###### Abstract  
Technical route decision making of intelligent driving has always been the focus of attention of automotive enterprises and even the industry. Firstly, this study combs the main technical routes of intelligent driving at different levels from three dimensions: development strategy, intelligence allocation and sensor combination. Then, the methodology of technical component combination is designed to disassemble different technical routes into corresponding technical component combinations. Finally, an improved evaluation model of total cost of ownership of intelligent driving is developed and the total cost of ownership of intelligent driving system under different technical routes is compared. For the development strategy, even if the function superposition can follow some research and development achievements of low-level intelligent driving, scenario-driven is still the option with lower cost and better sustainability. For intelligence allocation, collaborative intelligence can effectively reduce the cost of the vehicle compared with single-vehicle intelligence by up to 46%, but the cost reduction depends on the original on-board hardware. For sensor combination, the multi-source fusion always has the cost advantage compared with vision-only, but the advantage is more obvious in the medium-level and high-level stage of single-vehicle intelligence. intelligent driving; technical routes; combination of technical components; total cost of ownership +
Footnote †: journal: Medical Economics and Control  
make vehicles, roadside infrastructure and cloud collaborate through vehicle-to-everything (V2X) [5], and so on. The improvement of intelligence level is undoubtedly the main evolutionary direction of intelligent driving, but how to choose a technical route that can help products penetrate the market quickly is also a topic worthy of attention by the government and original equipment manufacturers (OEMs). According to the theory of technology diffusion, technological feasibility and legal permission can only help intelligent driving penetrate the market in the early stage [6]. Intelligent driving needs to be widely accepted by consumers if it is to be popularized. Some studies have investigated consumers' willingness to purchase various vehicle automation functions [7]. There are many factors that affect purchase decisions, but almost all studies have emphasized consumers' emphasis on economy. [PERSON] et al. [8] found that people are more sensitive to the purchase price of intelligent driving than other factors such as fuel efficiency, safety or environmental friendliness. According to a multinational survey, Chinese, German and American consumers are willing to pay USD 4600, USD 2900 and USD 3900, respectively, for autonomous driving [9]. If the cost exceeds consumers' willingness to pay, intelligent driving will be difficult to promote quickly. Although many technical routes have the possibility of achieving full self-driving, different technical routes determine the cost competitiveness of future intelligent driving products. For example, Tesla only uses cameras as sensors, which allows it to have stronger price adjustment ability in market competition. Therefore, OEMs must consider the cost characteristics of different technical routes when making technical strategic decisions. Moreover, because the government needs to formulate industrial policies to promote new technologies, it may also show some preference for low-cost technical routes [10]. For these reasons, it is necessary to evaluate the cost of intelligent driving systems under different technical routes. Some scholars have studied the economy of intelligent driving [11; 12; 13; 14; 15; 16; 17]. All these studies adopt the total cost of ownership (TCO) analysis. TCO is the sum of all costs related to buying a car and driving it during the period of owning it. This technique is mainly used to compare the relative economic advantages of different competing technologies. [PERSON] et al.