Add save_conflict_info function & gen_corner_case examples

examples/scenarios/Mcity_corner_case_generation.yaml

@@ -0,0 +1,192 @@
+simulation_module: "my_simulations"
+simulation_class: "CarSimulation"
+parameters:
+  num_cars: 10
+  simulation_time: 500
+
+# Simulation type: Choose between "safetest_mcity_av" or "safetest_mcity"
+simulation_type: "safetest_mcity_av"
+
+
+environment:
+  # Module containing the environment class
+  module: "terasim_nde_nade.envs"  # Use "terasim_nde_nade.envs.safetest_nade" for non-AV simulations
+  # Environment class name
+  class: "SafeTestNADE"  # Use "SafeTestNADE" for non-AV simulations
+  parameters:
+    # Vehicle factory class
+    vehicle_factory: "terasim_nde_nade.vehicle.nde_vehicle_factory.NDEVehicleFactory"
+    # Info extractor class
+    info_extractor: "terasim.logger.infoextractor.InfoExtractor"
+    log_flag: true
+    warmup_time_lb: 100  # Lower bound for warmup time
+    warmup_time_ub: 500  # Upper bound for warmup time
+    run_time: 500  # Simulation run time (use 30 for non-AV simulations)
+
+    MOBIL_lc_flag: True
+    stochastic_acc_flag: False
+    drive_rule: "righthand"
+
+    adversity_sampling_probability: 0.0
+    adversity_cfg:
+      vehicle:
+        roundabout_cutin:
+          _target_: terasim_nde_nade.adversity.vehicles.lanechange_adversity.LanechangeAdversity
+          _convert_: 'all'
+          location: 'roundabout'
+          ego_type: 'vehicle'
+          probability: 2.3123152310029683e-05
+          predicted_collision_type: "roundabout_cutin"
+
+        # roundabout_rearend:
+        #   _target_: terasim_nde_nade.adversity.vehicles.leader_adversity.LeaderAdversity
+        #   _convert_: 'all'
+        #   location: 'roundabout'
+        #   ego_type: 'vehicle'
+        #   probability: 1.8383121748678325e-10
+        #   predicted_collision_type: "roundabout_rearend"
+
+        roundabout_fail_to_yield:
+          _target_: terasim_nde_nade.adversity.vehicles.trafficrule_adversity.TrafficRuleAdversity
+          _convert_: 'all'
+          location: 'roundabout'
+          ego_type: 'vehicle'
+          probability: 0.0005025411072000003
+          predicted_collision_type: "roundabout_fail_to_yield"
+
+        # roundabout_neglect_conflict_lead:
+        #   _target_: terasim_nde_nade.adversity.roundabout_cutin_adversity.RoundaboutCutinAdversity
+        #   _convert_: 'all'
+        #   location: 'roundabout'
+        #   ego_type: 'vehicle'
+        #   probability: 0.00027119028871789013
+
+        highway_cutin:
+          _target_: terasim_nde_nade.adversity.vehicles.lanechange_adversity.LanechangeAdversity
+          _convert_: 'all'
+          location: 'highway'
+          ego_type: 'vehicle'
+          probability: 3.1996225726187704e-05
+          predicted_collision_type: "highway_cutin"
+
+        # highway_rearend:
+        #   _target_: terasim_nde_nade.adversity.vehicles.leader_adversity.LeaderAdversity
+        #   _convert_: 'all'
+        #   location: 'highway'
+        #   ego_type: 'vehicle'
+        #   probability: 0 # 0.8783674048511999
+        #   predicted_collision_type: "highway_rearend"
+
+        intersection_cutin:
+          _target_: terasim_nde_nade.adversity.vehicles.lanechange_adversity.LanechangeAdversity
+          _convert_: 'all'
+          location: 'intersection'
+          ego_type: 'vehicle'
+          probability: 6.694592721399203e-05
+          predicted_collision_type: "intersection_cutin"
+
+        # intersection_rearend:
+        #   _target_: terasim_nde_nade.adversity.vehicles.leader_adversity.LeaderAdversity
+        #   _convert_: 'all'
+        #   location: 'intersection'
+        #   ego_type: 'vehicle'
+        #   probability: 0.00017365940078885788
+        #   predicted_collision_type: "intersection_rearend"
+
+        intersection_headon:
+          _target_: terasim_nde_nade.adversity.vehicles.headon_adversity.HeadonAdversity
+          _convert_: 'all'
+          location: 'intersection'
+          ego_type: 'vehicle'
+          probability: 1.0663168272664859e-08
+          predicted_collision_type: "intersection_headon"
+
+        intersection_tfl:
+          _target_: terasim_nde_nade.adversity.vehicles.trafficrule_adversity.TrafficRuleAdversity
+          _convert_: 'all'
+          location: 'intersection'
+          ego_type: 'vehicle'
+          probability: 0.010141902545438387
+          predicted_collision_type: "intersection_tfl"
+
+        # intersection_neglect_conflict_lead:
+        #   _target_: terasim_nde_nade.adversity.roundabout_cutin_adversity.RoundaboutCutinAdversity
+        #   _convert_: 'all'
+        #   location: 'roundabout'
+        #   ego_type: 'vehicle'
+        #   probability: 0.015050715723004704
+
+      vulnerable_road_user:
+        jaywalking:
+          _target_: terasim_nde_nade.adversity.vru.jaywalking_adversity.JaywalkingAdversity
+          _convert_: 'all'
+          location: 'crosswalk'
+          ego_type: 'vulnerable_road_user'
+          # probability: 0.001
+          probability: 0.0
+          predicted_collision_type: "intersection_jaywalking"
+
+        runningredlight:
+          _target_: terasim_nde_nade.adversity.vru.runningredlight_adversity.RunningRedLightAdversity
+          _convert_: 'all'
+          location: 'crosswalk'
+          ego_type: 'vulnerable_road_user'
+          # probability: 0.001
+          probability: 0.0
+          predicted_collision_type: "intersection_runningredlight"
+
+        stopcrossing:
+          _target_: terasim_nde_nade.adversity.vru.stopcrossing_adversity.StopCrossingAdversity
+          _convert_: 'all'
+          location: 'crosswalk'
+          ego_type: 'vulnerable_road_user'
+          # probability: 0.001
+          probability: 0.0
+          predicted_collision_type: "intersection_stopcrossing"
+
+    AV_cfg:
+      route: ["EG_35_1_14", "EG_1_3_1", "EG_1_3_1.61", "EG_1_3_1.136", "EG_34_1_24", "EG_34_1_3", "gneE0", "EG_4_1_1", "EG_10_1_1", "EG_15_1_17", "EG_15_1_1", "EG_16_45_1", "EG_16_23_1", "EG_17_1_1", "EG_14_2_1", "EG_9_1_1", "EG_21_1_1", "EG_21_1_5", "EG_20_1_11", "EG_29_1_1", "EG_35_1_14"] # list of SUMO edges (example for Mcity)
+      type: "NDE_URBAN"
+      cache_radius: 100
+      control_radius: 100
+      warmup_control:
+        enabled: true  # Enable AV warmup (false keeps original behavior)
+
+        # Trigger condition (choose one)
+        trigger_type: "time"  # Options: "position", "time", "zone", "edge"
+
+        # Position trigger configuration
+        time_trigger:
+          duration: 3.0  # Warmup duration in seconds
+simulator:
+  module: "terasim.simulator"
+  class: "Simulator"
+  parameters:
+    num_tries: 10
+    gui_flag: false
+    realtime_flag: false  # Only applicable for AV mode
+    sumo_output_file_types: 
+      - "fcd_all"
+      - "collision"
+      - "tripinfo"
+
+# Path resolution mode:
+# - "config_relative": Relative paths are resolved from the config file's directory
+# - "cwd_relative": Relative paths are resolved from the current working directory
+path_resolution: "config_relative" 
+
+# New section for file paths
+input:
+  sumo_net_file: "../maps/Mcity/mcity.net.xml"
+  sumo_config_file: "../maps/Mcity/mcity.sumocfg"
+
+output:
+  dir: "outputs"  # Output directory (use "output" for non-AV simulations)
+  name: "Mcity"  # Experiment name
+  nth: "0_0"  # Experiment number
+  aggregated_dir: "aggregated"  # Directory for aggregated logs
+
+logging:
+  levels:
+    - "TRACE"  # Log level for the main log file
+    - "INFO"   # Log level for the aggregated log file

packages/terasim-nde-nade/terasim_nde_nade/envs/nade.py

@@ -347,6 +347,25 @@ def update_distance(self):
             distance_info_dict[veh_id] = traci.vehicle.getDistance(veh_id)
         return distance_info_dict
 
+
+    def save_conflict_info(self, conflict_veh_info):
+        sim_time = utils.get_time()
+
+        conflict_info_file = self.simulator.output_path / "conflict_info.jsonl"
+
+        vehicle_conflict_info = conflict_veh_info.get(AgentType.VEHICLE, {})
+        if not vehicle_conflict_info:
+            return
+
+        record = {
+            "timestamp": sim_time,
+            "conflict_veh_info": vehicle_conflict_info
+        }
+        # write JSON Lines file in append mode
+        with open(conflict_info_file, "a") as f:
+            f.write(json.dumps(record) + "\n")
+        return
+
     @profile
     def NADE_decision(self, env_command_information, env_observation):
         """NADE decision here.
@@ -369,13 +388,17 @@ def NADE_decision(self, env_command_information, env_observation):
         )
 
         # Step 2. Get maneuver challenge and mark the conflict vehicles and vrus.
-        env_maneuver_challenge, env_command_information = get_environment_maneuver_challenge(
+        env_maneuver_challenge, env_command_information, conflict_veh_info = get_environment_maneuver_challenge(
             env_future_trajectory,
             env_observation,
             env_command_information,
             centered_agent_set=self.excluded_agent_set,
         )
 
+        # Step 2.5. Save the conflict vehicle information
+        if conflict_veh_info:
+            self.save_conflict_info(conflict_veh_info)
+
         # Step 3. Add collision avoidance and acceptance command for the victim vehicles.
         env_future_trajectory, env_command_information = add_avoid_accept_collision_command(
             env_future_trajectory,

packages/terasim-nde-nade/terasim_nde_nade/envs/nade_with_av.py

@@ -1,5 +1,6 @@
 from addict import Dict
 import copy
+import json
 from loguru import logger
 import numpy as np
 import random
@@ -995,3 +996,21 @@ def collect_drl_obs(self, env_command_information):
         )
         total_obs_for_DRL = np.clip(total_obs_for_DRL, -5, 5)
         return np.array(total_obs_for_DRL).astype(float)
+
+    def save_conflict_info(self, conflict_veh_info):
+        sim_time = utils.get_time()
+
+        conflict_info_file = self.simulator.output_path / "conflict_info.jsonl"
+
+        vehicle_conflict_info = conflict_veh_info.get(AgentType.VEHICLE, {})
+        if not vehicle_conflict_info:
+            return
+
+        record = {
+            "timestamp": sim_time,
+            "conflict_veh_info": vehicle_conflict_info
+        }
+        # write JSON Lines file in append mode
+        with open(conflict_info_file, "a") as f:
+            f.write(json.dumps(record) + "\n")
+        return

packages/terasim-nde-nade/terasim_nde_nade/utils/nade/maneuver_challenge.py

@@ -166,6 +166,7 @@ def get_environment_maneuver_challenge(env_future_trajectory, env_observation, e
     Returns:
         dict: Environment maneuver challenge information.
         dict: Updated environment command information.
+        dict: Conflict vehicle information.
     """
     normal_future_trajectory_veh = Dict(
         {
@@ -276,5 +277,4 @@ def get_environment_maneuver_challenge(env_future_trajectory, env_observation, e
         AgentType.VEHICLE: maneuver_challenge_veh,
         AgentType.VULNERABLE_ROAD_USER: maneuver_challenge_vru,
     }
-    return env_maneuver_challenge, env_command_information
-
+    return env_maneuver_challenge, env_command_information, conflict_vehicle_info

scripts/gen_case_NADE.py

@@ -0,0 +1,99 @@
+import argparse
+import random
+import hydra
+from datetime import datetime
+from loguru import logger
+from omegaconf import DictConfig, OmegaConf
+from pathlib import Path
+from tqdm import tqdm
+from terasim.logger.infoextractor import InfoExtractor
+from terasim.simulator import Simulator
+
+from terasim_nde_nade.envs import NADE, NADEWithAV
+from terasim_nde_nade.vehicle import NDEVehicleFactory
+from terasim_nde_nade.vru import NDEVulnerableRoadUserFactory
+
+# Import resolve_config_paths function
+from terasim_service.utils.base import resolve_config_paths
+
+# Add packages directory to sys path if needed
+# sys.path.append(str(Path(__file__).resolve().parent.parent))
+
+
+
+def main(config_path: str) -> None:
+    config = OmegaConf.load(config_path)
+
+    # Convert OmegaConf to dict for path resolution
+    config_dict = OmegaConf.to_container(config, resolve=True)
+
+    # Resolve all paths in config
+    config_dict = resolve_config_paths(config_dict, config_path)
+
+    # Convert back to OmegaConf for attribute access
+    config = OmegaConf.create(config_dict)
+
+    # Time (format: 2025-09-15_16-05-30)
+    timestamp = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
+
+    base_dir = Path(config.output.dir) / config.output.name / "raw_data" / timestamp
+    base_dir.mkdir(parents=True, exist_ok=True)
+
+    env = NADE(
+        vehicle_factory=NDEVehicleFactory(cfg=config.environment.parameters),
+        vru_factory=NDEVulnerableRoadUserFactory(cfg=config.environment.parameters),
+        info_extractor=InfoExtractor, 
+        log_flag=True,
+        log_dir=base_dir,
+        warmup_time_lb=config.environment.parameters.warmup_time_lb,
+        warmup_time_ub=config.environment.parameters.warmup_time_ub,
+        run_time=1200,
+        configuration=config.environment.parameters,
+    )
+
+    # Paths already resolved in config
+    sumo_net_file = config.input.sumo_net_file
+    sumo_config_file = config.input.sumo_config_file
+
+    sim = Simulator(
+        sumo_net_file_path=sumo_net_file,
+        sumo_config_file_path=sumo_config_file,
+        num_tries=10,
+        # gui_flag=config.simulator.parameters.gui_flag,
+        gui_flag=True,
+        realtime_flag=config.simulator.parameters.realtime_flag,
+        output_path=base_dir,
+        sumo_output_file_types=["fcd_all"],
+        traffic_scale=config.simulator.parameters.traffic_scale if hasattr(config.simulator.parameters, "traffic_scale") else 1,
+        additional_sumo_args=[
+            "--device.bluelight.explicit","true",
+        ],
+    )
+    sim.bind_env(env)
+
+    terasim_logger = logger.bind(name="terasim_nde_nade")
+    terasim_logger.info(f"terasim_nde_nade: Experiment started")
+
+    sim.run()
+
+
+if __name__ == "__main__":
+    # Get all yaml files in examples/scenarios directory
+    config_dir = Path(__file__).parent / "examples" / "scenarios"
+    # yaml_files = sorted(config_dir.glob("*.yaml"), key=lambda x: int(''.join(filter(str.isdigit, x.stem)) or '0'))
+    # yaml_files = ["examples/scenarios/cutin.yaml"]
+    yaml_files = [Path("examples/scenarios/Mcity_corner_case_generation.yaml")]
+    # Randomly shuffle yaml files
+    random.shuffle(yaml_files)
+
+    # Run experiments for each yaml file
+    for yaml_file in tqdm(yaml_files):
+        print(yaml_file)
+        logger.info(f"Running experiment with config: {yaml_file}")
+        main(str(yaml_file))
+        # try:
+        #     main(str(yaml_file))
+        # except Exception as e:
+        #     logger.error(f"Error running {yaml_file}: {e}")
+        #     # yaml_file.unlink()  # Delete the yaml file
+            # continue