DongCheDi Autonomous Driving Test Revealed: Deep Analysis of Investment Opportunities in the Smart Driving Sector

Are you missing investment opportunities because you can't understand the development trends of autonomous driving technology? As Haowai Uncle, a practitioner who has been deeply involved in technology investment and financial analysis for many years, I use the latest closed highway accident scenario simulation test data from DongCheDi's 36 vehicle models to provide you with an in-depth analysis of the investment value of the autonomous driving industry chain, helping you find the most promising technology company investment targets.

Important Disclaimer: This article is based on publicly available test data for technical analysis and is for educational purposes only. It does not constitute any investment advice. Investment decisions should be based on individual risk tolerance and professional consultation.

I. Cognitive Reconstruction: Why Traditional Automotive Investment Thinking Makes You Poorer?

1.1 Analysis of Traditional Automotive Investment Misconceptions

Many investors still remain in the traditional automotive manufacturing mindset, focusing on sales volume, production capacity, brand premiums, and other traditional metrics. However, DongCheDi's autonomous driving test results show that intelligent driving technology has become the core factor determining the future competitiveness of automotive brands.

From the test results, differences in technical routes directly lead to huge gaps in product competitiveness. This technological divide is reshaping the value distribution pattern of the entire automotive industry, and traditional investment logic is no longer applicable.

1.2 Reconstruction of Investment Logic in the Era of Autonomous Driving

In the era of autonomous driving, automobiles are transforming from transportation tools to intelligent terminals. Investment logic must also shift from hardware manufacturing to software and algorithmic capabilities. Haowai Uncle believes that the value distribution in the future automotive industry will be reshuffled:

• Technology suppliers will occupy the commanding heights of the value chain, enjoying higher profit margins and valuation multiples
• OEMs will face profit compression, differentiating into technology leaders and contract manufacturing roles
• Software and algorithm companies will enjoy the highest valuation premiums, similar to current tech stock valuations

II. The True Core of the Autonomous Driving Industry Chain: A Technology Supplier-Led Ecosystem

2.1 Analysis of Core Elements in the Industry Chain

The autonomous driving industry chain can be compared to the smartphone industry: if OEMs are "phone brand manufacturers," then chip suppliers are "Qualcomm," algorithm companies are "Google," and sensor manufacturers are "camera suppliers."

Based on DongCheDi test data analysis, core players in the current autonomous driving industry chain include:

Chip Level: NVIDIA dominates absolutely, providing computing power support for multiple brands including XPeng, Li Auto, NIO, Zeekr, and Mercedes-Benz Algorithm Level: Tesla's in-house development, Huawei's HarmonyOS Smart Mobility, and Mobileye form a three-way standoff Integration Capability: Determines the final user experience and safety performance

2.2 In-Depth Analysis of DongCheDi's 36-Vehicle Autonomous Driving Test Results

According to the latest "Comprehensive Performance of 36 Vehicles in Highway Accident Scenario Simulation for Autonomous Driving Assistance" test released by DongCheDi, vehicles are ranked by the number of test scenarios passed:

First Tier (5-6 points): Technology Leaders

• Tesla Model 3 (2023 Autopilot version): 5/6 points - Using self-developed FSD Computer (HW3)
• Tesla Model X (2023 Autopilot version): 5/6 points - Using self-developed FSD Computer (HW3)

Second Tier (3 points): Technology Followers

• WEY Blue Mountain (2025 Smart Driving Max): 3/6 points - Using Coffee Pilot Ultra system + NVIDIA Orin-X chip + Hesai AT128 LiDAR
• XPeng G6 (Max version): 3/6 points - Using dual NVIDIA Orin-X chips + LiDAR
• AITO M9 (2024 Smart Driving version): 3/6 points - Using Huawei ADS 2.0 system
• ZHIJIE R7 (Max version): 3/6 points - Using Huawei technology stack
• DENZA Z9GT EV (Smart Driving version): 3/6 points - Using BYD's "God's Eye" system + BAS 3.0+ technology + BYD9000 custom chip

Third Tier (2 points): Technology Development Stage

• AION RT (2025 Smart Driving version): 2/6 points - Using 126-line LiDAR + NVIDIA Orin-X chip (254 TOPS) + Axera M55H SoC + NDA3.0 advanced smart driving system
• Zeekr 3X (Smart Driving version): 2/6 points - Switching from Mobileye SuperVision to Vast Smart Driving 2.0 system + dual NVIDIA Orin-X chips
• Wenjie L12 (Smart Driving version): 2/6 points - Technology solution to be verified

Fourth Tier (0-1 points): Technology Initial Stage

• Li Auto L6 (Max version): 1/6 points - Using dual NVIDIA Orin-X chips
• XPeng SU7 (Max version): 1/6 points - Using NVIDIA technology solution
• AITO M8 (Smart Driving version): 1/5 points - Using Huawei technology stack

The test covers six core scenarios including highway warning, construction zone response, highway timeout handling, disappearing vehicle recognition, highway entrance merging, and irregular route handling.

Key Insight: Different performances with the same chip platform indicate that algorithm optimization and data accumulation are decisive factors. Particularly noteworthy is that AION RT in the third tier still performs mediocrely despite being equipped with top-tier hardware, while Zeekr 3X is experiencing a technology transition period from Mobileye to NVIDIA platform, all of which directly affect our judgment of investment value in the industry chain.

III. Core Data Analysis: Market Share and Investment Value Analysis of Technology Companies

3.1 NVIDIA: Investment Value of the Smart Driving Chip Hegemon

Among the 36 vehicle models tested by DongCheDi, vehicles using NVIDIA chips include XPeng G6, Li Auto L6, NIO vehicles, Zeekr series, WEY Blue Mountain, AION RT, etc., with a market share exceeding 40%. However, test results show huge performance differences with the same NVIDIA chips, reflecting:

• Hardware Standardization Trend: NVIDIA Orin becomes the industry standard configuration
• Software Differentiation Value: Algorithm optimization becomes core competitiveness
• Enhanced Ecosystem Stickiness: Once NVIDIA is chosen, switching costs are extremely high

From an investment perspective, NVIDIA's technological advantages in the autonomous driving field are reflected in:

• Leading Computing Power: DRIVE Orin provides 254 TOPS computing power, DRIVE Thor will reach 2000 TOPS
• Complete Ecosystem: Full toolchain support from training to inference
• Customer Stickiness: High technology migration costs, strong customer loyalty

3.2 Huawei: Ecosystem Value of HarmonyOS Smart Mobility Alliance

Huawei's relatively stable performance in DongCheDi tests through the AITO brand, with AITO M9 achieving a 3/6 score, significantly outperforming most vehicles using the same NVIDIA solution. Huawei's autonomous driving technology characteristics:

• Full-Stack Self-Development: Complete technology stack from chips to algorithms
• Localization Advantages: Optimized for complex Chinese road conditions
• Ecosystem Integration Capability: Seamless connection with smartphones and smart homes
• Data Closed Loop: Rapid data accumulation through multi-brand cooperation

Since Huawei is not publicly listed, investors can focus on its industry chain partners and related competitor targets.

3.3 BYD: Strong Rise of Self-Developed Technology Route

DENZA Z9GT EV achieved a 3/6 score in DongCheDi tests, demonstrating BYD's self-developed smart driving technology strength. BYD's technology characteristics:

• Full-Stack Self-Development Capability: Complete technology stack from "God's Eye" system to BYD9000 custom chips
• End-to-End Large Model: BAS 3.0+ becomes the world's first "vehicle-wide intelligent electric fusion end-to-end" technology
• Data Accumulation Advantage: Over 3.5 million L2-level smart driving vehicles provide data support
• Cost Control Advantage: Vertical integration reduces supply chain costs

BYD stock is listed on both Hong Kong Stock Exchange and A-share market, providing direct investment channels for investors.

3.4 Mobileye: Challenges and Opportunities in Traditional ADAS Field

From Zeekr 3X's technology route switch, we can see that Mobileye is facing unprecedented competitive pressure. However, it's worth noting:

• Solid Market Foundation: Still the dominant player in the global ADAS market
• Accelerated Technology Upgrades: EyeQ6 series chips significantly improve computing power
• Localization Efforts: Deepening cooperation with Chinese brands like Zeekr

Investment value needs careful assessment, with significant uncertainty during the technology transition period.

3.5 Tesla: Moat Value of Self-Developed Technology Route

Tesla's excellent performance (5/6 score) in DongCheDi tests validates its technical strength. Notably, Tesla is the only pure vision technology route performing excellently in the test, proving the foresight of its technology choice.

Tesla's technological advantages:

• Technology Self-Control: FSD chips are completely self-developed, independent of external suppliers
• Data Flywheel Effect: Over 5 million vehicles worldwide provide training data
• Cost Control Capability: Pure vision solutions cost far less than LiDAR solutions
• Software Subscription Model: FSD subscription model provides continuous revenue streams

It should be noted that stock investment requires comprehensive consideration of technical strength, financial status, market environment, and other multiple factors; single test results do not constitute investment advice.

IV. Practical Investment Strategies: How to Layout Autonomous Driving Industry Chain Investment Portfolio

4.1 Core Target Selection Strategy

Based on DongCheDi test results and industry analysis, autonomous driving investment portfolio construction strategy:

First Tier (Core Focus 40%):

• NVIDIA (NVDA): Chip ecosystem leader with deep technology moats
• Tesla (TSLA): Self-developed technology route validator with highest software integration

Second Tier (Important Allocation 30%):

• BYD (BYD HK/002594 A-share): Outstanding self-developed technology strength with significant vertical integration advantages
• Mobileye (MBLY): Traditional ADAS upgrade route, need to monitor technology transition risks

Third Tier (Satellite Allocation 30%):

• Related ETFs: Such as ARKQ (autonomous driving theme ETF) for risk diversification
• Emerging Suppliers: Focus on development opportunities of domestic chip suppliers like Axera
• Chinese Concept Stocks: NIO, XPeng, Li Auto, etc. (need to monitor technology development differences)

4.2 Investment Timing and Risk Control

Autonomous driving is a long-term trend, but there are uncertainties in technology development and commercialization processes. Recommend adopting the following strategies:

Phased Position Building Strategy:

1. Technology Validation Period (Current-2025): Small positions focusing on technology progress
2. Commercialization Acceleration Period (2025-2027): Adjust positions based on L3-level commercialization progress
3. Scale Popularization Period (2028-2030): Enjoy industry maturity dividends

Risk Control Points:

• Technology routes have uncertainties, avoid single bets
• Regulatory policy changes may affect commercialization pace
• Rapid changes in competitive landscape require continuous tracking

V. Common Investment Misconceptions: Why Blindly Chasing Hot Topics Makes You Fall into Traps

5.1 Analysis of Three Typical Errors

Misconception 1: Only Looking at OEMs While Ignoring Suppliers Many investors only focus on OEMs like NIO and XPeng, ignoring the technology suppliers behind them. DongCheDi tests show huge performance differences among vehicles with the same NVIDIA chips, indicating that suppliers actually have higher bargaining power and technology barriers.

Misconception 2: Absolutizing Single Test Results Any single test has its limitations and test results should not be absolutized. Investment decisions need to comprehensively consider technology trends, business models, financial status, and other multiple factors.

Misconception 3: Ignoring Long-term Evolution of Technology Routes Current technology leaders may not be long-term winners. Technology route choices, cost control capabilities, ecosystem building capabilities, etc., all affect long-term competitive patterns.

5.2 Correct Investment Mindset and Methods

• Long-term Perspective: Autonomous driving is a 10-15 year long-term technology revolution
• Portfolio Thinking: Diversify risks through portfolio investment of single targets
• Continuous Learning: Rapid technology iteration requires constantly updating cognitive frameworks

VI. 2025 Outlook: Key Inflection Point for Autonomous Driving Investment

6.1 Analysis of Expected Technology Breakthroughs

2025 will be a key node for autonomous driving technology:

L3-Level Autonomous Driving Commercialization Year:

• Mercedes-Benz, Zeekr, etc. will launch L3-level mass production models
• Regulatory environment gradually improves, commercialization barriers decrease
• Consumer acceptance increases, market demand releases

Intensified Differentiation of Technology Routes:

• Competition between pure vision solutions (Tesla model) and multi-sensor fusion solutions
• Coexistence of self-developed chips (Tesla, Huawei, NIO, BYD) and general chip platforms
• Intensified competition between traditional suppliers (Mobileye) and emerging players (Axera)

6.2 Coexistence of Investment Opportunities and Risks

Opportunity Side:

• Increased concentration of technology suppliers, leading companies enjoy scale dividends
• Clear trend of software-defined vehicles, increased proportion of software value
• Highlighted data value, data accumulation advantages transform into business moats

Risk Side:

• Technology routes still have uncertainties
• Regulatory policies may affect commercialization pace
• International trade frictions may affect supply chain stability

VII. From Theory to Practice: Progressive Path of Autonomous Driving Investment

7.1 Beginner Action Plan

Step 1: Establish Basic Cognitive Framework

• Follow professional evaluation platforms like DongCheDi and 42Garage
• Learn L0-L5 level classification standards for autonomous driving
• Understand differences between LiDAR, pure vision, multi-sensor fusion, and other technology routes

Step 2: Build Information Tracking System

• Subscribe to financial reports from core companies like NVIDIA, Tesla, BYD
• Follow important industry exhibitions like CES and Shanghai Auto Show
• Track autonomous driving regulatory progress in major countries

Step 3: Small-Scale Trial Investment

• Start with related ETFs like ARKQ or technology theme funds
• Allocate small amounts to core targets like NVIDIA
• Set stop-loss lines to control single target risks

7.2 Advanced Investment Strategy Framework

Establish Multi-Dimensional Evaluation System:

• Technical Indicators: L3/L4 function launch timelines, test mileage data
• Business Indicators: Smart driving option rates, ASP (Average Selling Price) improvements
• Financial Indicators: Related business revenue proportions, gross margin changes

Investment Tools and Platform Recommendations:

• US Stock Investors: Low-fee brokers like Interactive Brokers, Firstrade Securities
• Hong Kong/US Stock Investors: Internet brokers like Snowball Securities, Tiger Brokers
• Research Tools: TradingView - Professional technical analysis tools

Continuous Optimization Strategy:

• Quarterly review of investment portfolio performance, adjust weight allocation
• Annual assessment of technology route changes, reselect targets when necessary
• Maintain learning mindset, focus on emergence of new technologies and new players

Conclusion

Autonomous driving is not only a technological revolution in the automotive industry but also a historic wealth opportunity for our generation of investors. DongCheDi's test data provides valuable industry insights, but true investment success requires us to have long-term vision, professional judgment, and risk awareness.

From test results, we can see that gaps in technical strength are transforming into gaps in business value. Those investors who can identify true moats in the early stages of technological transformation often achieve excess returns. Particularly noteworthy is the rapid breakthrough of Chinese companies like BYD in self-developed technology routes, which is rewriting the global competitive landscape of autonomous driving.

At the same time, we also see challenges during technology transition periods: Zeekr switching from Mobileye to NVIDIA platform, AION RT equipped with top-tier hardware yet performing mediocrely - these all remind us that in autonomous driving investment, technology route selection and execution capabilities are equally important.

Remember: In this trillion-dollar autonomous driving market, choosing the right track and companies with long-term competitiveness is more important than trying to predict short-term stock price fluctuations. We recommend starting today to build your autonomous driving investment knowledge framework and make rational investments based on deep understanding.

Risk Warning: This article is for educational purposes only and does not constitute investment advice. Stock markets involve risks, investment requires caution. Past performance does not represent future performance. Please invest prudently based on your risk tolerance and seek professional investment advisor advice when necessary.

I am Haowai Uncle, focused on interpreting technology investment wisdom with data and logic, helping investors find the right investment direction in the rapidly changing technology wave. If this article helps you, welcome to follow Haowai Uncle for more in-depth investment analysis. On the investment journey, let us think rationally together and seize the opportunities of our times.