| --- |
| license: other |
| tags: |
| - heal |
| - horizon |
| --- |
| |
| # QCNetOE (Trajectory Prediction) |
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| QCNetOE encodes each agent's relative relationships with surrounding map and other agents in a query-centric manner, streaming encoder hidden states agent-by-agent, then the decoder outputs multimodal candidate trajectories and probabilities; removes `torch_geometric`/`torch_cluster` dependencies and eliminates most index/gather/scatter ops for quantization-friendly deployment. |
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| ## Deployment Metrics |
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| ### Model Parameters |
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| | Model | Model Input | Backbone | Neck | Model Output | |
| |---|---|---|---|---| |
| | QCNetOE | Streaming scene representation tensor group `(B,A,pl,pt,HT)` | QCNetOEMapEncoder + QCNetOEAgentEncoderStream | — | Candidate future trajectories `(B,A,6,12,2)` + trajectory probabilities | |
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| ### Accuracy Metrics |
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| | March | Metric | float | calibration | qat | hbm | |
| | --- | --- | --- | --- | --- | --- | |
| | J6M | HitRate | 0.8003 | 0.6817 | 0.7984 | 0.7981 | |
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| > Data measured with `march = March.NASH_M` (J6M) configuration. |
| > |
| > HEAL version: heal 0.0.2 / hbdk4-compiler 4.11.11 / horizon_plugin_pytorch 3.3.10. |
| |
| ### Performance Metrics |
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| > **Performance test methodology**: FPS is measured with single-core eight-thread; Latency is measured with single-core single-thread; Memory is peak DDR usage. |
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| | March | latency (ms) | fps | Memory Usage | |
| |---|---|---|---| |
| | J6M | 3.72 | 293.43 | 34.80 | |
| | J6P | 2.65 | 1572.60 | 38.60 | |
| | J6B | 12.25 | 149.40 | 32.00 | |
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| --- |
| |
| ## Model Overview |
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| ### Core Design |
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| QCNetOE encodes each agent's relative relationships with surrounding map and other agents in a query-centric manner, streaming encoder hidden states agent-by-agent, then the decoder outputs multimodal candidate trajectories and probabilities; removes `torch_geometric`/`torch_cluster` dependencies and eliminates most index/gather/scatter ops for quantization-friendly deployment. |
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| - **Task type**: Trajectory prediction (Motion Forecasting, multimodal trajectory prediction). |
| - **backbone**: `QCNetOEMapEncoder` + `QCNetOEAgentEncoderStream` (streaming inference, `stream_infer=True`; `hidden_dim=128`, `num_heads=8`, `head_dim=16`, `num_freq_bands=32`, `num_map_layers=1`, `num_agent_layers=1`, `time_span=2`, `dropout=0.1`; agent-map interaction `num_pl2a=32`, agent-agent interaction `num_a2a=36`). |
| - **neck**: — (QCNetOE has no standalone neck; encoder output feeds directly into decoder). |
| - **Decoder**: `QCNetOEDecoder` (`num_dec_layers=1`), outputs `num_modes=6` candidate trajectories and their probabilities. |
| - **Preprocessing**: `QCNetOEPreprocess` (`stream=True`, constructs agent/map relative representations and spatiotemporal relative position encoding). |
| - **Post-processing**: `QCNetOEPostprocess` (output dimension `output_dim=2`, i.e. predicted trajectory (x, y)). |
| - **Loss**: `QCNetOELoss`. |
| - **Model input**: Streaming scene representation tensor group (`B=1, A=30` agents, `pl=80` map polygons, `pt=50` polygon points, `HT=10` history steps), including `agent`/`map_polygon`/`map_point`/`decoder` etc. as OrderedDict inputs; 5s history (`num_historical_steps=10`) + 6s prediction (`num_future_steps=12`). |
| - **Model output**: 6 candidate future trajectories per agent to predict (`num_modes=6`, 12 steps, 2-dim coordinates `output_dim=2`) + probability per trajectory. |
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| **Deployment notes**: Model supports cold-start/hot-start streaming inference (`quant_infer_cold_start` controls). Both `cali_model` and `deploy_model` enable `stream_infer=True`; `save_memory` configured separately for training/deployment (training `save_memory=True` saves GPU memory, deployment `save_memory=False`). HBIR export enables `enable_vpu=True`; compilation uses `input_source=["ddr"]` (trajectory prediction input read from DDR, not pyramid image input), unlike image-based tasks. |
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| ### Official Repo and Paper |
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| Official repo: https://github.com/ZikangZhou/QCNet |
| Paper: https://openaccess.thecvf.com/content/CVPR2023/papers/Zhou_Query-Centric_Trajectory_Prediction_CVPR_2023_paper.pdf |
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| ### Reference |
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| For more J6 chip deployment details, see https://developer.horizon.auto/blog/14089 |
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