verse.agents.example_agent.car_agent.NPCAgent

class verse.agents.example_agent.car_agent.NPCAgent(id, initial_state=None, initial_mode=None)

Bases: BaseAgent

Methods

TC_simulate(mode, init, time_bound, time_step)	Abstract simulation function
action_handler(mode, state, lane_map)	Computes steering and acceleration based on current lane, target lane and current state using a Stanley controller-like rule
set_initial(initial_state, initial_mode[, ...])	Initialize the states

dynamic
set_initial_mode
set_initial_state
set_static_parameter
set_uncertain_parameter

TC_simulate(mode: Tuple[str], init, time_bound, time_step, lane_map: LaneMap = None) → ndarray[Any, dtype[float64]]

Abstract simulation function

Parameters:

mode: str

The current mode to simulate

initialSet: List[float]

The initial condition to perform the simulation

time_horizon: float

The time horizon for simulation

time_step: float

time_step for performing simulation

map: LaneMap, optional

Provided if the map is used

__init__(id, initial_state=None, initial_mode=None)

Constructor of agent base class.

Parameters:

idstr

id of the agent.

code: str

actual code of python controller

file_name: str

file name to the python controller

action_handler(mode, state, lane_map: LaneMap) → Tuple[float, float]

Computes steering and acceleration based on current lane, target lane and current state using a Stanley controller-like rule

set_initial(initial_state, initial_mode, static_param=None, uncertain_param=None)

Initialize the states