| # Network description of the OnePlanner model in this directory (oneplanner_planner.onnx and | |
| # bevfusion_lidar_feature.onnx), read by autoware_oneplanner as ml_package_<model_name>.param.yaml. | |
| # It overrides the package defaults for this network; host and deployment behaviour (workspace, | |
| # rates, tolerances, paths) stays in the package's oneplanner.param.yaml. See the package's | |
| # docs/model_contract.md for the meaning of every field. | |
| /**: | |
| ros__parameters: | |
| # The input convention the model was trained with; the node refuses any other. | |
| input_convention: "oneplanner_v1" | |
| # Which sensor input providers the model needs, matched by tensor name. | |
| sensor_inputs: ["bev_feature"] | |
| enable_context_inputs: true | |
| # When the plan starts: at the newest odometry; the graph reads sensor_latency. | |
| planning_time: "planning_time" | |
| # Builder precision of the planner graph. | |
| precision: "fp32" | |
| context: | |
| ego_history_interval_seconds: 0.1 | |
| # The graph does not read ego_curvature_bias. | |
| curvature_bias: | |
| enabled: false | |
| tau_s: 2.0 | |
| min_speed_mps: 1.0 | |
| # The graph does not read lane_signals / route_signals. | |
| signal_history: | |
| enabled: false | |
| steps: 7 | |
| step_s: 0.5 | |
| turn_indicators: | |
| # The graph carries this input for a stable signature only; perturbing it changes no | |
| # output. Bind a constant, subscribe to nothing. | |
| enabled: false | |
| bev_feature: | |
| history_tensor: "bev_feature_history" | |
| frames: 3 | |
| interval_seconds: 0.5 | |
| bev_half_extent_m: 122.4 | |
| extractor: | |
| precision: "fp16" | |
| feature_tensor: "bev_feature" | |
| max_points_per_voxel: 32 # from the voxels input shape | |
| use_intensity: true # 5 point features | |
| voxels_num: [1, 128000, 256000] # [min, opt, max] | |
| point_cloud_range: [-122.4, -122.4, -3.0, 122.4, 122.4, 5.0] | |
| voxel_size: [0.17, 0.17, 0.2] | |
| detection: | |
| # The frozen BEVFusion detection head the extractor graph carries. It emits undecoded | |
| # proposals; the node decodes and suppresses them with autoware_bevfusion's | |
| # postprocessing and publishes ~/output/detected_objects. | |
| enabled: true | |
| # One Autoware class name per head class, in the head's label order. A head class | |
| # with no ObjectClassification label is written as UNKNOWN. | |
| # head classes: car, truck, bus, bicycle, pedestrian, traffic_cone, barrier | |
| class_names: ["CAR", "TRUCK", "BUS", "BICYCLE", "PEDESTRIAN", "UNKNOWN", "UNKNOWN"] | |
| num_proposals: 500 | |
| out_size_factor: 8 | |
| # Score thresholds per (distance band, class), calibrated for this head and applied as | |
| # autoware_bevfusion applies detection_score_thresholds: the box centre's radial | |
| # distance picks the first band whose upper limit exceeds it, and a box beyond the last | |
| # band is dropped. | |
| distance_bin_upper_limits: [50.0, 90.0, 121.0, 200.0] | |
| # distance-major: one row per band, one column per head class | |
| # CAR TRUCK BUS BICYCLE PEDESTRIAN TRAFFIC_CONE BARRIER | |
| score_thresholds: [ | |
| 0.194733, 0.167118, 0.109791, 0.245172, 0.150065, 0.12842, 0.043799, # [0, 50) m | |
| 0.221063, 0.18848, 0.168961, 0.160301, 0.152223, 0.15707, 0.1, # [50, 90) m | |
| 0.155292, 0.163172, 0.08902, 0.185649, 0.154218, 0.239001, 0.1, # [90, 121) m | |
| 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1 # [121, 200) m | |
| ] | |
| postprocess: | |
| prediction_tensor: "trajectory" | |
| extra_trajectory_tensors: ["prior_trajectory"] | |
| # Logits of DISABLE, ENABLE_LEFT, ENABLE_RIGHT, published as ~/output/turn_indicators. | |
| turn_indicator: | |
| tensor: "turn_indicator_logits" | |
| horizon_seconds: 6.0 # 60 steps | |
| time_step: 0.1 | |
| generator_name: "OnePlanner" | |