Autonomous Driving Capabilities in Special Scenarios: Challenges and Breakthroughs

According to Waymo's 2023 technical report, while 99% of routine driving scenarios have been mastered by autonomous systems, the remaining 1% of special scenarios account for 47% of intervention requests. These "long-tail problems" represent the final technical barriers to widespread autonomous vehicle (AV) adoption. This article examines three typical edge cases, analyzing current capabilities and future solutions.

1. Unmarked Rural Roads: Pushing Vision Algorithms to the Limit

1.1 Current Performance

Path recognition rate: 68% for vision-only systems (vs. 95% on marked roads)
Average speed: 25 km/h (vs. 50 km/h urban driving)
Common failures: 12% incidence of confusing field boundaries with roads

1.2 Innovative Solutions

Technology	Principle	Improvement	Leading Companies
Multispectral imaging	IR+visible light differentiation	+31% boundary recognition	Mercedes-Benz
Terrain-matching algorithms	Comparing elevation data	+45% path keeping	XPeng
Crowdsourced road memory	Generating virtual lanes from prior vehicle paths	+18 km/h speed	Tesla

2. Construction Zone Detours: Testing Decision-Making Flexibility

2.1 Key Challenges

Sign recognition delay: 3.2s for temporary signs (vs. 0.8s for permanent signs)
Route replanning failure rate: 21% in complex detours
Handover risks: 83% of takeovers occur in final 50 meters

2.2 Tiered Response Strategy

Basic detours (cone-guided)
94% recognition accuracy
Solution: Follow preceding vehicle's path
Intermediate detours (signage + flaggers)
79% recognition accuracy
Solution: Receive digital instructions via V2X
Complex detours (unguided + multiple forks)
52% recognition accuracy
Solution: Remote human assistance

2.3 Technology Trends

Amap's "Construction Brain" platform: Provides 48-hour advance notice for 95% of work zones
4D millimeter-wave radar: Cone detection range extended to 150m (+75%)
Dynamic semantic mapping: Updates detour topology every 30 seconds

3. Automated Parking: Reliability in Controlled Environments

3.1 Full-Process Success Rates

Step Underground Surface Lots Mechanical Spaces
Space detection 98% 99% 89%
Path planning 95% 97% 76%
Final precision (<5cm) 92% 94% 68%
Fully autonomous 88% 91% 54%

Step	Underground	Surface Lots	Mechanical Spaces
Space detection	98%	99%	89%
Path planning	95%	97%	76%
Final precision (<5cm)	92%	94%	68%
Fully autonomous	88%	91%	54%

3.2 Failure Analysis

3.3 Next-Gen Parking Solutions

Memory Parking Pro: Learns frequent parking routes (+23% efficiency - NIO ET7)
Multi-level navigation: Supports 6-floor parking structures (XPeng G9)
Robotic charging arms: Auto-connect after parking (Tesla V4 Superchargers)

Technology Roadmap

Short-term (2024-2026): Reduce LiDAR costs to $100 for cm-level modeling
Medium-term (2027-2030): V2X coverage for 90% of edge cases
Long-term (2030+): Human-like reasoning AI for extreme scenarios

As BMW's AV Director Dr. Huber notes: "The breakthrough for edge cases lies not in sensor quantity, but in graceful degradation when facing uncertainty." With simulation testing expected to exceed 100 billion kilometers in the next three years, we anticipate raising edge-case success rates to over 90% of human-level performance.