Feature Identification, Solution Disassembly and Cost Comparison of Intelligent Driving under Different Technical Routes
2. Feature Identification of Intelligent Driving Technical Routes
2.1. Development Strategy
2.2. Intelligence Allocation
2.3. Sensor Combination
2.4. Main Technical Routes of Intelligent Driving at Different Levels
3. Solution Disassembly of Intelligent Driving Technical Routes
3.1. Technical Components of Intelligent Driving
3.2. Methodology of Technical Component Combination
3.3. Technical Component Combinations under Different Technical Routes
4. TCO Evaluation Model of Intelligent Driving
4.1. Hardware Cost
4.2. Software Cost
4.3. Power Consumption Cost
4.4. Data Traffic Cost
4.5. Maintenance Cost
5. Results and Discussion
5.1. Cost Comparison of Different Levels
5.2. Cost Comparison of Different Development Strategies
5.3. Cost Comparison of Different Intelligence Allocations
5.4. Cost Comparison of Different Sensor Combinations
5.5. Uncertainty Analysis
- 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%.
- 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.
- 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 .
- 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. OEMs and the government should consciously increase consumers’ willingness to pay for intelligent driving through advertising, popular science and other methods, and may also need to provide appropriate subsidies at the initial stage of market penetration of innovation.
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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|Camera||9*2 MP||5*2 MP||13*2 MP|
|Actuators||1*electric power steering|
1*electric power braking
|1*electric power steering|
1*electric power braking
|Camera (2 MP)||$33, 10 W||$31, 8 W||$30, 6 W|
|Camera (8 MP)||$93, 15 W||$86, 12 W||$78, 10 W|
|Camera (12 MP)||$148, 20 W||$120, 16 W||$93, 12 W|
|MMW (short-range)||$76, 5 W||$73, 4 W||$69, 3 W|
|MMW (long-range)||$140, 12 W||$130, 10 W||$117, 8 W|
|MMW (4D-imaging)||$312, 20 W||$204, 18 W||$121, 16 W|
|Lidar (mechanical)||$2836, 45 W||$1866, 40 W||$1104, 36 W|
|Lidar (hybrid-solid)||$933, 40 W||$760, 35 W||$588, 32 W|
|Lidar (solid-state)||$560, 35 W||$367, 32 W||$216, 30 W|
|MEMS||$3, 1 W||$2.8, 1 W||$2.7, 1 W|
|Ultrasonic radar||$24, 12 W||$23, 11 W||$22, 10 W|
|HD map (m)||$343, 1 W||$279, 1 W||$216, 1 W|
|HD map (dm)||$1104, 2 W||$957, 2 W||$740, 2 W|
|HD map (cm)||$2015, 4 W||$1858, 4 W||$1433, 3 W|
|T-Box (4G-V2X)||$21, 4 W||$17, 4 W||$13, 4 W|
|T-Box (5G-V2X)||$93, 8 W||$61, 8 W||$36, 8 W|
|Electric power steering||$224, 40 W||$215, 40 W||$204, 40 W|
|Electric power braking||$179, 50 W||$172, 50 W||$164, 50 W|
|Wire-controlled steering||$522, 70 W||$482, 70 W||$436, 70 W|
|Wire-controlled braking||$373, 80 W||$343, 80 W||$310, 80 W|
|Computer (<100 TOPS)||$7.5, 1 W/TOPS||$6, 0.4 W/TOPS||$4.5, 0.17 W/TOPS|
|Computer (100–500 TOPS)||$2.8, 1 W/TOPS||$2.2, 0.4 W/TOPS||$1.8, 0.17 W/TOPS|
|Computer (>500 TOPS)||$1.5, 1 W/TOPS||$1.2, 0.4 W/TOPS||$0.9, 0.17 W/TOPS|
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Liu, Z.; Zhang, W.; Tan, H.; Zhao, F. Feature Identification, Solution Disassembly and Cost Comparison of Intelligent Driving under Different Technical Routes. Appl. Sci. 2023, 13, 4361. https://doi.org/10.3390/app13074361
Liu Z, Zhang W, Tan H, Zhao F. Feature Identification, Solution Disassembly and Cost Comparison of Intelligent Driving under Different Technical Routes. Applied Sciences. 2023; 13(7):4361. https://doi.org/10.3390/app13074361Chicago/Turabian Style
Liu, Zongwei, Wang Zhang, Hong Tan, and Fuquan Zhao. 2023. "Feature Identification, Solution Disassembly and Cost Comparison of Intelligent Driving under Different Technical Routes" Applied Sciences 13, no. 7: 4361. https://doi.org/10.3390/app13074361