###### Abstract
[11] introduced self-driving cars into the urban transportation system to calculate the total travel cost and TCO of self-driving cars in the future. There are many similar studies. [PERSON] [12] discussed the influence of urban density and public transportation on the total travel cost and TCO. [PERSON] [13] used dynamic parameters such as the share of self-driving fleets and the demand for self-driving travel in the TCO analysis method. However, these studies all evaluate the cost from the perspective of consumer payment, so the intelligent driving system is packaged and there is no analysis of technical details. In addition, their research boundaries are also vague. These limitations lead to the distortion or idealization of the TCO of intelligent driving, which is seriously inconsistent with the real world. There is also more specific research on technical details. [PERSON] et al. [14] systematically analyzed the composition of TCO of intelligent driving from the perspectives of suppliers, OEMs and consumers, which were mainly divided into acquisition cost and use cost. They also discussed the importance and sensitivity of various costs under different research boundaries. On this basis, by building an intelligent driving system from bottom to top on a battery electric platform and evaluating its TCO, [PERSON] et al. [15] find that, although the acquisition cost of intelligent electric vehicles is higher than that of traditional electric vehicles, the TCO can be reduced by 75%. Their study shows a complete TCO analysis framework and process of intelligent driving, but it only evaluates the cost of a specific intelligent driving solution and compares it with traditional vehicles, rather than analyzing the cost differences within the intelligent driving technical group. [PERSON] [16] compared the TCO of autonomous passenger cars and autonomous commercial vehicles and found that full automation may reduce the TCO of passenger cars by about 30% and trucks by 20%. In his research, the technical solutions of intelligent driving are different due to the vehicle sizes. However, the difference in this technical solution only involves the number and layout of sensors, and it does not involve the dispute of the technical route, which is of little help to guide OEM development. [PERSON] et al. [17] analyzed fourteen kinds of atomic sensing technologies on the vehicle and the roadside of intelligent driving, and obtained the optimal combination solution of atomic sensing technologies under safety constraints through the screening of full permutation combination algorithm. The granularity of this research is refined to the technical components of the vehicle and the roadside. However, the cost analysis in this study only focuses on the life cycle cost of the sensors, ignoring the software cost, use cost and hardware cost of computers, actuators and other components. Moreover, the discussion of different technical solutions in this study is limited to the combination of atomic sensing technologies on the vehicle and the roadside. There is no technical route dispute involving other dimensions. As can be understood from the above literature, previous studies have often paid more attention to the life cycle cost of hardware while ignoring the software and data. It should be emphasized that the value proportion of software in vehicles is increasing. [PERSON] et al. [18] believe that in the future, software components will constitute a central general operating system, which may eventually account for 20-40% of the automobile value. The purpose of this study is to compare the cost of intelligent driving systems under different technical routes from a more objective and accurate technical perspective to provide a quantitative reference for intelligent driving technical route decision making. The remainder of this study is organized as follows. Firstly, it combs the main technical routes of intelligent driving at different levels from different dimensions.