Research on Vehicle-Road Intelligent Capacity Redistribution and Cost Sharing in the Context of Collaborative Intelligence
Source abstract
Abstract
The vehicle-road collaborative intelligence approach has become an industry consensus. It can efficiently tackle the technical hurdles and reduce the performance requirements and costs of on-board perception and computing devices. There is a need for in-depth quantitative studies to optimize the allocation of vehicle-road intelligent capabilities for collaborative intelligence. However, current research tends to focus more on qualitative analysis, and there is little research on the redistribution of vehicle and roadside intelligent capabilities. In this paper, we present a model for distributing perception and computing capabilities between vehicle-side and roadside, ensuring to meet the needs of various autonomous driving levels. Meanwhile, the collaborative intelligence approach will also introduce the costs of intelligent infrastructure deployment, energy, and maintenance. Different roads have varying scene characteristics and usage intensities. It is necessary to conduct a cost-effectiveness analysis of the intelligent deployment of different road types. A vehicle-road cost allocation model is developed based on the lifecycle traveled distance of vehicles and the lifecycle traffic flow of various roads to evaluate the function-cost effectiveness. Our study presents several vehicle-road intelligent schemes that meet the needs of various autonomous driving levels and selects Beijing for case analysis. The results indicate that primary intelligent infrastructure can reduce the lifecycle cost of the vehicle-side intelligent scheme for intermediate autonomous driving from ¥65,301 to ¥37,703, and advanced intelligent infrastructure can reduce the lifecycle cost for advanced autonomous driving from ¥126,938 to ¥42,180. Considering the distributed cost of vehicle-side and roadside, urban roads in Beijing have higher function-cost effectiveness compared to highways, especially urban expressways, which are expected to generate 43.3 times the vehicle-function-cost benefits after the advanced intelligent upgrades. The corresponding research findings can serve as a reference for city managers to make decisions on intelligent road deployment.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Transportation and Mobility Innovations · Traffic control and management · Transportation Planning and Optimization
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
State Key Laboratory of Intelligent Green Vehicle and Mobility, Tsinghua University, Beijing 100084, China
Tsinghua Automotive Strategy Research Institute, Tsinghua University, Beijing 100084, China
Funding
Beijing Natural Science Foundation
References (46)
Show source-provided references
1. The Impact of Cooperative Adaptive Cruise Control on Traffic-Flow Characteristics10.1109/TITS.2006.884615 ↗
2. Simulating impacts of automated driving behavior and traffic conditions on vehicle emissions10.1016/j.trd.2019.09.020 ↗
3. Traffic capacity implications of automated vehicles mixed with regular vehicles10.1080/15472450.2017.1404680 ↗
4. Impacts of connected and autonomous vehicles with level 2 automation on traffic efficiency and energy consumption10.1155/2023/6348778 ↗
5. Policy and society related implications of automated driving: A review of literature and directions for future research10.1080/15472450.2017.1291351 ↗
6. Engine-map-based predictive fuel-efficient control strategies for a group of connected vehicles10.1007/s42154-018-0042-8 ↗
7. Assessing the potential impacts of connected vehicles: Mobility, environmental, and safety perspectives10.1080/15472450.2015.1062728 ↗
8. Effects of nine typical technologies for primary autonomous vehicles on road safety in China10.1016/j.isci.2023.106109 ↗
9. Evaluating the safety impact of adaptive cruise control in traffic oscillations on freeways10.1016/j.aap.2017.04.025 ↗
10. China Industry Innovation Alliance for the Intelligent and Connected Vehicles (2022). China Intelligent and Connected Vehicles Market Analysis Report in 2022, China Industry Innovation Alliance for the Intelligent and Connected Vehicles.
11. Li, K. (2019). Annual Report on the Development of China’s Intelligent Connected Vehicle Industry, Social Sciences Academic Press.
12. Liu, Z. (2020). Zhao Fuquan’s Insights on the Automotive Industry, China Machine Press.
13. State-of-the-art and technical trends of intelligent and connected vehicles
14. Evaluation of the cost of intelligent upgrades of transportation infrastructure for intelligent connected vehicles
15. Institute for AI Industry Research, Tsinghua University (2022). Key Technologies and Developing Prospect of Vehicle Infrastructure Cooperated Driving (VICAD)2.0, Institute for AI Industry Research, Tsinghua University.
16. Chang, X. (2020). Intelligent and Connected Vehicles’ Cloud Control System and its Control Technology, Tsinghua University.
17. Key Technology of Vehicle-Infrastructure Coordination for High-level Autonomous Driving
18. Distributed and collaborative system to improve traffic conditions using fuzzy logic and V2X communications
19. Optimization of isolated intersection signal timing and trajectory planning under mixed traffic environment: The flexible catalysis of connected and automated vehicles10.1016/j.physa.2024.129668 ↗
20. Development and experiment of an intelligent connected cooperative vehicle infrastructure system based on multiple V2I modes and BWM-IGR method10.1016/j.physa.2024.129498 ↗
21. Robust network sensor location for complete link flow observability under uncertainty10.1016/j.trb.2016.03.006 ↗
22. Methods for multi-type sensor allocations along a freeway corridor10.1109/MITS.2018.2806639 ↗
23. A probabilistic optimization model for allocating freeway sensors10.1016/j.trc.2016.02.015 ↗
24. Liu, X.-F., Zhang, J.-S., Gao, T.-T., Guan, Z.-W., and Song, Y.-Q. (2017, January 29–31). A two-stage traffic sensor location method for low-volume road incident detection. Proceedings of the 2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), Guilin, China.10.1109/FSKD.2017.8393281 ↗
25. A cost-effective strategy for road-side unit placement in vehicular networks10.1109/TCOMM.2012.062512.100550 ↗
26. Aslam, B., Amjad, F., and Zou, C.C. (2012, January 1–4). Optimal roadside units placement in urban areas for vehicular networks. Proceedings of the 2012 IEEE Symposium on Computers and Communications (ISCC), Cappadocia, Turkey.10.1109/ISCC.2012.6249333 ↗
27. Aslam, B., and Zou, C.C. (2011, January 9–12). Optimal roadside units placement along highways. Proceedings of the 2011 IEEE Consumer Communications and Networking Conference (CCNC), Las Vegas, NV, USA.10.1109/CCNC.2011.5766608 ↗
28. Lee, J., and Kim, C.M. (2010, January 23–25). A roadside unit placement scheme for vehicular telematics networks. Proceedings of the International Conference on Advanced Computer Science and Information Technology, Berlin, Germany.10.1007/978-3-642-13577-4_17 ↗
29. Xu, J., Wu, L., Shi, L., Shi, Y., and Zhou, W. (2020, January 13–15). Research on 5G Internet of vehicles facilities based on coherent beamforming. Proceedings of the International Conference on Wireless Algorithms, Systems, and Applications, Qingdao, China.10.1007/978-3-030-59019-2_8 ↗
30. Optimize the communication cost of 5G internet of vehicles through coherent beamforming technology10.1155/2021/6668984 ↗
31. Edge server placement in mobile edge computing10.1016/j.jpdc.2018.06.008 ↗
32. Li, Y., and Wang, S. (2018, January 2–7). An energy-aware edge server placement algorithm in mobile edge computing. Proceedings of the 2018 IEEE International Conference on Edge Computing (EDGE), San Francisco, CA, USA.10.1109/EDGE.2018.00016 ↗
33. Society, C.H.T. (2024, August 16). Classification and Definition for Connected Automated Highway (CAH) Systems, 2019. Available online: https://www.chts.cn/cms_files/filemanager/1389253025/attach/20235/8271b114bea6483c9af6d0e44eb0186e.pdf.
34. The People’s Government of Beijing Municipality (2022). Beijing Statistical Bulletin of National Economic and Social Development in 2022, The People’s Government of Beijing Municipality.
35. 2022 Annual Commuting Monitoring Report for Major Cities in China
36. Cost Analysis of Vehicle-Road Cooperative Intelligence Solutions for High-Level Autonomous Driving: A Beijing Case Study10.1155/2024/6170743 ↗
37. (2021). Taxonomy and Definitions for Terms Related to Driving Automation Systems for On-Road Motor Vehicles (Standard No. ISO/TC 204).
38. Wu, D. (2023). Environment Perception for Autonomous Driving by Fusing Video Images and LiDAR Point Cloud Data, Beijing Jiaotong University.
39. Hu, Y., and Chaojun, Z. (2022). Vehicle Optic Sensor System: ADAS Penetration Accelerates and Vehicle Sensing Growth Accelerates, Huaan Securities.
40. (2022, January 26). Smart city infrastructure and intelligent connected vehicle collaborative development annual research report (2022). Proceedings of the China EV100, Beijing, China.
41. Sensor-layout Optimization of Autonomous Vehicles Based on Perceptual Area Coverage
42. Li, X. (2022). In-Vehicle Computing Platform Industry Chain Comprehensively Disassembled from Chip to Software, Guosen Securities.
43. Zhang, W., Zhao, F., and Liu, Z. (2023, January 25–27). Development Strategies of Intelligent Automotive Industry Under the Background of Increasing Demand for Computational Capacity. Proceedings of the Society of Automotive Engineers (SAE)-China Congress, Shanghai, China.10.1007/978-981-99-1365-7_9 ↗
44. Dickert, C. (2024, August 16). Network Overload? Adding up the Data Produced by Connected Cars. Available online: https://www.visualcapitalist.com/network-overload/.
45. Ministry of Commerce of The People’s Republic of China (2024, August 16). The Standard Regulations of Motor Vehicle Mandatory Scrapping, Available online: https://www.mee.gov.cn/gkml/hbb/gwy/201301/t20130115_245171.htm.
46. Beijing Transport Institute (2023). Beijing Transport Development Annual Report, Beijing Transport Institute.