## 2 Feature Identification of Intelligent Driving Technical Routes
### Main Technical Routes of Intelligent Driving at Different Levels  
As shown in Figure 1, the prominence of technical controversial points is different at different levels of intelligent driving systems, so the main technical routes of intelligent driving at different levels can be identified.  
For low-level intelligent driving, due to the ease of research and development, most products on the market develop the ADAS based on a function superposition strategy. In the intelligence allocation dimension, because collaborative intelligence technology is still immature, single-vehicle intelligence is still the only choice. Therefore, sensor combination has become the main technical dispute at this stage, and two technical routes of low-level intelligent driving, \"L-FSV\" and \"L-FSM\", are identified.  
For medium-level intelligent driving, conditional automatic driving in simple scenarios, such as parking and closed road cruising, can be realized by adopting the function superposition strategy, but complex scenarios, such as urban road cruising, must be based on the scenario-driven strategy. In the intelligence allocation dimension, the scenario feature of medium-level intelligent driving means that the collaborative intelligence route will lead to frequent degradation of functions, which is extremely unfriendly to the user experience, so single-vehicle intelligence is still the best choice [28]. Similar to the low-level, medium-level intelligent driving is also faced with the choice of using vision-only and multi-source fusion. The difference is that the medium level can use lidar, while the low level usually does not. Therefore, six technical routes of medium-level intelligent driving are identified, including \"M1-FSV\", \"M1-FSM\", \"M1-SSV\", \"M1-SSM\", \"M2-SSV\" and \"M2-SSM\".  
For high-level intelligent driving, the system complexity requires that it must be based on a scenario-driven strategy to solve the long tail problem of most extreme cases through continuous data accumulation [23]. At this stage, considering intelligence allocation has the real application value and there are still differences in sensor combinations between vision-only and multi-source fusion routes. Therefore, four technical routes of high-level intelligent driving are identified, including \"H-SSV\", \"H-SSM\", \"H-SCV\" and \"H-SCM\".  
Behind every technical dispute lies the difference in the TCO of intelligent driving. In the development strategy dimension, each function in function superposition has its own hardware and software, while the scenario-driven option can effectively improve the integration of functions to reduce system redundancy and cost [29]. In the intelligent allocation dimension, collaborative intelligence allows some sensing and decision-making tasks to be transferred to the environment outside the vehicle, so the on-board hardware can be appropriately reduced, and the software may be more complex, which will change the cost. In the sensor combination dimension, the cost of lidar is more expensive than that of the camera, and the type and number of sensors will have a great impact on the cost. Therefore, in order to evaluate the cost difference between different technical routes  
Figure 1: Main technical routes of intelligent driving at different levels.  
more accurately, the specific technical solutions must be disassembled to the components for analysis.