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Toward Driving Scene Understanding: A Dataset for Learning Driver Behavior and Causal Reasoning
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add CAN sensor inputs to CNN conv (ablation) | CNN conv · HDD test split, Goal-oriented action | mAP: 26.96 -> 32.71 | Helped |
| Replace spatial pooling with a learnable convolution for CNN features (ablation) | CNN+Sensors · HDD test split, Goal-oriented action | mAP: 13.46 -> 26.96 | Helped |
| Use CAN sensors without camera input (ablation) | Goal-oriented action detector · HDD test split, Goal-oriented action | mAP: 23.84 vs 32.71 | Hurt |
| Add visual inputs to the sensor-only model (ablation) | Sensors · HDD test split, Goal-oriented action | mAP: 23.84 -> 32.71 | Helped |
| Use sensor-only inputs instead of visual CNN features (ablation) | Goal-oriented action detector · HDD test split, Goal-oriented action | average precision | Mixed |
Closest papers by meaning
- nuReasoning: A Reasoning-Centric Dataset and Benchmark for Long-Tail Autonomous Driving (NeurIPS 2026 · similarity 0.86)
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- PSI: A Benchmark for Human Interpretation and Response in Traffic Interactions (NeurIPS 2025 · similarity 0.86)
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- BAT: Behavior-Aware Human-Like Trajectory Prediction for Autonomous Driving (AAAI 2024 · similarity 0.85)
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