Enterprise AI Analysis
Measuring What Matters: Scenario-Driven Evaluation for Trajectory Predictors in Autonomous Driving
This paper introduces ED-Eva, a novel scenario-driven evaluation pipeline for trajectory predictors in autonomous driving. Unlike traditional error-based metrics, ED-Eva adaptively assesses predictor performance by balancing accuracy and diversity based on the criticality of the driving scenario. This ensures that selected predictors contribute most effectively to the self-driving vehicle's (SDV) driving performance, enhancing safety, efficiency, and passenger comfort in complex interactive environments.
Executive Impact Snapshot
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Improvement in Correlation with SDV Performance
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Annual potential savings through optimized trajectory prediction
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Real-world scenarios tested in evaluation
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Predictor models evaluated in closed-loop benchmark
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Precision of ScenarioNN in identifying critical scenarios
ED-Eva vs. Traditional Metrics (Correlation with Driving Performance)
| Metric | MTR | Autobot | Wayformer |
|---|---|---|---|
| ED-Eva (GAD, ADE) | +0.2200 | +0.2652 | +0.1843 |
| ADE | -0.3399 | -0.3737 | -0.3991 |
| FDE | -0.3462 | -0.3966 | -0.4202 |
| Diversity (GAD) | -0.5905 | 0.6058 | -0.5871 |
Enterprise Process Flow
Observe Scenario (t=n)
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Predict Trajectories
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Classify Scenario Criticality
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Calculate GAD & Error
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Combine Scores (Pc, 1-Pc)
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Plan Ego Trajectory (t=n+1)
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Evaluate Driving Performance
Impact of GAD on Diverse Predictions
A key finding is how the GMM-Area Diversity (GAD) metric quantifies the spread of predicted trajectories. In a critical intersection scenario, a predictor exhibiting higher GAD (e.g., GAD=0.346) is favored, indicating a better anticipation of multiple plausible agent maneuvers. Conversely, in simpler highway scenarios, a lower GAD (e.g., GAD=0.056) combined with high accuracy is preferred. This adaptive weighting, driven by the ScenarioNN classifier, ensures that the evaluation aligns with the real-world demands of different driving contexts, leading to safer and more robust decisions by the SDV planner. Our experiments show that ED-Eva consistently achieves higher correlation with actual driving performance compared to traditional error-based metrics, highlighting its potential for selecting predictors that truly enhance overall system safety and efficiency.
Key Challenges Identified
- Traditional error-based metrics (ADE, FDE) do not reflect actual impact on SDV driving performance.
- Difficulty in quantifying prediction diversity for robust decision-making in complex interactive scenarios.
- Lack of scenario-aware evaluation that dynamically balances accuracy and diversity based on context.
Proposed Solutions & Innovations
- Proposed ED-Eva: scenario-driven evaluation balancing Error and Diversity.
- Introduced GMM-Area Diversity (GAD) to quantify prediction spread robustly.
- Developed ScenarioNN classifier to dynamically weigh accuracy and diversity based on scenario criticality.
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Implementation Roadmap
A structured approach to integrating AI-powered trajectory prediction within your existing systems.
Phase 1: Data Integration & Scenario Classification
Integrate real-world driving data and train the ScenarioNN for robust criticality assessment.
Phase 2: Predictor Calibration & GAD Implementation
Calibrate existing trajectory predictors and implement the GMM-Area Diversity metric.
Phase 3: Closed-Loop System Integration & Testing
Integrate ED-Eva into the SDV planning pipeline and conduct extensive closed-loop testing.
Phase 4: Performance Validation & Deployment
Validate improved driving performance and deploy optimized predictors.
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