How to Benchmark Autonomous Driving Motion Planning Algorithms

Overview of Technical Issues:

Current benchmarking approaches for motion planning algorithms insufficiently evaluate multi-dimensional performance across safety, efficiency, comfort, and rule compliance simultaneously, and test scenarios insufficiently cover critical edge cases and complex traffic interactions found in real-world driving; the goal is to establish a comprehensive benchmarking framework that reliably predicts real-world algorithm performance and identifies failure modes before deployment.

Solution directions generated for this problem

Problem Direction 1 :

ImproveScenario coverage comprehensiveness
VS
ConstraintBenchmark execution time

Inspiration 1 : Cross-domain reference

Application Principle: #1 Segmentation
Cross-domain applicability Assess applicability
A method, terminal, and server for displaying information.
Innovative Solution Refine solution

Hierarchical scenario clustering with parallel execution architecture

Cluster scenarios into independent functional modules for parallel execution
How to solve :
  • Partition 500+ scenarios into 8-10 functional clusters (highway merge, urban intersection, pedestrian crossing, sensor occlusion, parking, emergency response, construction zones, adverse weather) based on traffic interaction patterns and environmental characteristics
  • each cluster runs independently on separate compute nodes with pre-allocated CPU/GPU resources
  • Deploy distributed execution framework using containerized simulation instances — each cluster assigned dedicated hardware (4-core CPU, 8GB RAM minimum per cluster), scenarios within clusters execute sequentially while clusters run in parallel across infrastructure
  • Implement dynamic load balancing — monitor cluster completion rates every 5 minutes, redistribute unfinished scenarios from slow clusters to idle nodes when completion time variance exceeds 15%, ensuring all clusters finish within similar timeframes
  • Pre-generate scenario initialization states offline (vehicle positions, traffic densities, map data) and cache in optimized binary format, reducing per-scenario startup overhead from 45-60 seconds to under 5 seconds
Expected Effect : Execution time reduced to 4-6 hours (70% reduction); scenario coverage 80%+; tolerance ±20 min across clusters
Risk Control :
  • cluster size imbalance causing bottlenecks
  • inter-cluster dependency conflicts
  • node failure mid-execution

Problem Direction 2 :

ImproveScenario coverage comprehensiveness
VS
ConstraintComputational resource consumption

Inspiration 1 : Cross-domain reference

Application Principle: #35 Parameter changes
Cross-domain applicability Assess applicability
Machine learning for determining protein structures
Innovative Solution Refine solution

Adaptive fidelity simulation with scenario-driven resource allocation

Dynamically adjust simulation fidelity per scenario risk level
How to solve :
  • Classify 500+ scenarios into three fidelity tiers: Tier-1 (critical edge cases, 15%) uses full physics simulation with detailed sensor models and vehicle dynamics
  • Tier-2 (moderate complexity, 35%) uses medium-fidelity kinematic models with simplified sensor noise
  • Tier-3 (baseline cases, 50%) uses lightweight geometric collision checking only, reducing GPU load by 8× and memory from 2GB to 250MB per instance
  • Implement risk-based scenario tagging during offline preprocessing: analyze historical failure data to assign risk scores (0-100) to each scenario type, automatically routing scenarios to appropriate fidelity engines—acceptance threshold: Tier-1 ≥85 risk score, Tier-2 50-84, Tier-3 <50, with ±3-point tolerance for boundary cases
  • Deploy parallel execution architecture where Tier-3 scenarios run 20 concurrent instances per GPU, Tier-2 runs 5 instances, Tier-1 runs 1 instance with dedicated resources—total benchmark completes in 4.5-6 hours versus 20+ hours uniform high-fidelity, achieving 80%+ coverage with 3.2× resource efficiency gain compared to current 100-scenario high-fidelity benchmarks
Expected Effect : Coverage 80%+, resource use +220% vs baseline, 65% savings vs uniform approach
Risk Control :
  • risk classification accuracy below 90%
  • fidelity transition artifacts at tier boundaries
  • load balancing inefficiency across heterogeneous tiers

Problem Direction 3 :

ImprovePerformance evaluation dimensionality
VS
ConstraintResult interpretation complexity

Inspiration 1 : Cross-domain reference

Application Principle: #24 Intermediary
Cross-domain applicability Assess applicability
Used for maintenance and diagnosis of refrigeration systems
Innovative Solution Refine solution

Hierarchical metric aggregation dashboard with automated drill-down triggers

Hierarchical dashboard synthesizes multi-dimensional data into interpretable layers
How to solve :
  • Implement three-tier metric hierarchy: Tier-1 displays single composite index (0-100) per scenario category combining weighted safety/efficiency/comfort/rule scores using domain-calibrated weights (safety 40%, efficiency 25%, comfort 20%, compliance 15%)
  • Tier-2 shows four dimensional scores per category as color-coded bar charts with automated threshold flags (red <60, yellow 60-80, green >80)
  • Tier-3 provides raw metric drill-down triggered automatically when any Tier-2 score falls below 70
  • Deploy automated anomaly detection layer using statistical process control: calculate rolling mean and ±2σ bounds for each metric across scenario types, auto-flag outliers exceeding control limits and generate ranked failure list (top 20 critical cases) with one-click access to detailed logs
  • Integrate interactive 3D scatter visualization: plot scenarios as points in safety-efficiency-comfort space, color-code by compliance score, enable click-to-filter by scenario type—engineers identify failure clusters spatially within 5 minutes versus 2 hours manual review
Expected Effect : Interpretation time reduced 75%; 90% of critical issues surfaced automatically in top-20 list; engineers analyze 10 category summaries + flagged outliers instead of 2000 data points
Risk Control :
  • Weight calibration requires domain expert validation across 3 deployment contexts
  • threshold tuning sensitivity to algorithm type variation
  • dashboard refresh latency with real-time 500-scenario updates

Problem Direction 4 :

ImproveFailure mode detection capability
VS
ConstraintBenchmark execution time

Inspiration 1 : Cross-domain reference

Application Principle: #10 Preliminary action
Cross-domain applicability Assess applicability
Immersive design management system
Innovative Solution Refine solution

Pre-cached failure signature library for instant anomaly detection

Offline pre-compute failure signatures
How to solve :
  • Build offline failure signature library by pre-simulating 2000+ edge-case scenarios with known failure patterns (sensor occlusion, merge conflicts, pedestrian occlusion) and extract characteristic state vectors (time-to-collision <1.5s, lateral acceleration >4m/s², rule violation flags)
  • store signatures in indexed hash tables for O(1) lookup
  • During live benchmark execution, compute real-time state fingerprints every 100ms from algorithm outputs (position, velocity, planned trajectory)
  • match against pre-cached signatures using cosine similarity threshold ≥0.85 to instantly flag potential failure modes without running full scenario variations
  • Implement progressive validation protocol: when signature match detected, trigger lightweight 30-second confirmation test with perturbed parameters (±10% speed, ±0.5m spacing)
  • only escalate to full high-fidelity simulation if confirmation test shows instability, reducing false positive overhead to <5% of cases
Expected Effect : Detection rate 90%+, execution time <6 hours, 70% reduction vs exhaustive testing
Risk Control :
  • signature library incompleteness for novel scenarios
  • false positive rate from similarity threshold tuning
  • hash collision in high-dimensional state space

Problem Direction 5 :

ImproveScenario coverage comprehensiveness
VS
ConstraintMust not deteriorate

Inspiration 1 : Cross-domain reference

Application Principle: #10 Preliminary action
Cross-domain applicability Assess applicability
Home agent discovery upon changing the mobility management scheme
Innovative Solution Refine solution

Pre-cached scenario state library with instant load architecture

Pre-generate offline scenario state library with full initialization data for instant deployment
How to solve :
  • Build offline scenario state repository containing 500+ pre-initialized scenarios (vehicle positions, traffic states, HD maps, sensor configurations) stored as binary snapshots — each scenario loads in <5 seconds vs 2-3 minutes runtime initialization
  • Implement two-phase temporal execution — Phase 1 (weeks 1-6): deploy 300 broad scenarios at medium fidelity (kinematic models, 50% sensor detail) achieving 60% coverage in 4-hour runs
  • Phase 2 (weeks 7-10): deploy 200 deep edge-case scenarios at full fidelity (physics-based dynamics, 100% sensor simulation) targeting remaining 20%+ coverage in 6-hour runs
  • Apply progressive scenario retirement protocol — after algorithm passes any scenario category (e.g., highway lane-change) with zero failures across 3 consecutive iterations, archive 60% of that category and allocate freed compute budget to newly identified edge cases (construction zones, sensor occlusion variants) — maintain active test pool at 350-400 scenarios while coverage evolves from common to rare cases
Expected Effect : Scenario load time reduced 96% (3min→5sec); breadth coverage 60% in 4hrs, depth coverage 80%+ total in 10hrs cumulative; active scenario count stable at 350-400
Risk Control :
  • binary snapshot compatibility across simulation versions
  • scenario retirement triggers premature removal of critical cases
  • temporal phase transition timing misalignment with algorithm maturity
Patsnap Eureka Solution