param_server["EvalutaorRss"]["MapFilename"] = map_path
param_server["EvalutaorRss"][
"DefaultVehicleDynamics"] = default_vehicle_dynamics
param_server["EvalutaorRss"][
"SpecificAgentVehicleDynamics"] = agents_vehicle_dynamics
param_server["EvalutaorRss"]["CheckingRelevantRange"] = 1
# run n scenarios
for episode in range(0, 10):
env.reset()
current_world = env._world
eval_agent_id = env._scenario._eval_agent_ids[0]
# There are two ways to upset EvaluatorRss
# evaluator_rss = EvaluatorRss(eval_agent_id, map_path,
# default_vehicle_dynamics,
# agents_vehicle_dynamics,
# checking_relevent_range=1)
evaluator_rss = EvaluatorRss(eval_agent_id, param_server)
current_world.AddEvaluator("rss", evaluator_rss)
# step each scenario 40 times
for step in range(0, 40):
env.step()
print_rss_safety_response(evaluator_rss, current_world)
time.sleep(sim_step_time / sim_real_time_factor)
viewer.export_video(filename="/tmp/merging_rss", remove_image_dir=False)
viewer = MPViewer(params=param_server,
x_range=[-35, 35],
y_range=[-35, 35],
follow_agent_id=True)
sim_step_time = param_server["simulation"]["step_time",
"Step-time used in simulation", 0.2]
sim_real_time_factor = param_server["simulation"][
"real_time_factor", "execution in real-time or faster", 1.]
viewer = VideoRenderer(renderer=viewer, world_step_time=sim_step_time)
env = Runtime(step_time=0.2,
viewer=viewer,
scenario_generator=scenarios,
render=True,
maintain_world_history=True)
# run 3 scenarios
for _ in range(0, 3):
env.reset()
# step each scenario 20 times
for step in range(0, 40):
env.step()
time.sleep(sim_step_time / sim_real_time_factor)
df = env.ExtractTimeSeries()
print(df)
viewer.export_video(filename="./example_video", remove_image_dir=False)