":"10","author":"Lin Bing","year":"2021","unstructured":"Bing Lin, Kai Lin, Changhang Lin, Yu Lu, Ziqing Huang, and Xinwei Chen. 2021. Computation offloading strategy based on deep reinforcement learning for connected and autonomous vehicle in vehicular edge computing. Journal of Cloud Computing 10, 1 (2021), 33.","journal-title":"Journal of Cloud Computing"},{"key":"e_1_3_1_66_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jnca.2020.102781"},{"key":"e_1_3_1_67_2","first-page":"44","article-title":"Self-driving cars vs. the world.","author":"Lindsey Joe","year":"2024","unstructured":"Joe Lindsey. 2024. Self-driving cars vs. the world. Popular Mechanics (2024), 44\u201358.","journal-title":"Popular Mechanics"},{"issue":"1","key":"e_1_3_1_68_2","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1186\/s13677-022-00340-3","article-title":"A collaborative computation and dependency-aware task offloading method for vehicular edge computing: A reinforcement learning approach","volume":"11","author":"Liu Guozhi","year":"2022","unstructured":"Guozhi Liu, Fei Dai, Bi Huang, Zhenping Qiang, Shuai Wang, and Lecheng Li. 2022. A collaborative computation and dependency-aware task offloading method for vehicular edge computing: A reinforcement learning approach. Journal of Cloud Computing 11, 1 (2022), 68.","journal-title":"Journal of Cloud Computing"},{"key":"e_1_3_1_69_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2896000"},{"key":"e_1_3_1_70_2","first-page":"244","volume-title":"Proceedings of the 2023 IEEE\/ACM Symposium on Edge Computing","author":"Liu Yongkang","year":"2023","unstructured":"Yongkang Liu, Chianing Wang, and Kentaro Oguchi. 2023. Poster: Towards realistic federated learning evaluations for connected and automated vehicles. In Proceedings of the 2023 IEEE\/ACM Symposium on Edge Computing. IEEE, 244\u2013246."},{"key":"e_1_3_1_71_2","article-title":"Automated vehicle platooning: A two-stage approach based on vehicle-road cooperation","author":"Liu Ziye","year":"2024","unstructured":"Ziye Liu, Chen Chen, Zhiyi Wang, Li Cong, Haitao Lu, Qingqi Pei, and Shaohua Wan. 2024. Automated vehicle platooning: A two-stage approach based on vehicle-road cooperation. IEEE Transactions on Intelligent Vehicles (2024), 1\u201315.","journal-title":"IEEE Transactions on Intelligent Vehicles"},{"key":"e_1_3_1_72_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2897617"},{"issue":"2","key":"e_1_3_1_73_2","first-page":"504","article-title":"Deep reinforcement learning based computation offloading and trajectory planning for multi-UAV cooperative target search","volume":"41","author":"Luo Quyuan","year":"2022","unstructured":"Quyuan Luo, Tom H Luan, Weisong Shi, and Pingzhi Fan. 2022. Deep reinforcement learning based computation offloading and trajectory planning for multi-UAV cooperative target search. IEEE Journal on Selected Areas in Communications 41, 2 (2022), 504\u2013520.","journal-title":"IEEE Journal on Selected Areas in Communications"},{"issue":"3","key":"e_1_3_1_74_2","first-page":"1228","article-title":"Edge computing task offloading for environmental perception of autonomous vehicles in 6g networks","volume":"10","author":"Lv Pin","year":"2022","unstructured":"Pin Lv, Wenbiao Xu, Jiangtian Nie, Yanli Yuan, Chao Cai, Zhe Chen, and Jia Xu. 2022. Edge computing task offloading for environmental perception of autonomous vehicles in 6g networks. IEEE Transactions on Network Science and Engineering 10, 3 (2022), 1228\u20131245.","journal-title":"IEEE Transactions on Network Science and Engineering"},{"key":"e_1_3_1_75_2","doi-asserted-by":"publisher","DOI":"10.1145\/3431231"},{"key":"e_1_3_1_76_2","article-title":"Intelligent resource allocation and task offloading model for IoT applications in fog networks: A game-theoretic approach","author":"Mebrek Adila","year":"2021","unstructured":"Adila Mebrek and Abdulsalam Yassine. 2021. Intelligent resource allocation and task offloading model for IoT applications in fog networks: A game-theoretic approach. IEEE Transactions on Emerging Topics in Computational Intelligence (2021), 1\u201315.","journal-title":"IEEE Transactions on Emerging Topics in Computational Intelligence"},{"key":"e_1_3_1_77_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11390-019-1947-3"},{"issue":"1","key":"e_1_3_1_78_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3485129","article-title":"Vehicular edge computing: Architecture, resource management, security, and challenges","volume":"55","author":"Meneguette Rodolfo","year":"2021","unstructured":"Rodolfo Meneguette, Robson De Grande, Jo Ueyama, Geraldo P. Rocha Filho, and Edmundo Madeira. 2021. Vehicular edge computing: Architecture, resource management, security, and challenges. ACM Computing Surveys 55, 1 (2021), 1\u201346.","journal-title":"ACM Computing Surveys"},{"key":"e_1_3_1_79_2","doi-asserted-by":"publisher","DOI":"10.3390\/machines11020168"},{"key":"e_1_3_1_80_2","unstructured":"Komeil Moghaddasi and Shakiba Rajabi. 2024. Blockchain-enhanced offloading in mobile edge computing: A systematic review and survey of current trends and future directions. 1\u201337. arXiv:2403.05961. Retrieved from https:\/\/arxiv.org\/abs\/2403.05961"},{"key":"e_1_3_1_81_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.rineng.2023.100969"},{"key":"e_1_3_1_82_2","doi-asserted-by":"publisher","DOI":"10.3390\/s22249595"},{"key":"e_1_3_1_83_2","first-page":"1","article-title":"A survey on computation resource allocation in IoT enabled vehicular edge computing","year":"2022","unstructured":"Naren, Abhishek Kumar Gaurav, Nishad Sahu, Abhinash Prasad Dash, GSS Chalapathi, and Vinay Chamola. 2022. A survey on computation resource allocation in IoT enabled vehicular edge computing. Complex and Intelligent Systems 8, 5 (2022), 1\u201323.","journal-title":"Complex and Intelligent Systems"},{"key":"e_1_3_1_84_2","doi-asserted-by":"publisher","DOI":"10.1109\/CloudNet51028.2020.9335809"},{"key":"e_1_3_1_85_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3167641"},{"key":"e_1_3_1_86_2","unstructured":"NMSC. [n.d.]. Autonomous Vehicle Market. Retrieved September 20 2024 from https:\/\/www.nextmsc.com\/report\/autonomous-vehicle-market"},{"key":"e_1_3_1_87_2","volume-title":"National Highway Traffic Safety Administration (NHTSA)","author":"automation Level of","year":"2024","unstructured":"Level of automation. 2024. National Highway Traffic Safety Administration (NHTSA). Retrieved April 20, 2024 from https:\/\/www.nhtsa.gov\/"},{"key":"e_1_3_1_88_2","doi-asserted-by":"publisher","DOI":"10.1145\/3594541"},{"key":"e_1_3_1_89_2","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-023-00701-y"},{"key":"e_1_3_1_90_2","doi-asserted-by":"crossref","first-page":"101147","DOI":"10.1016\/j.iot.2024.101147","article-title":"Towards optimal edge resource utilization: Predictive analytics and reinforcement learning for task offloading","author":"Pradhan Srikanta","year":"2024","unstructured":"Srikanta Pradhan, Somanath Tripathy, and Rakesh Matam. 2024. Towards optimal edge resource utilization: Predictive analytics and reinforcement learning for task offloading. Internet of Things 26 (2024), 101147.","journal-title":"Internet of Things"},{"issue":"5","key":"e_1_3_1_91_2","doi-asserted-by":"crossref","first-page":"3234","DOI":"10.1109\/TNSE.2021.3103124","article-title":"Federated learning empowered computation offloading and resource management in 6G-V2X","volume":"9","author":"Prathiba Sahaya Beni","year":"2021","unstructured":"Sahaya Beni Prathiba, Gunasekaran Raja, Sudha Anbalagan, Kapal Dev, Sugeerthi Gurumoorthy, and Atshaya P. Sankaran. 2021. Federated learning empowered computation offloading and resource management in 6G-V2X. IEEE Transactions on Network Science and Engineering 9, 5 (2021), 3234\u20133243.","journal-title":"IEEE Transactions on Network Science and Engineering"},{"key":"e_1_3_1_92_2","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2020.3029864"},{"key":"e_1_3_1_93_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2023.3256522"},{"issue":"1","key":"e_1_3_1_94_2","first-page":"3159762","article-title":"A survey on vehicular edge computing: Architecture, applications, technical issues, and future directions","volume":"2019","author":"Raza Salman","year":"2019","unstructured":"Salman Raza, Shangguang Wang, Manzoor Ahmed, and Muhammad Rizwan Anwar. 2019. A survey on vehicular edge computing: Architecture, applications, technical issues, and future directions. Wireless Communications and Mobile Computing 2019, 1 (2019), 3159762.","journal-title":"Wireless Communications and Mobile Computing"},{"key":"e_1_3_1_95_2","first-page":"1541","volume-title":"Proceedings of the 2024 International Wireless Communications and Mobile Computing","author":"Rzig Insaf","year":"2024","unstructured":"Insaf Rzig, Wael Jafaar, Maha Jebalia, and Sami Tabbane. 2024. Dependency-aware task offloading in cooperative UAV-HAPS-assisted vehicular networks. In Proceedings of the 2024 International Wireless Communications and Mobile Computing. IEEE, 1541\u20131546."},{"key":"e_1_3_1_96_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2021.108177"},{"key":"e_1_3_1_97_2","first-page":"1","volume-title":"Proceedings of the 2020 International Conference on Artificial Intelligence and Signal Processing","author":"Sanil Nischal","year":"2020","unstructured":"Nischal Sanil, V. Rakesh, Rishab Mallapur, and Mohammed Riyaz Ahmed. 2020. Deep learning techniques for obstacle detection and avoidance in driverless cars. In Proceedings of the 2020 International Conference on Artificial Intelligence and Signal Processing. IEEE, 1\u20134."},{"key":"e_1_3_1_98_2","first-page":"012032","volume-title":"Proceedings of the IOP Conference Series: Materials Science and Engineering","author":"Scurt F. B.","year":"2021","unstructured":"F. B. Scurt, T. Vesselenyi, R. C. Tarca, H. Beles, and G. Dragomir. 2021. Autonomous vehicles: Classification, technology and evolution. In Proceedings of the IOP Conference Series: Materials Science and Engineering. IOP Publishing, 012032."},{"key":"e_1_3_1_99_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2022.11.012"},{"key":"e_1_3_1_100_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2023.03.038"},{"key":"e_1_3_1_101_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.vehcom.2019.100182"},{"key":"e_1_3_1_102_2","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2021.3135332"},{"key":"e_1_3_1_103_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.trpro.2020.08.101"},{"key":"e_1_3_1_104_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.tra.2020.03.024"},{"key":"e_1_3_1_105_2","doi-asserted-by":"crossref","DOI":"10.1109\/ACCESS.2024.3363426","article-title":"Differentiable optimization for orchestration: Resource offloading for vehicles in smart cities","author":"Strauss Thilo","year":"2024","unstructured":"Thilo Strauss, Michael Oechsle, and Uwe Bauknecht. 2024. Differentiable optimization for orchestration: Resource offloading for vehicles in smart cities. IEEE Access 12 (2021), 23798\u201323807.","journal-title":"IEEE Access"},{"key":"e_1_3_1_106_2","article-title":"RVEAPE: An approach to computation offloading for connected autonomous vehicles","author":"Su Jian","year":"2023","unstructured":"Jian Su, Jinguo Pan, Xiukai Ruan, and Xuedong Zhang. 2023. RVEAPE: An approach to computation offloading for connected autonomous vehicles. IEEE Transactions on Automation Science and Engineering 21, 3 (2023), 2412\u20132424.","journal-title":"IEEE Transactions on Automation Science and Engineering"},{"key":"e_1_3_1_107_2","article-title":"Deep deterministic policy gradient algorithm: A systematic review","author":"Sumiea Ebrahim Hamid","year":"2024","unstructured":"Ebrahim Hamid Sumiea, Said Jadid Abdulkadir, Hitham Seddig Alhussian, Safwan Mahmood Al-Selwi, Alawi Alqushaibi, Mohammed Gamal Ragab, and Suliman Mohamed Fati. 2024. Deep deterministic policy gradient algorithm: A systematic review. Heliyon 10, 9 (2024), 1\u201326.","journal-title":"Heliyon"},{"key":"e_1_3_1_108_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2965620"},{"key":"e_1_3_1_109_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.adhoc.2021.102639"},{"key":"e_1_3_1_110_2","article-title":"Adaptive prioritization and task offloading in vehicular edge computing through deep reinforcement learning","author":"Uddin Ashab","year":"2024","unstructured":"Ashab Uddin, Ahmed Hamdi Sakr, and Ning Zhang. 2024. Adaptive prioritization and task offloading in vehicular edge computing through deep reinforcement learning. IEEE Transactions on Vehicular Technology 74, 3 (2024), 5038\u20135052.","journal-title":"IEEE Transactions on Vehicular Technology"},{"key":"e_1_3_1_111_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2017.09.020"},{"key":"e_1_3_1_112_2","first-page":"1","volume-title":"Proceedings of the 2018 IEEE Globecom Workshops","author":"Wang Hansong","year":"2018","unstructured":"Hansong Wang, Xi Li, Hong Ji, and Heli Zhang. 2018. Federated offloading scheme to minimize latency in MEC-enabled vehicular networks. In Proceedings of the 2018 IEEE Globecom Workshops. IEEE, 1\u20136."},{"key":"e_1_3_1_113_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2020.3014896"},{"key":"e_1_3_1_114_2","article-title":"Deep reinforcement learning-based computation offloading and power allocation within dynamic platoon network","author":"Wang Lei","year":"2023","unstructured":"Lei Wang, Hongbin Liang, and Dongmei Zhao. 2023. Deep reinforcement learning-based computation offloading and power allocation within dynamic platoon network. IEEE Internet of Things Journal 11, 6 (2023), 10500\u201310512.","journal-title":"IEEE Internet of Things Journal"},{"key":"e_1_3_1_115_2","first-page":"151","volume-title":"Proceedings of the 2023 14th International Conference on Ubiquitous and Future Networks","author":"Wang Zekun","year":"2023","unstructured":"Zekun Wang, Jin Nakazato, Muhammad Asad, Ehsan Javanmardi, and Manabu Tsukada. 2023. Overcoming environmental challenges in CAVs through MEC-based federated learning. In Proceedings of the 2023 14th International Conference on Ubiquitous and Future Networks. IEEE, 151\u2013156."},{"key":"e_1_3_1_116_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2981045"},{"key":"e_1_3_1_117_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.techfore.2021.120852"},{"key":"e_1_3_1_118_2","article-title":"DRL-based pricing-driven for task offloading and dynamic resource in vehicle edge computing","author":"Wu Sijun","year":"2025","unstructured":"Sijun Wu, Liang Yang, Junjie Li, Hongzhi Guo, Ishtiaq Ahmad, Daniel Benevides Da Costa, Hongbo Jiang, and Dusit Niyato. 2025. DRL-based pricing-driven for task offloading and dynamic resource in vehicle edge computing. IEEE Transactions on Mobile Computing 24, 10 (2025), 10389\u201310404.","journal-title":"IEEE Transactions on Mobile Computing"},{"key":"e_1_3_1_119_2","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2022.3227027"},{"issue":"4","key":"e_1_3_1_120_2","doi-asserted-by":"crossref","first-page":"2226","DOI":"10.1109\/TITS.2020.3015210","article-title":"Intelligent task offloading for heterogeneous V2X communications","volume":"22","author":"Xiong Kai","year":"2020","unstructured":"Kai Xiong, Supeng Leng, Chongwen Huang, Chau Yuen, and Yong Liang Guan. 2020. Intelligent task offloading for heterogeneous V2X communications. IEEE Transactions on Intelligent Transportation Systems 22, 4 (2020), 2226\u20132238.","journal-title":"IEEE Transactions on Intelligent Transportation Systems"},{"key":"e_1_3_1_121_2","first-page":"738","volume-title":"Proceedings of the 2022 9th International Conference on Dependable Systems and Their Applications","author":"Yang Guang","year":"2022","unstructured":"Guang Yang, Pengqi Wang, Yuan Shi, Qian Dong, and Weiyu Liu. 2022. A high precision annotation method for vision simulation data of autonomous vehicle. In Proceedings of the 2022 9th International Conference on Dependable Systems and Their Applications. IEEE, 738\u2013745."},{"key":"e_1_3_1_122_2","article-title":"Adaptive testing environment generation for connected and automated vehicles with dense reinforcement learning","author":"Yang Jingxuan","year":"2025","unstructured":"Jingxuan Yang, Ruoxuan Bai, Haoyuan Ji, Yi Zhang, Jianming Hu, and Shuo Feng. 2025. Adaptive testing environment generation for connected and automated vehicles with dense reinforcement learning. IEEE Transactions on Intelligent Transportation Systems 26, 4 (2025), 5135\u20135145.","journal-title":"IEEE Transactions on Intelligent Transportation Systems"},{"key":"e_1_3_1_123_2","first-page":"250","volume-title":"Proceedings of the 2024 7th World Conference on Computing and Communication Technologies","author":"Yang Yang","year":"2024","unstructured":"Yang Yang, Caixing Shao, Jinhui Zuo, and Chunwen Shi. 2024. Energy efficient algorithm for multi-user adaptive edge computing offloading in vehicular networks based on meta reinforcement learning. In Proceedings of the 2024 7th World Conference on Computing and Communication Technologies. IEEE, 250\u2013254."},{"key":"e_1_3_1_124_2","doi-asserted-by":"publisher","DOI":"10.1145\/3558052"},{"issue":"5","key":"e_1_3_1_125_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3403952","article-title":"Traffic efficiency applications over downtown roads: A new challenge for intelligent connected vehicles","volume":"53","author":"Younes Maram Bani","year":"2020","unstructured":"Maram Bani Younes and Azzedine Boukerche. 2020. Traffic efficiency applications over downtown roads: A new challenge for intelligent connected vehicles. ACM Computing Surveys 53, 5 (2020), 1\u201330.","journal-title":"ACM Computing Surveys"},{"issue":"1","key":"e_1_3_1_126_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3603703","article-title":"Reinforcement learning methods for computation offloading: A systematic review","volume":"56","author":"Zabihi Zeinab","year":"2023","unstructured":"Zeinab Zabihi, Amir Masoud Eftekhari Moghadam, and Mohammad Hossein Rezvani. 2023. Reinforcement learning methods for computation offloading: A systematic review. Computing Surveys 56, 1 (2023), 1\u201341.","journal-title":"Computing Surveys"},{"key":"e_1_3_1_127_2","article-title":"Task offloading delay minimization in vehicular edge computing based on vehicle trajectory prediction","author":"Zeng Feng","year":"2024","unstructured":"Feng Zeng, Zheng Zhang, and Jinsong Wu. 2024. Task offloading delay minimization in vehicular edge computing based on vehicle trajectory prediction. Digital Communications and Networks 11, 2 (2024), 537\u2013546.","journal-title":"Digital Communications and Networks"},{"issue":"22","key":"e_1_3_1_128_2","doi-asserted-by":"crossref","first-page":"23224","DOI":"10.1109\/JIOT.2022.3188434","article-title":"Federated-reinforcement-learning-enabled joint communication, sensing, and computing resources allocation in connected automated vehicles networks","volume":"9","author":"Zhang Qixun","year":"2022","unstructured":"Qixun Zhang, Hao Wen, Ying Liu, Shuo Chang, and Zhu Han. 2022. Federated-reinforcement-learning-enabled joint communication, sensing, and computing resources allocation in connected automated vehicles networks. IEEE Internet of Things Journal 9, 22 (2022), 23224\u201323240.","journal-title":"IEEE Internet of Things Journal"},{"key":"e_1_3_1_129_2","article-title":"A survey of computation offloading with task types","author":"Zhang Siqi","year":"2024","unstructured":"Siqi Zhang, Na Yi, and Yi Ma. 2024. A survey of computation offloading with task types. IEEE Transactions on Intelligent Transportation Systems 25, 8 (2024), 8313\u20138333.","journal-title":"IEEE Transactions on Intelligent Transportation Systems"},{"issue":"4","key":"e_1_3_1_130_2","doi-asserted-by":"crossref","first-page":"5808","DOI":"10.1109\/TVT.2023.3334192","article-title":"Latency estimation and computational task offloading in vehicular mobile edge computing applications","volume":"73","author":"Zhang Wenhan","year":"2023","unstructured":"Wenhan Zhang, Mingjie Feng, and Marwan Krunz. 2023. Latency estimation and computational task offloading in vehicular mobile edge computing applications. IEEE Transactions on Vehicular Technology 73, 4 (2023), 5808\u20135823.","journal-title":"IEEE Transactions on Vehicular Technology"},{"key":"e_1_3_1_131_2","article-title":"Crowdsourcing live high definition map via collaborative computation in automotive edge computing","author":"Zhang Yuru","year":"2024","unstructured":"Yuru Zhang, Qiang Liu, Haoxin Wang, Dawei Chen, and Kyungtae Han. 2024. Crowdsourcing live high definition map via collaborative computation in automotive edge computing. IEEE Transactions on Vehicular Technology 73, 9 (2024), 13569\u201313583.","journal-title":"IEEE Transactions on Vehicular Technology"},{"key":"e_1_3_1_132_2","doi-asserted-by":"crossref","first-page":"105555","DOI":"10.1016\/j.clsr.2021.105555","article-title":"China\u2019s self-driving car legislation study","volume":"41","author":"Ziyan Chen","year":"2021","unstructured":"Chen Ziyan and Liu Shiguo. 2021. China\u2019s self-driving car legislation study. Computer Law and Security Review 41 (2021), 105555.","journal-title":"Computer Law and Security Review"}],"container-title":["ACM Computing Surveys"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3783984","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,4]],"date-time":"2026-02-04T12:18:35Z","timestamp":1770207515000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3783984"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,4]]},"references-count":131,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2026,6,30]]}},"alternative-id":["10.1145\/3783984"],"URL":"https:\/\/doi.org\/10.1145\/3783984","relation":{},"ISSN":["0360-0300","1557-7341"],"issn-type":[{"value":"0360-0300","type":"print"},{"value":"1557-7341","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,4]]},"assertion":[{"value":"2024-10-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-12-03","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-02-04","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}