## 6 Conclusions  
This study systematically combed the main technical route alternatives of intelligent driving at different levels. Based on the unique perspective of the disassembly and combination of technical components, the TCO of different technical routes is evaluated. It is found that there are obvious cost differences among different technical routes:  
* At the low-level, the vision-only route has an 11% cost advantage compared with the multi-source fusion route. * At the medium-level with simple scenarios, the scenario-driven strategy saves about 13% TCO compared with the function superposition strategy. Even considering the inheritance of R&D cost of low-level systems under the function superposition strategy, the scenario-driven strategy still has a cost advantage of 10%. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**TCO Composition** & **L-FSV** & **L-FSM** & **M1-SSV** & **M1-SSM** & **H-SSV** & **H-SSM** \\ \hline Hardware & $1547 & $1756 & $2083 & $4530 & $3970 & $6679 \\ Software & $304 & $422 & $462 & $1353 & $923 & $2017 \\ Data Traffic & $29 & $29 & $143 & $143 & $714 & $714 \\ Power Consumption & $94 & $79 & $323 & $420 & $596 & $985 \\ Maintenance & $185 & $218 & $382 & $883 & $979 & $1739 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Cost comparison of different sensor combinations. All costs are shown in USD. * At the medium-level with complex scenarios, the hardware cost of the sensor combination solution with a multi-source fusion route reaches USD 7737, far exceeding the current willingness of consumers in China to pay USD 4600, while the TCO of the vision-only route can be controlled at USD 5400. * At the high-level, collaborative intelligence can save up to 46% of the TCO compared with single-vehicle intelligence, and the reduction of costs depends on the type and quantity of the original on-board hardware. In addition, with the help of collaborative intelligence, the cost difference between vision-only and multi-source fusion routes will be controlled within USD 2000. Based on these findings, this study provides the following suggestions for OEMs to formulate technical strategies and the government to formulate industrial policies:
* In the choice of development strategy, a scenario-driven strategy not only has cost advantages, but also can continuously evolve to a higher level of intelligence. OEMs should shift their development strategy to scenario-driven options as soon as possible and put products on the market to build a data closed loop. At the same time, due to the high cost of medium-level intelligent driving in complex scenarios, whether OEMs should commercialize it in 2025 is a topic worthy of further discussion. * In the choice of intelligence allocation, collaborative intelligence can effectively reduce the TCO compared with single-vehicle intelligence. On the premise that China has announced that it will develop collaborative intelligence, the government should speed up the deployment of infrastructure, the construction of pilot demonstration areas and the improvement of relevant standards and regulations. OEMs should actively seek cross-border cooperation and jointly explore the new value that collaborative intelligence can create, such as traffic safety and travel efficiency, so as to further enhance the economy of collaborative intelligence [45]. * In the choice of sensor combinations, the vision-only route has an obvious cost advantage, but at present, only a few OEMs, such as Tesla, have made some breakthroughs in technology. Therefore, OEMs which lack previous relevant technical experience should avoid blindly switching technical routes. In addition, because collaborative intelligence can effectively narrow the cost gap between vision-only and multi-source fusion routes, OEMs need not worry too much that choosing multi-source fusion will make the product lose its market competitiveness. * Compared with the TCO of medium- and high-level intelligent driving, China consumers' willingness to pay is relatively low at present.