combine dataset function

scenarionet/examples/run_scenario.py

@@ -0,0 +1,50 @@
+import os
+
+from metadrive.envs.scenario_env import ScenarioEnv
+from metadrive.policy.replay_policy import ReplayEgoCarPolicy
+
+from scenarionet import SCENARIONET_DATASET_PATH
+
+if __name__ == '__main__':
+    dataset_path = os.path.join(SCENARIONET_DATASET_PATH, "nuscenes_no_mapping_test")
+
+    env = ScenarioEnv(
+        {
+            "use_render": True,
+            "agent_policy": ReplayEgoCarPolicy,
+            "manual_control": False,
+            "show_interface": True,
+            "show_logo": False,
+            "show_fps": False,
+            "num_scenarios": 10,
+            "horizon": 1000,
+            "no_static_vehicles": True,
+            "vehicle_config": dict(
+                show_navi_mark=False,
+                no_wheel_friction=True,
+                lidar=dict(num_lasers=120, distance=50, num_others=4),
+                lane_line_detector=dict(num_lasers=12, distance=50),
+                side_detector=dict(num_lasers=160, distance=50)
+            ),
+            "data_directory": dataset_path,
+        }
+    )
+    success = []
+    env.reset(force_seed=0)
+    while True:
+        env.reset(force_seed=env.current_seed + 1)
+        for t in range(10000):
+            o, r, d, info = env.step([0, 0])
+            assert env.observation_space.contains(o)
+            c_lane = env.vehicle.lane
+            long, lat, = c_lane.local_coordinates(env.vehicle.position)
+            # if env.config["use_render"]:
+            env.render(
+                text={
+                    "seed": env.engine.global_seed + env.config["start_scenario_index"],
+                }
+            )
+
+            if d and info["arrive_dest"]:
+                print("seed:{}, success".format(env.engine.global_random_seed))
+                break