| import logging, warnings |
| import numpy as np |
| from ROAR_Sim.configurations.configuration import Configuration as CarlaConfig |
| from ROAR.configurations.configuration import Configuration as AgentConfig |
| from pathlib import Path |
| from ROAR.agent_module.pure_pursuit_agent \ |
| import PurePursuitAgent |
| from ROAR_Sim.carla_client.carla_runner import CarlaRunner |
| from typing import Tuple |
| from prettytable import PrettyTable |
| from ROAR.agent_module.michael_pid_agent import PIDAgent |
|
|
|
|
| def compute_score(carla_runner: CarlaRunner) -> Tuple[float, int, int]: |
| """ |
| Calculates the score of the vehicle upon completion of the track based on certain metrics |
| Args: |
| carla_runner (): |
| |
| Returns: |
| time_elapsed: |
| num_collision: number of collisions during simulation |
| laps_completed: Number of laps completed |
| |
| """ |
| time_elapsed: float = carla_runner.end_simulation_time - carla_runner.start_simulation_time |
| num_collision: int = carla_runner.agent_collision_counter |
| laps_completed = 0 if carla_runner.completed_lap_count < 0 else carla_runner.completed_lap_count |
|
|
| return time_elapsed, num_collision, laps_completed |
|
|
|
|
| def run(agent_class, agent_config_file_path: Path, carla_config_file_path: Path, |
| num_laps: int = 10) -> Tuple[float, int, int]: |
| """ |
| Run the agent along the track and produce a score based on certain metrics |
| Args: |
| num_laps: int number of laps that the agent should run |
| agent_class: the participant's agent |
| agent_config_file_path: agent configuration path |
| carla_config_file_path: carla configuration path |
| Returns: |
| float between 0 - 1 representing scores |
| """ |
|
|
| agent_config: AgentConfig = AgentConfig.parse_file(agent_config_file_path) |
| carla_config = CarlaConfig.parse_file(carla_config_file_path) |
|
|
| |
| agent_config.num_laps = num_laps |
| carla_runner = CarlaRunner(carla_settings=carla_config, |
| agent_settings=agent_config, |
| npc_agent_class=PurePursuitAgent, |
| competition_mode=True, |
| lap_count=num_laps) |
| try: |
| my_vehicle = carla_runner.set_carla_world() |
| agent = agent_class(vehicle=my_vehicle, agent_settings=agent_config) |
| carla_runner.start_game_loop(agent=agent, use_manual_control=False) |
| return compute_score(carla_runner) |
| except Exception as e: |
| print(f"something bad happened during initialization: {e}") |
| carla_runner.on_finish() |
| logging.error(f"{e}. Might be a good idea to restart Server") |
| return 0, 0, False |
|
|
|
|
| def suppress_warnings(): |
| logging.basicConfig(format='%(levelname)s - %(asctime)s - %(name)s ' |
| '- %(message)s', |
| level=logging.INFO) |
| logging.getLogger("matplotlib").setLevel(logging.WARNING) |
| warnings.simplefilter("ignore") |
| np.set_printoptions(suppress=True) |
|
|
|
|
| def main(): |
| suppress_warnings() |
| agent_class = PIDAgent |
| num_trials = 10 |
| total_score_array = [] |
| num_laps = 1 |
| table = PrettyTable() |
| table.field_names = ["time_elapsed (sec)", "num_collisions", "laps completed"] |
|
|
| for i in range(num_trials): |
| print(f"runner_competition_evaluator -> Trial: {i}") |
| scores = run(agent_class=agent_class, |
| agent_config_file_path=Path("./ROAR/configurations/carla/carla_agent_configuration.json"), |
| carla_config_file_path=Path("./ROAR_Sim/configurations/configuration.json"), |
| num_laps=num_laps) |
| total_score_array.append(scores[0]) |
| table.add_row(scores) |
| table.add_row((f"AVG for {num_trials} trials = {np.average(total_score_array)}", "N/A", "N/A")) |
| print(table) |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
