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Friends, fasten your seatbelts! Today, we won’t discuss gossip, but rather dive into the hardcore and thrilling ultimate showdown of Advanced Driver Assistance System (ADAS) sensors. The three main characters: Camera, LiDAR, and Radar are battling fiercely in the autonomous driving arena.
Who is the king? Who is just a runner-up? Whose future will be completely rewritten by AI?
The Three Sensor Brothers: Each with Unique Skills and Weaknesses
| Key Metrics | Weight | Camera | LiDAR | Radar | Radar+AI (Easter egg later) |
|---|---|---|---|---|---|
| Cost | 20 | 8 | 4 | 6 | 6 |
| Illumination | 10 | 4 | 8 | 8 | 8 |
| Noise | 5 | 8 | 4 | 4 | 4 |
| Range | 15 | 8 | 6 | 6 | 6 |
| Resolution | 5 | 8 | 6 | 4 | 4 |
| Weather | 15 | 6 | 4 | 8 | 8 |
| Velocity | 5 | 6 | 6 | 8 | 8 |
| Height | 5 | 6 | 8 | 4 | 8 |
| Distance | 5 | 4 | 8 | 8 | 8 |
| Classification | 15 | 8 | 8 | 6 | 8 |
| Overall Score | 100 | 690 | 590 | 640 | 700 |
A radar chart is worth a thousand words:
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Camera: Dominates in cost and resolution
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LiDAR: Stands out in distance and height
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Radar: Steady in weather and speed
Unfortunately, none are hexagonal warriors!
Why Does Tesla Insist on ‘Pure Vision’?
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Cost weight is as high as 20: Camera 8 points vs LiDAR 4 points, the savings are all profit.
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Resolution/Classification: High pixel count allows AI algorithms to directly perform target segmentation and semantic understanding.
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Illumination Pain Point: FSD v12 is making a comeback at night with 60 fps high frame rate + software noise reduction.
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Weather Hardship: Heavy rain and fog still cause failures, but Musk bets on the “data flywheel”—using shadow mode + massive mileage to iterate algorithms.
Conclusion: In Tesla’s weighted scoring model, Camera 690 points > Radar 640 points > LiDAR 590 points, the pure vision solution is currently leading.
Radar+AI: From ‘Old Eyes’ to ‘Fire Eyes’
Aptiv’s technical blog directly addresses Radar’s shortcomings:
| Scenario | Traditional Radar | Radar+AI |
|---|---|---|
| Target Classification | 60% accuracy, unable to distinguish between bicycles/motorcycles | 90% accuracy, ML extracts shape + speed + trajectory |
| Height Tracking | ±50 cm error, road signs treated as obstacles | ±10 cm error, multipath + micro-Doppler ML modeling |
With no hardware changes, only algorithm upgrades, the overall score of Radar+AI skyrocketed to 700 points, completing a comeback!
ASPICE: The ‘Iron-faced Judge’ of Sensor Selection
SYS.3 System Architecture Design
| Best Practices | Quality Personnel’s Soul Question |
|---|---|
| SYS.3.BP5 Architecture Assessment | “Why choose Camera instead of Radar+AI? Please provide a cost, distance, and classification accuracy comparison table!” |
| SYS.3.BP3 Hardware Interface | “Has the Camera MIPI latency been measured? Radar CAN-FD packet loss rate <0.01% report, please!” |
| MAN.3 Measurement Management | “Why did the LiDAR point cloud density trend drop by 8% in a week?” |
Staron’s ‘Three Views’ Implementation
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Functional View: SysML Requirement Diagram—”Target classification accuracy >90%”.
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Logical View: UML Sequence Diagram—Radar+AI signal processing link.
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Physical View: UML Deployment Diagram—Sensor layout on the vehicle (front bumper/roof/rearview mirror).
Toolchain: Polarion manages requirement traceability, Jira manages defects, EA for diagramming, ASPICE document auto-generation, programmers no longer go bald!
Sensor Fusion in Regional Architecture: A Three Kingdoms Showdown
| Faction | Solution | Advantages | Disadvantages |
|---|---|---|---|
| Tesla | Pure Vision Camera+AI | Low cost 690 points | Failures in night rain scenarios |
| Weilai/Xiaopeng | Camera+Radar+LiDAR | No dead angles in all scenarios | Exploding costs |
| Aptiv | Radar+AI | 700 points cost-performance dark horse | Insufficient brand presence |
Future Dynamic Game:
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LiDAR costs dropping: Huawei’s 96-line from 2000 USD → 500 USD, scores approaching 640 → 680.
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Camera weather issues: AI rain removal algorithm + infrared fill light, can it increase by another 20 points?
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Radar+AI: Once ASPICE measurement data is validated in mass production, OEMs may collectively find it appealing!
Quality Personnel’s ‘Divine Assistance’ Checklist
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Requirement traceability matrix: One-click generation in Polarion to prevent missed tests.
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Interface test report: MIPI latency, CAN-FD packet loss rate, Ethernet bandwidth bottlenecks.
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Performance trend graphs: Weekly monitoring of Radar classification accuracy, Camera night frame rate.
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AI model iteration frequency: Radar+AI’s ML model OTA cycle? Data closed loop?
Remember: Choosing without data support is just playing tricks!
One-Sentence Summary
Camera is like Liu Bei, low cost and well-liked; LiDAR is like Cao Cao, high precision and high price, controlling the emperor to command the lords; Radar is like Sun Quan, steady against the weather. Once AI enters the scene, the Three Kingdoms turns into a ‘sci-fi movie’
END
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