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Create pedestrian_detection.yaml
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‎launch/pedestrian_detection.yaml‎

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description: "Run the yielding trajectory planner on the real vehicle with real perception"
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mode: hardware
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vehicle_interface: gem_hardware.GEMHardwareInterface
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mission_execution: StandardExecutor
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# Recovery behavior after a component failure
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recovery:
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planning:
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trajectory_tracking : recovery.StopTrajectoryTracker
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# Driving behavior for the GEM vehicle. Runs real pedestrian perception, yield planner, but does not send commands to real vehicle.
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drive:
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perception:
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state_estimation: GNSSStateEstimator
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agent_detection:
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type: pedestrian_detection.PedestrianDetector3D
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args:
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camera_name: front #[front, front_right]
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camera_calib_file: ./GEMstack/knowledge/calibration/gem_e4_perception_cameras.yaml
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enable_tracking: True # True if you want to enable tracking
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visualize_2d: False # True to see 2D detection visualization
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use_cyl_roi: False # True to use a cylinder ROI
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save_data: False # True to save sensor input data
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orientation: False # True to detect flipped cones
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use_start_frame: True # True to output in START frame
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perception_normalization: StandardPerceptionNormalizer
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planning:
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relations_estimation:
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type: pedestrian_yield_logic.PedestrianYielder
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args:
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mode: 'real'
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params: {
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'yielder': 'expert', # 'expert', 'analytic', or 'simulation'
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'planner': 'milestone', # 'milestone', 'dt', or 'dx'
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'desired_speed': 1.0, # m/s, 1.5 m/s seems max speed? Feb24
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'acceleration': 0.75 # m/s2, 0.5 is not enough to start moving. Feb24
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}
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route_planning:
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type: StaticRoutePlanner
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args: [!relative_path '../GEMstack/knowledge/routes/forward_15m.csv','start']
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motion_planning: longitudinal_planning.YieldTrajectoryPlanner
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trajectory_tracking:
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type: pure_pursuit.PurePursuitTrajectoryTracker
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print: False
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log:
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# Specify the top-level folder to save the log files. Default is 'logs'
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#folder : 'logs'
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# If prefix is specified, then the log folder will be named with the prefix followed by the date and time. Default no prefix
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#prefix : 'fixed_route_'
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# If suffix is specified, then logs will output to folder/prefix+suffix. Default uses date and time as the suffix
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#suffix : 'test3'
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# Specify which ros topics to record to vehicle.bag. Default records nothing. This records the "standard" ROS topics.
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ros_topics :
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# Specify options to pass to rosbag record. Default is no options.
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#rosbag_options : '--split --size=1024'
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# If True, then record all readings / commands of the vehicle interface. Default False
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vehicle_interface : True
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# Specify which components to record to behavior.json. Default records nothing
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components : ['state_estimation','agent_detection','motion_planning']
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# Specify which components of state to record to state.json. Default records nothing
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#state: ['all']
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# Specify the rate in Hz at which to record state to state.json. Default records at the pipeline's rate
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#state_rate: 10
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# If True, then make plots of the recorded data specifically from PurePursuitTrajectoryTracker_debug.csv and report metrics. Default False
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#auto_plot : True
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replay: # Add items here to set certain topics / inputs to be replayed from logs
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# Specify which log folder to replay from
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log:
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ros_topics : []
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components : []
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#usually can keep this constant
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computation_graph: !include "../GEMstack/knowledge/defaults/computation_graph.yaml"
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variants:
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detector_only:
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run:
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description: "Run the pedestrian detection code"
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drive:
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planning:
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trajectory_tracking:
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real_sim:
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run:
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description: "Run the pedestrian detection code with real detection and fake simulation"
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mode: hardware
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vehicle_interface:
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type: gem_mixed.GEMRealSensorsWithSimMotionInterface
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args:
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scene: !relative_path '../scenes/xyhead_demo.yaml'
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mission_execution: StandardExecutor
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require_engaged: False
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visualization: !include "klampt_visualization.yaml"
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drive:
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perception:
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state_estimation : OmniscientStateEstimator
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planning:
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relations_estimation:
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type: pedestrian_yield_logic.PedestrianYielder
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args:
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mode: 'sim'
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fake_real:
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run:
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description: "Run the yielding trajectory planner on the real vehicle with faked perception"
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drive:
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perception:
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agent_detection : pedestrian_detection.FakePedestrianDetector2D
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fake_sim:
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run:
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description: "Run the yielding trajectory planner in simulation with faked perception"
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mode: simulation
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vehicle_interface:
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type: gem_simulator.GEMDoubleIntegratorSimulationInterface
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args:
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scene: !relative_path '../scenes/xyhead_demo.yaml'
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visualization: !include "klampt_visualization.yaml"
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drive:
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perception:
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# agent_detection : pedestrian_detection.FakePedestrianDetector2D
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agent_detection : OmniscientAgentDetector #this option reads agents from the simulator
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state_estimation : OmniscientStateEstimator
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planning:
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relations_estimation:
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type: pedestrian_yield_logic.PedestrianYielder
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args:
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mode: 'sim'

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