Effects of nine typical technologies for primary autonomous vehicles on road safety in China

Tan, Hong; Zhao, Fuquan; Liu, Zongwei; Song, Haokun

doi:10.1016/j.isci.2023.106109

Cited by 3 publications

(1 citation statement)

References 21 publications

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“…Te current intelligence schemes of vehicles demonstrate greater diferentiation due to the varying technical routes and capability levels of various companies. With reference to the primary, intermediate, and advanced intelligence scheme commonly adopted in the industry, as well as several published works [40,41], typical vehicle intelligence schemes are presented, as shown in Table 6.…”

Section: Te Cost Evaluation Submodel Of Fleetmentioning

confidence: 99%

Cost Analysis of Vehicle-Road Cooperative Intelligence Solutions for High-Level Autonomous Driving: A Beijing Case Study

Zhu,

Zhao,

Song

et al. 2024

Journal of Advanced Transportation

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The development of the vehicle-road cooperative intelligence can effectively resolve the current technical impediment and cost quandary associated with high-level autonomous driving. Nevertheless, the intelligent infrastructure entails initial deployment costs and ongoing energy consumption and maintenance costs, necessitating a comprehensive and quantitative analysis of the costs of intelligent infrastructure and the corresponding changes in comprehensive costs. The cost evaluation model for the cooperative intelligent system is designed in this paper, considering the corresponding intelligent infrastructure layout scheme for different road types within the technical framework. The intelligent configuration and corresponding cost transfer from roadside to vehicle side under the synergy effect is also analyzed. Using Beijing as a case study, the results indicate that the deployment of intelligent infrastructure will effectively reduce acquisition and usage costs of high-level intelligent vehicles and achieve a greater “reuse” effect by serving more intelligent connected vehicles (ICVs). Compared to the vehicle intelligence, collaborative intelligence will reduce cumulative total costs by more than ¥200 billion from 2023 to 2050, even with the inclusion of intelligent infrastructure’s costs.

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Section: Te Cost Evaluation Submodel Of Fleetmentioning

confidence: 99%

Cost Analysis of Vehicle-Road Cooperative Intelligence Solutions for High-Level Autonomous Driving: A Beijing Case Study

Zhu,

Zhao,

Song

et al. 2024

Journal of Advanced Transportation

Self Cite

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Research on Safety Analysis and Response Methods of Autonomous Driving Technology Based on Artificial Intelligence

Zhu,

Wang,

Wang

et al. 2023

2023 IEEE International Conference on Image Processing and Computer Applications (ICIPCA)

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Research on Vehicle-Road Intelligent Capacity Redistribution and Cost Sharing in the Context of Collaborative Intelligence

Zhu,

Zhao,

Song

et al. 2024

Applied Sciences

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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.

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Effects of nine typical technologies for primary autonomous vehicles on road safety in China

Cited by 3 publications

References 21 publications

Cost Analysis of Vehicle-Road Cooperative Intelligence Solutions for High-Level Autonomous Driving: A Beijing Case Study

Cost Analysis of Vehicle-Road Cooperative Intelligence Solutions for High-Level Autonomous Driving: A Beijing Case Study

Research on Safety Analysis and Response Methods of Autonomous Driving Technology Based on Artificial Intelligence

Research on Vehicle-Road Intelligent Capacity Redistribution and Cost Sharing in the Context of Collaborative Intelligence

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