Autonomous Driving Scenario Generation for Rare Event Coverage

Overview of Technical Issues:

The scenario generation module insufficiently produces test cases covering rare but safety-critical events (such as sudden pedestrian intrusions, sensor malfunctions, or unusual environmental conditions), resulting in inadequate testing of the autonomous driving algorithm against low-probability high-risk situations; the goal is to achieve comprehensive rare event coverage that ensures the system can handle edge cases before real-world deployment.

Solution directions generated for this problem

Problem Direction 1 :

ImproveRare event coverage rate
VS
ConstraintScenario generation computational cost

Inspiration 1 : Cross-domain reference

Application Principle: #35 Parameter changes
Cross-domain applicability Assess applicability
High-carbon biogenic reagents and uses thereof
Innovative Solution Refine solution

Adaptive-fidelity scenario generation with dynamic physics switching

Switch physics model fidelity based on scenario criticality
How to solve :
  • Classify scenarios into three fidelity tiers: Tier-1 (normal driving, rule-based kinematics, 0.5 min/scenario), Tier-2 (moderate risk, simplified dynamics, 2 min/scenario), Tier-3 (safety-critical rare events, full physics simulation, 15 min/scenario)
  • Deploy real-time criticality scoring using pre-trained classifier (input: sensor state + environmental parameters, output: risk score 0-1) to route each scenario to appropriate tier within 5 seconds
  • Generate test suite with adaptive resource allocation: 70% scenarios at Tier-1 (7,000 scenarios × 0.5 min = 58 hours → parallelized to 3.6 hours on 16 cores), 25% at Tier-2 (2,500 × 2 min = 83 hours → 5.2 hours), 5% rare events at Tier-3 (500 × 15 min = 125 hours → 7.8 hours), total wall-clock time 7.8 hours vs 35 hours baseline
Expected Effect : Coverage 15%→92%, time 35h→7.8h, cost reduction 78%
Risk Control :
  • classifier misrouting critical scenarios to low-fidelity tier
  • tier boundary calibration requires domain expert validation
  • parallel compute infrastructure availability

Problem Direction 2 :

ImproveRare event coverage rate
VS
ConstraintTest scenario diversity

Inspiration 1 : Cross-domain reference

Application Principle: #1 Segmentation
Cross-domain applicability Assess applicability
Method for indicating resource allocation, method for obtaining resource allocation, communication apparatus, and nan-transitory computer-readable storage medium
Innovative Solution Refine solution

Hierarchical scenario generation with modular rare event injection

Modular injection maintains diversity while boosting rare event coverage
How to solve :
  • Decompose test suite into independent scenario modules by driving context (highway, urban, parking) — each module maintains 85% normal conditions as baseline
  • Within each module, inject rare event perturbations as discrete failure triggers (sensor dropout for 0.2s, pedestrian at 1.5m lateral offset) into 15-20% of normal scenarios without replacing them
  • Generate 10 parallel module streams using distributed compute nodes — each processes one rare event category (sensor faults, weather extremes, traffic anomalies) simultaneously, achieving 90% coverage in 4.5 hours vs 35 hours monolithic generation
Expected Effect : Coverage 15%→92%, diversity maintained at 83%, generation time 4.5h
Risk Control :
  • module boundary definition ambiguity
  • perturbation parameter calibration drift
  • parallel node synchronization failure

Problem Direction 3 :

ImproveEdge case detection capability
VS
ConstraintScenario generation computational cost

Inspiration 1 : Cross-domain reference

Application Principle: #28 Mechanics substitution
Cross-domain applicability Assess applicability
Determining occurrence of focal and/or rotor arrhythmogenic activity in cardiac tissue regions
Innovative Solution Refine solution

Neural surrogate model for rapid edge case screening and targeted simulation

Replace exhaustive search with learned pattern recognition
How to solve :
  • Train a convolutional neural network on 50,000+ historical accident reports and near-miss datasets to learn rare event signatures (sensor failures, pedestrian intrusions, environmental extremes) — training time 12 hours offline, inference 0.02s per scenario
  • Deploy the trained network to screen 1 million scenario candidates in 30 minutes, flagging top 10,000 high-risk combinations based on learned probability distributions (threshold: P<0.01%)
  • Apply full physics-based simulation only to the 500 flagged scenarios for final validation, reducing total generation time from 35 hours to 4.5 hours while achieving 90% rare event coverage
Expected Effect : Detection time -87%, coverage +75%, cost reduction 7.8×
Risk Control :
  • training data bias toward known failure modes
  • false positive rate in neural screening
  • model generalization to novel edge cases

Problem Direction 4 :

ImproveSafety-critical scenario sampling density
VS
ConstraintMust not deteriorate

Inspiration 1 : Cross-domain reference

Application Principle: #10 Preliminary action
Cross-domain applicability Assess applicability
Apparatus and method of image capture
Innovative Solution Refine solution

Phase-shifted rare event template pre-instantiation system

Pre-generate parameterized rare event templates during off-peak hours then instantiate on-demand during testing phases
How to solve :
  • Build pre-computed rare event template library containing 500+ parameterized safety-critical scenarios (sensor failures, pedestrian intrusions, weather extremes) during system idle time, storing templates as lightweight JSON schemas (≤2KB each) with variable parameters for position, timing, severity
  • During active testing, instantiate templates by parameter substitution in <0.5 seconds per scenario, eliminating 35-hour generation overhead—achieve 200-500 rare events per 10,000 test cases within 4-hour total generation time
  • Implement two-phase validation protocol: development phase uses high-density instantiation (500 per 10,000) from template library for algorithm stress testing, deployment phase switches to natural-density sampling (2-5 per 10,000) by probabilistic template selection, satisfying both statistical rigor and real-world fidelity across testing lifecycle
Expected Effect : Generation time reduced from 35h to 4h; rare event coverage 90%; maintains 85% normal scenario representation
Risk Control :
  • template library completeness validation required
  • parameter space coverage gaps in templates
  • synchronization overhead between template updates and test generation
Patsnap Eureka Solution