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DevTekVE
1a8dd310ae
* Add support for TinyGrad model runner processing Introduced a new function `is_tinygrad_model` to detect TinyGrad as an active model runner. Updated the `is_stock_model` logic to account for TinyGrad models and added a new process entry for TinyGrad in the model manager. This enables handling TinyGrad models alongside existing configurations. adding modeld back Add support for `modeld_v2` and update paths for consistency Updated `SConscript` files to integrate `modeld_v2` alongside `modeld` and adjusted script paths for correct metadata handling. Adjusted various configurations and scripts, such as `labeler.yaml` and `build_release.sh`, to include `modeld_v2` and ensure cohesive project structure. Refactor imports to use updated `modeld_v2` paths. Replaced outdated `modeld` references with their `modeld_v2` counterparts for consistency and clarity across the codebase. Also updated `.gitignore` to accommodate new directory structure. This change ensures better maintainability and alignment with the new directory schema. Refactor and reorganize modeld to sunnypilot/modeld_v2 structure. Moved and renamed `modeld` components to the new `sunnypilot/modeld_v2` directory for better organization and modularity. Updated imports and file references to align with the new structure, ensuring compatibility and functionality. Streamlined project structure to improve maintainability and future development. * typo * Use `stock` model runner and refactor model checks. Replaces outdated model detection logic with unified `stock` runner integration, simplifying the decision flow for model selection. Includes `stock` as a new enum in the `Runner` type and updates affected references accordingly. * Handle missing 'sim_pose' in model outputs gracefully. Added conditional checks to ensure the code handles cases where 'sim_pose' is absent in the model outputs. Fallback behaviors use 'plan' data when 'sim_pose' is unavailable, preventing potential errors and enhancing robustness.
85 lines
2.1 KiB
Python
85 lines
2.1 KiB
Python
import numpy as np
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def index_function(idx, max_val=192, max_idx=32):
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return (max_val) * ((idx/max_idx)**2)
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class ModelConstants:
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# time and distance indices
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IDX_N = 33
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T_IDXS = [index_function(idx, max_val=10.0) for idx in range(IDX_N)]
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X_IDXS = [index_function(idx, max_val=192.0) for idx in range(IDX_N)]
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LEAD_T_IDXS = [0., 2., 4., 6., 8., 10.]
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LEAD_T_OFFSETS = [0., 2., 4.]
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META_T_IDXS = [2., 4., 6., 8., 10.]
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# model inputs constants
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MODEL_FREQ = 20
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FEATURE_LEN = 512
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FULL_HISTORY_BUFFER_LEN = 99
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DESIRE_LEN = 8
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TRAFFIC_CONVENTION_LEN = 2
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LAT_PLANNER_STATE_LEN = 4
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LATERAL_CONTROL_PARAMS_LEN = 2
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PREV_DESIRED_CURV_LEN = 1
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# model outputs constants
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FCW_THRESHOLDS_5MS2 = np.array([.05, .05, .15, .15, .15], dtype=np.float32)
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FCW_THRESHOLDS_3MS2 = np.array([.7, .7], dtype=np.float32)
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FCW_5MS2_PROBS_WIDTH = 5
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FCW_3MS2_PROBS_WIDTH = 2
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DISENGAGE_WIDTH = 5
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POSE_WIDTH = 6
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WIDE_FROM_DEVICE_WIDTH = 3
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LEAD_WIDTH = 4
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LANE_LINES_WIDTH = 2
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ROAD_EDGES_WIDTH = 2
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PLAN_WIDTH = 15
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DESIRE_PRED_WIDTH = 8
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LAT_PLANNER_SOLUTION_WIDTH = 4
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DESIRED_CURV_WIDTH = 1
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NUM_LANE_LINES = 4
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NUM_ROAD_EDGES = 2
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LEAD_TRAJ_LEN = 6
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DESIRE_PRED_LEN = 4
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PLAN_MHP_N = 5
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LEAD_MHP_N = 2
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PLAN_MHP_SELECTION = 1
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LEAD_MHP_SELECTION = 3
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FCW_THRESHOLD_5MS2_HIGH = 0.15
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FCW_THRESHOLD_5MS2_LOW = 0.05
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FCW_THRESHOLD_3MS2 = 0.7
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CONFIDENCE_BUFFER_LEN = 5
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RYG_GREEN = 0.01165
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RYG_YELLOW = 0.06157
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POLY_PATH_DEGREE = 4
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# model outputs slices
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class Plan:
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POSITION = slice(0, 3)
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VELOCITY = slice(3, 6)
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ACCELERATION = slice(6, 9)
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T_FROM_CURRENT_EULER = slice(9, 12)
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ORIENTATION_RATE = slice(12, 15)
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class Meta:
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ENGAGED = slice(0, 1)
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# next 2, 4, 6, 8, 10 seconds
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GAS_DISENGAGE = slice(1, 31, 6)
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BRAKE_DISENGAGE = slice(2, 31, 6)
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STEER_OVERRIDE = slice(3, 31, 6)
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HARD_BRAKE_3 = slice(4, 31, 6)
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HARD_BRAKE_4 = slice(5, 31, 6)
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HARD_BRAKE_5 = slice(6, 31, 6)
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# next 0, 2, 4, 6, 8, 10 seconds
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GAS_PRESS = slice(31, 55, 4)
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BRAKE_PRESS = slice(32, 55, 4)
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LEFT_BLINKER = slice(33, 55, 4)
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RIGHT_BLINKER = slice(34, 55, 4)
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