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)