system: 
You are an expert in composing functions. You are given a question and a set of possible functions.
Based on the question, you will need to make one or more function/tool calls to achieve the purpose.
If none of the function can be used, point it out. If the given question lacks the parameters required by the function,
also point it out. You should only return the function call in tools call sections.

If you decide to invoke any of the function(s), you MUST put it in the format of [func_name1(params_name1=params_value1, params_name2=params_value2...), func_name2(params)]

You SHOULD NOT include any other text in the response.

Here is a list of functions in JSON format that you can invoke.

[{'name': 'get_headway', 'description': "Calculates the headway, which is the distance from the front of the ego vehicle to the closest leading object, using curvilinear coordinates. This function requires the ego vehicle's information, the detected lane, and the 3D bounding boxes from perception data. Note that the provided function is in Python 3 syntax.", 'parameters': {'type': 'dict', 'required': ['ego_info', 'lane_info', 'bounding_boxes'], 'properties': {'ego_info': {'type': 'dict', 'description': 'Information about the ego vehicle, including its position and orientation.', 'properties': {'position': {'type': 'dict', 'description': 'Curvilinear coordinates of the ego vehicle, consisting of lateral and longitudinal positions.', 'properties': {'lateral': {'type': 'float', 'description': 'Lateral position of the ego vehicle in meters.'}, 'longitudinal': {'type': 'float', 'description': 'Longitudinal position of the ego vehicle in meters.'}}}, 'orientation': {'type': 'float', 'description': 'Orientation of the ego vehicle in degrees.'}}}, 'lane_info': {'type': 'dict', 'description': 'Information about the detected lane.', 'properties': {'lane_id': {'type': 'string', 'description': 'Unique identifier for the detected lane.'}, 'lane_type': {'type': 'string', 'description': 'Type of the lane.', 'enum': ['regular', 'merge', 'exit', 'turn']}}}, 'bounding_boxes': {'type': 'array', 'items': {'type': 'dict'}, 'description': "List of 3D bounding boxes representing detected objects. Each bounding box should include dimensions and the object's position relative to the ego vehicle."}}}}, {'name': 'get_time_headway', 'description': "Calculates the time headway (THW), which is the time it will take for the ego vehicle to reach the closest leading object in curvilinear coordinates. Inputs include the ego vehicle's information, the detected lane, and the 3D bounding boxes of the perceived objects. Note that the provided function is in Python 3 syntax.", 'parameters': {'type': 'dict', 'required': ['ego_info', 'lane_info', 'bboxes', 'velocities', 'accelerations'], 'properties': {'ego_info': {'type': 'dict', 'description': "Contains the ego vehicle's current position and velocity.", 'properties': {'position': {'type': 'tuple', 'description': "The vehicle's current position as a tuple of x, y, and z coordinates (meters).", 'items': {'type': 'float'}}, 'velocity': {'type': 'float', 'description': "The vehicle's current velocity in meters per second (m/s)."}}}, 'lane_info': {'type': 'dict', 'description': 'Information about the detected lane, including its curvature and width.', 'properties': {'curvature': {'type': 'float', 'description': "The curvature of the lane at the ego vehicle's position (1/meters)."}, 'width': {'type': 'float', 'description': 'The width of the lane in meters (m).'}}}, 'bboxes': {'type': 'array', 'items': {'type': 'dict'}, 'description': 'List of 3D bounding boxes representing perceived objects. Each bounding box is described by its position and size.'}, 'velocities': {'type': 'array', 'items': {'type': 'float'}, 'description': 'List of velocities of the detected objects in meters per second (m/s).'}, 'accelerations': {'type': 'array', 'items': {'type': 'float'}, 'description': 'List of accelerations of the detected objects in meters per second squared (m/s^2).'}}}}, {'name': 'get_time_to_collision', 'description': "Calculates the time it will take for the ego vehicle to collide with the closest leading object in curvilinear coordinates, considering both vehicles' velocities and the leading object's acceleration. Note that the provided function is in Python 3 syntax.", 'parameters': {'type': 'dict', 'required': ['ego_velocity', 'ego_acceleration', 'leading_object_velocity', 'leading_object_acceleration', 'initial_distance'], 'properties': {'ego_velocity': {'type': 'float', 'description': 'The current velocity of the ego vehicle in meters per second.'}, 'ego_acceleration': {'type': 'float', 'description': 'The current acceleration of the ego vehicle in meters per second squared.'}, 'leading_object_velocity': {'type': 'float', 'description': 'The current velocity of the leading object in meters per second.'}, 'leading_object_acceleration': {'type': 'float', 'description': 'The current acceleration of the leading object in meters per second squared.'}, 'initial_distance': {'type': 'float', 'description': 'The initial distance between the ego vehicle and the leading object in meters.'}}}}]


user: Could you determine the distance to the closest leading object for our vehicle? Here's the data: our car's position is at 10.5 meters laterally and 50 meters longitudinally, with an orientation of 30 degrees. The lane we're in has an ID of 'L123' and it's a regular type. We've detected several objects, but the closest has a bounding box with x coordinate 60.2 meters and y coordinate 12.3 meters.

Available functions:
[{"name": "get_headway", "description": "Calculates the headway, which is the distance from the front of the ego vehicle to the closest leading object, using curvilinear coordinates. This function requires the ego vehicle's information, the detected lane, and the 3D bounding boxes from perception data.", "parameters": {"type": "dict", "required": ["ego_info", "lane_info", "bounding_boxes"], "properties": {"ego_info": {"type": "dict", "description": "Information about the ego vehicle, including its position and orientation.", "properties": {"position": {"type": "dict", "description": "Curvilinear coordinates of the ego vehicle, consisting of lateral and longitudinal positions.", "properties": {"lateral": {"type": "float", "description": "Lateral position of the ego vehicle in meters."}, "longitudinal": {"type": "float", "description": "Longitudinal position of the ego vehicle in meters."}}}, "orientation": {"type": "float", "description": "Orientation of the ego vehicle in degrees."}}}, "lane_info": {"type": "dict", "description": "Information about the detected lane.", "properties": {"lane_id": {"type": "string", "description": "Unique identifier for the detected lane."}, "lane_type": {"type": "string", "description": "Type of the lane.", "enum": ["regular", "merge", "exit", "turn"]}}}, "bounding_boxes": {"type": "array", "items": {"type": "dict"}, "description": "List of 3D bounding boxes representing detected objects. Each bounding box should include dimensions and the object's position relative to the ego vehicle."}}}}, {"name": "get_time_headway", "description": "Calculates the time headway (THW), which is the time it will take for the ego vehicle to reach the closest leading object in curvilinear coordinates. Inputs include the ego vehicle's information, the detected lane, and the 3D bounding boxes of the perceived objects.", "parameters": {"type": "dict", "required": ["ego_info", "lane_info", "bboxes", "velocities", "accelerations"], "properties": {"ego_info": {"type": "dict", "description": "Contains the ego vehicle's current position and velocity.", "properties": {"position": {"type": "tuple", "description": "The vehicle's current position as a tuple of x, y, and z coordinates (meters).", "items": {"type": "float"}}, "velocity": {"type": "float", "description": "The vehicle's current velocity in meters per second (m/s)."}}}, "lane_info": {"type": "dict", "description": "Information about the detected lane, including its curvature and width.", "properties": {"curvature": {"type": "float", "description": "The curvature of the lane at the ego vehicle's position (1/meters)."}, "width": {"type": "float", "description": "The width of the lane in meters (m)."}}}, "bboxes": {"type": "array", "items": {"type": "dict"}, "description": "List of 3D bounding boxes representing perceived objects. Each bounding box is described by its position and size."}, "velocities": {"type": "array", "items": {"type": "float"}, "description": "List of velocities of the detected objects in meters per second (m/s)."}, "accelerations": {"type": "array", "items": {"type": "float"}, "description": "List of accelerations of the detected objects in meters per second squared (m/s^2)."}}}}, {"name": "get_time_to_collision", "description": "Calculates the time it will take for the ego vehicle to collide with the closest leading object in curvilinear coordinates, considering both vehicles' velocities and the leading object's acceleration.", "parameters": {"type": "dict", "required": ["ego_velocity", "ego_acceleration", "leading_object_velocity", "leading_object_acceleration", "initial_distance"], "properties": {"ego_velocity": {"type": "float", "description": "The current velocity of the ego vehicle in meters per second."}, "ego_acceleration": {"type": "float", "description": "The current acceleration of the ego vehicle in meters per second squared."}, "leading_object_velocity": {"type": "float", "description": "The current velocity of the leading object in meters per second."}, "leading_object_acceleration": {"type": "float", "description": "The current acceleration of the leading object in meters per second squared."}, "initial_distance": {"type": "float", "description": "The initial distance between the ego vehicle and the leading object in meters."}}}}]

Ground-truth function call:
[{"get_headway": {"ego_info": [{"position": [{"lateral": 10.5, "longitudinal": 50}], "orientation": [30]}], "lane_info": [{"lane_id": ["L123"], "lane_type": ["regular"]}], "bounding_boxes": [[{"x": [60.2], "y": [12.3]}]]}}]