def create_run_scenario_overtake_random (Configure):
config = Configure
population = config.population
result = np.zeros((population, config.goal_num), float)
Vars = []
file_dir_sce = config.file_dir_sce
file_dir_data = config.file_dir_data
file_dir_eval = config.file_dir_eval
file_dir_var = config.file_dir_var
with open('MyScenario/Overtake.json', 'r', encoding='utf-8') as f:
ret_dic = json.load(f)
for key in ret_dic:
if key == "config":
configList = ret_dic[key]
configList["s0"] = random.uniform(config.ego_s0[0], config.ego_s0[1])
configList["v0"] = random.uniform(config.ego_v0[0], config.ego_v0[1])
ret_dic[key] = configList
Vars.extend([configList["s0"],configList["v0"]])
elif key == "traffic_signal":
ret_dic[key] = []
objDict = {"start_s": random.uniform(config.start_s[0], config.start_s[1]),
"end_s": random.uniform(config.end_s[0], config.end_s[1]),
"green_time": random.uniform(config.green_time[0], config.green_time[1]),
"yellow_time": random.uniform(config.yellow_time[0], config.yellow_time[1]),
"red_time": random.uniform(config.red_time[0], config.red_time[1])}
ret_dic[key].append(objDict)
Vars.extend([objDict["start_s"],objDict["end_s"],objDict["green_time"],objDict["yellow_time"],objDict["red_time"]])
elif key == "dynamic_obs":
obsList = ret_dic[key]
obsList[0]["pos_y"] = random.uniform(config.pos_y_1[0], config.pos_y_1[1])
obsList[0]["velo"] = random.uniform(config.velo_1[0], config.velo_1[1])
obsList[0]["acc"] = random.uniform(config.acc_1[0], config.acc_1[1])
obsList[0]["start_time"] = random.uniform(config.start_time_1[0], config.start_time_1[1])
obsList[1]["pos_y"] = random.uniform(config.pos_y_2[0], config.pos_y_2[1])
obsList[1]["velo"] = random.uniform(config.velo_2[0], config.velo_2[1])
obsList[1]["acc"] = random.uniform(config.acc_2[0], config.acc_2[1])
obsList[1]["start_time"] = random.uniform(config.start_time_2[0], config.start_time_2[1])
obsList[2]["pos_y"] = random.uniform(config.pos_y_3[0], config.pos_y_3[1])
obsList[2]["velo"] = random.uniform(config.velo_3[0], config.velo_3[1])
obsList[2]["acc"] = random.uniform(config.acc_3[0], config.acc_3[1])
obsList[2]["start_time"] = random.uniform(config.start_time_3[0], config.start_time_3[1])
for j in range(3):
Vars.extend([obsList[j]["pos_y"], obsList[j]["velo"], obsList[j]["acc"], obsList[j]["start_time"]])
traffic_light = ret_dic["traffic_signal"]
st_obsList = ret_dic["static_obs"]
dy_obsList = ret_dic["dynamic_obs"]
# print(traffic_light, st_obsList, dy_obsList)
now_time = get_time_stamp()
uuid_str = uuid.uuid4().hex
scenario_name = file_dir_sce + "/scenario_" + now_time + "_" + uuid_str + ".json"
# scenario_name = file_dir_sce + "/scenario_" + str(bestlog.round) +'-' + str(num_sce) + ".json"
# print(scenario_name)
with open(scenario_name, 'w', encoding='utf-8') as f:
json.dump(ret_dic, f, ensure_ascii=False, indent=4)
var_name = file_dir_var + "/var_" + now_time + "_" + uuid_str + ".txt"
with open(var_name, 'w', encoding='utf-8') as f:
json.dump(Vars, f, ensure_ascii=False, indent=4)
## run the scenario
duration = config.duration
log_name = file_dir_data + "/datalog_" + now_time + "_" + uuid_str + ".txt"
# cmd = "C:/Users/lenovo/Documents/GitHub/mazda-path-planner-sbt_changes/mazda-path-planner-sbt_changes/ERATO_planning/x64/Release/dynamic_cost.exe -c %d -v EGO_TESTER -i %s > %s" % (duration, scenario_name, log_name)
## weiming
cmd = "wine /gpfs/share/home/1801111354/Release/dynamic_cost.exe -c %d -v EGO_TESTER -i %s > %s" % (duration, scenario_name, log_name)
## amazon
# cmd = "wine /home/yixing/Release/dynamic_cost.exe -c %d -v EGO_TESTER -i %s > %s" % (duration, scenario_name, log_name)
## mac
# cmd = "wine /Users/luoyixing/Downloads/Release/dynamic_cost.exe -c %d -v EGO_TESTER -i %s > %s" % (duration, scenario_name, log_name)
# print(cmd)
start = time.clock()
os.system(cmd)
elapsed = (time.clock() - start)
# print('totally cost', elapsed)
num_dynamic_obs = 3
num_static_obs = 0
ego_vehicle_state = []
dynamic_vehicle_state = [[] for i in range(num_dynamic_obs)]
static_vehicle_state = [[] for i in range(num_static_obs)]
with open(log_name, 'r') as f:
my_data = f.readlines()
for line in my_data:
data = line.split()
if len(data) == 8 and data[0] == "EGO_STATUS":
log = []
for i in range(1, len(data)):
log.append(float(data[i]))
if len(log) == 7:
ego_vehicle_state.append(log)
if len(data) == 10 and data[0] == "DYNAMIC_OBS_INFO":
log = []
for i in range(2, len(data)):
log.append(float(data[i]))
# print(log)
if len(log) == 8:
dynamic_vehicle_state[int(data[1])].append(log)
elif len(data) == 5 and data[0] == "STATIC_OBS_INFO":
log = []
for i in range(2, len(data)):
log.append(float(data[i]))
# print(log)
if len(log) == 3:
static_vehicle_state[int(data[1])].append(log)
comfort1, comfort2 = evaluate_comfort(ego_vehicle_state, config)
avg_speed, min_speed = evaluate_speed(ego_vehicle_state, config)
# min_dis, avg_dis_satisfaction, min_dis_satisfaction = evaluate_distance(ego_vehicle_state, dynamic_vehicle_state,
# dy_obsList, static_vehicle_state, st_obsList, config)
min_dis, avg_dis_satisfaction, min_dis_satisfaction = evaluate_collision (ego_vehicle_state, dynamic_vehicle_state, dy_obsList, static_vehicle_state, st_obsList, config)
avg_stable, min_stable = evaluate_stability(ego_vehicle_state, config)
traffic_light = evaluate_traffic_light(ego_vehicle_state, traffic_light)
cross_lane = evaluate_cross_lane(ego_vehicle_state)
result = [min_stable, min_dis, min_speed, traffic_light, cross_lane, comfort1, comfort2]
print("Results:", len(result), result)
result_name = file_dir_eval + "/result_" + now_time + "_" + uuid_str + ".txt"
# print(result_name)
np.savetxt(result_name, result, fmt="%f", delimiter=" ")
return result
def create_run_scenario_overtake (Vars, Configure):
config = Configure
population = config.population
result = np.zeros((population, config.goal_num), float)
file_dir_sce = config.file_dir_sce
file_dir_data = config.file_dir_data
file_dir_eval = config.file_dir_eval
file_dir_var = config.file_dir_var
with open('MyScenario/Overtake.json', 'r', encoding='utf-8') as f:
ret_dic = json.load(f)
for key in ret_dic:
if key == "config":
configList = ret_dic[key]
configList["s0"] = Vars[0]
configList["v0"] = Vars[1]
ret_dic[key] = configList
elif key == "traffic_signal":
ret_dic[key] = []
objDict = {"start_s": Vars[2],
"end_s": Vars[3],
"green_time": Vars[4],
"yellow_time": Vars[5],
"red_time": Vars[6]}
ret_dic[key].append(objDict)
# elif key == "static_obs":
# obsList = ret_dic[key]
# obsList[0]["pos_s"] = Vars[7]
# obsList[1]["pos_s"] = Vars[8]
elif key == "dynamic_obs":
obsList = ret_dic[key]
obsList[0]["pos_y"] = Vars[7]
obsList[0]["velo"] = Vars[8]
obsList[0]["acc"] = Vars[9]
obsList[0]["start_time"] = Vars[10]
obsList[1]["pos_y"] = Vars[11]
obsList[1]["velo"] = Vars[12]
obsList[1]["acc"] = Vars[13]
obsList[1]["start_time"] = Vars[14]
obsList[2]["pos_y"] = Vars[15]
obsList[2]["velo"] = Vars[16]
obsList[2]["acc"] = Vars[17]
obsList[2]["start_time"] = Vars[18]
traffic_light = ret_dic["traffic_signal"]
st_obsList = ret_dic["static_obs"]
dy_obsList = ret_dic["dynamic_obs"]
# print(traffic_light, st_obsList, dy_obsList)
# global bestpop
# bestlog = globalvar.get_value('BestPop')
# print("\033[1;32m scenario round: \033[0m", bestlog.round)
now_time = get_time_stamp()
uuid_str = uuid.uuid4().hex
scenario_name = file_dir_sce + "/scenario_" + now_time + "_" + uuid_str + ".json"
# scenario_name = file_dir_sce + "/scenario_" + str(bestlog.round) +'-' + str(num_sce) + ".json"
# print(scenario_name)
with open(scenario_name, 'w', encoding='utf-8') as f:
json.dump(ret_dic, f, ensure_ascii=False, indent=4)
var_name = file_dir_var + "/var_" + now_time + "_" + uuid_str + ".txt"
# with open(var_name, 'w', encoding='utf-8') as f:
# json.dump(Vars, f, ensure_ascii=False, indent=4)
np.savetxt(var_name, Vars, fmt="%f", delimiter=" ")
## run the scenario
duration = config.duration
# file_path = os.path.abspath(os.path.join(os.getcwd(), "../Random"))
# print(file_path)
log_name = file_dir_data + "/datalog_" + now_time + "_" + uuid_str + ".txt"
# cmd = "C:/Users/lenovo/Documents/GitHub/mazda-path-planner-sbt_changes/mazda-path-planner-sbt_changes/ERATO_planning/x64/Release/dynamic_cost.exe -c %d -v EGO_TESTER -i %s > %s" % (duration, scenario_name, log_name)
## weiming
cmd = "wine /gpfs/share/home/1801111354/Release/dynamic_cost.exe -c %d -v EGO_TESTER -i %s > %s" % (duration, scenario_name, log_name)
## amazon
# cmd = "wine /home/yixing/Release/dynamic_cost.exe -c %d -v EGO_TESTER -i %s > %s" % (duration, scenario_name, log_name)
## mac
# cmd = "wine /Users/luoyixing/Downloads/Release/dynamic_cost.exe -c %d -v EGO_TESTER -i %s > %s" % (duration, scenario_name, log_name)
# print(cmd)
start = time.clock()
os.system(cmd)
elapsed = (time.clock() - start)
# print('totally cost', elapsed)
num_dynamic_obs = 3
num_static_obs = 0
ego_vehicle_state = []
dynamic_vehicle_state = [[] for i in range(num_dynamic_obs)]
static_vehicle_state = [[] for i in range(num_static_obs)]
with open(log_name, 'r') as f:
my_data = f.readlines() # txt�������ַ�������data���õ�����һ��list
# ��list�е��������ָ�������ת��
# for line in my_data:
# line_data = line.split()
# numbers_float = map(float, line_data) # ת��Ϊ������
for line in my_data:
data = line.split()
if len(data) == 8 and data[0] == "EGO_STATUS":
log = []
for i in range(1, len(data)):
log.append(float(data[i]))
if len(log) == 7:
ego_vehicle_state.append(log)
if len(data) == 10 and data[0] == "DYNAMIC_OBS_INFO":
log = []
for i in range(2, len(data)):
log.append(float(data[i]))
# print(log)
if len(log) == 8:
dynamic_vehicle_state[int(data[1])].append(log)
elif len(data) == 5 and data[0] == "STATIC_OBS_INFO":
log = []
for i in range(2, len(data)):
log.append(float(data[i]))
# print(log)
if len(log) == 3:
static_vehicle_state[int(data[1])].append(log)
comfort1, comfort2 = evaluate_comfort(ego_vehicle_state, config)
avg_speed, min_speed = evaluate_speed(ego_vehicle_state, config)
# min_dis, avg_dis_satisfaction, min_dis_satisfaction = evaluate_distance(ego_vehicle_state, dynamic_vehicle_state,
# dy_obsList, static_vehicle_state, st_obsList, config)
min_dis, avg_dis_satisfaction, min_dis_satisfaction = evaluate_collision (ego_vehicle_state, dynamic_vehicle_state, dy_obsList, static_vehicle_state, st_obsList, config)
avg_stable, min_stable = evaluate_stability(ego_vehicle_state, config)
traffic_light = evaluate_traffic_light(ego_vehicle_state, traffic_light)
cross_lane = evaluate_cross_lane(ego_vehicle_state)
# result = [avg_stable, min_stable, avg_dis_satisfaction, min_dis_satisfaction, avg_speed, min_speed, traffic_light, cross_lane, comfort1,
# comfort2]
#
#
# result = [avg_stable, avg_dis_satisfaction, min_dis_satisfaction, avg_speed, min_speed, traffic_light,
# cross_lane, comfort1, comfort2]
result = [min_stable, min_dis, min_speed, traffic_light, cross_lane, comfort1, comfort2]
# result = [-min_dis, -min_stable, min_speed, traffic_light, -cross_lane, -comfort1, comfort2]
# global _global_dict
# _global_dict
# BestPopulation.round = 2
# print(BestPopulation.round)
# bestlog = globalvar.get_value('BestPop')
# print("Scenario:", BestPopulation.round)
# print("Variables:", Vars)
# print("Results:", len(result), result)
# print("Weights:", bestlog.weights)
# print("Round: %d" %(bestpop.round))
result_name = file_dir_eval + "/result_" + now_time + "_" + uuid_str + ".txt"
# print(result_name)
np.savetxt(result_name, result, fmt="%f", delimiter=" ")
# if Configure.algorithm == 'NSGA_III_Adapt':
# weights = BestPop.weights
# for i in range (config.goal_num):
# result[i] = weights[i] * result[i]
# print("Results after weight:", result)
return result
