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traffic.py
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/
traffic.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# =====================================
# @Time : 2020/11/08
# @Author : Yang Guan (Tsinghua Univ.)
# @FileName: traffic.py
# =====================================
import copy
import math
import os
import random
import sys
from collections import defaultdict
from math import fabs, cos, sin, pi
if 'SUMO_HOME' in os.environ:
tools = os.path.join(os.environ['SUMO_HOME'], 'tools')
sys.path.append(tools)
else:
sys.exit("please declare environment variable 'SUMO_HOME'")
import sumolib
from sumolib import checkBinary
import traci
from traci.exceptions import FatalTraCIError
from gym.envs.user_defined.toyota_3way.endtoend_env_utils import shift_and_rotate_coordination, _convert_car_coord_to_sumo_coord, \
_convert_sumo_coord_to_car_coord, xy2_edgeID_lane, SUMOCFG_DIR
SUMO_BINARY = checkBinary('sumo')
SIM_PERIOD = 1.0 / 10
class Traffic(object):
def __init__(self, step_length, mode, init_n_ego_dict, training_task='left'): # mode 'display' or 'training'
self.random_traffic = None
self.sim_time = 0
self.n_ego_vehicles = defaultdict(list)
self.step_length = step_length
self.step_time_str = str(float(step_length) / 1000)
self.collision_flag = False
self.n_ego_collision_flag = {}
self.collision_ego_id = None
self.v_light = None
self.n_ego_dict = init_n_ego_dict
# dict(DL1=dict(x=1.875, y=-30, v=3, a=90, l=4.8, w=2.2),
# UR1=dict(x=-1.875, y=30, v=3, a=-90, l=4.8, w=2.2),
# DR1=dict(x=5.625, y=-30, v=3, a=90, l=4.8, w=2.2),
# RU1=dict(x=5.625, y=-30, v=3, a=90, l=4.8, w=2.2))
self.mode = mode
self.training_light_phase = 0
self.training_task = training_task
self.first_add = True
if training_task == 'right':
if random.random() > 0.5:
self.training_light_phase = 2
try:
traci.start(
[SUMO_BINARY, "-c", SUMOCFG_DIR,
"--step-length", self.step_time_str,
"--lateral-resolution", "3.75",
"--random",
# "--start",
# "--quit-on-end",
"--no-warnings",
"--no-step-log",
# '--seed', str(int(seed))
], numRetries=5) # '--seed', str(int(seed))
except FatalTraCIError:
print('Retry by other port')
port = sumolib.miscutils.getFreeSocketPort()
traci.start(
[SUMO_BINARY, "-c", SUMOCFG_DIR,
"--step-length", self.step_time_str,
"--lateral-resolution", "1.25",
"--random",
# "--start",
# "--quit-on-end",
"--no-warnings",
"--no-step-log",
# '--seed', str(int(seed))
], port=port, numRetries=5) # '--seed', str(int(seed))
traci.vehicle.subscribeContext('collector',
traci.constants.CMD_GET_VEHICLE_VARIABLE,
999999, [traci.constants.VAR_POSITION,
traci.constants.VAR_LENGTH,
traci.constants.VAR_WIDTH,
traci.constants.VAR_ANGLE,
traci.constants.VAR_SIGNALS,
traci.constants.VAR_SPEED,
# traci.constants.VAR_TYPE,
# traci.constants.VAR_EMERGENCY_DECEL,
# traci.constants.VAR_LANE_INDEX,
# traci.constants.VAR_LANEPOSITION,
traci.constants.VAR_EDGES,
# traci.constants.VAR_ROUTE_INDEX
],
0, 2147483647)
while traci.simulation.getTime() < 100:
if traci.simulation.getTime() < 80:
traci.trafficlight.setPhase('0', 2)
else:
traci.trafficlight.setPhase('0', 0)
# if self.mode == "training":
# traci.trafficlight.setPhase('0', self.training_light_phase)
traci.simulationStep()
def __del__(self):
traci.close()
def add_self_car(self, n_ego_dict):
for egoID, ego_dict in n_ego_dict.items():
ego_v_x = ego_dict['v_x']
ego_v_y = ego_dict['v_y']
ego_l = ego_dict['l']
ego_x = ego_dict['x']
ego_y = ego_dict['y']
ego_phi = ego_dict['phi']
ego_x_in_sumo, ego_y_in_sumo, ego_a_in_sumo = _convert_car_coord_to_sumo_coord(ego_x, ego_y, ego_phi, ego_l)
edgeID, lane = xy2_edgeID_lane(ego_x, ego_y)
if self.first_add:
traci.vehicle.addLegacy(vehID=egoID, routeID=ego_dict['routeID'],
depart=0, pos=20, lane=1, speed=ego_dict['v_x'],
typeID='self_car')
try:
traci.vehicle.moveToXY(egoID, edgeID, lane, ego_x_in_sumo, ego_y_in_sumo, ego_a_in_sumo, keepRoute=1)
except traci.exceptions.TraCIException:
print('Don\'t worry, it\'s handled well')
traci.vehicle.addLegacy(vehID=egoID, routeID=ego_dict['routeID'],
depart=0, pos=20, lane=1, speed=ego_dict['v_x'],
typeID='self_car')
traci.vehicle.moveToXY(egoID, edgeID, lane, ego_x_in_sumo, ego_y_in_sumo, ego_a_in_sumo, keepRoute=1)
traci.vehicle.setLength(egoID, ego_dict['l'])
traci.vehicle.setWidth(egoID, ego_dict['w'])
traci.vehicle.setSpeed(egoID, math.sqrt(ego_v_x ** 2 + ego_v_y ** 2))
self.first_add = False
def generate_random_traffic(self):
random_traffic = traci.vehicle.getContextSubscriptionResults('collector')
random_traffic = copy.deepcopy(random_traffic)
for ego_id in self.n_ego_dict.keys():
if ego_id in random_traffic:
del random_traffic[ego_id]
return random_traffic
def init_traffic(self, init_n_ego_dict):
self.sim_time = 0
self.n_ego_vehicles = defaultdict(list)
self.collision_flag = False
self.n_ego_collision_flag = {}
self.collision_ego_id = None
self.v_light = None
self.training_light_phase = 0
if self.training_task == 'right':
if random.random() > 0.5:
self.training_light_phase = 2
self.n_ego_dict = init_n_ego_dict
traci.trafficlight.setPhase('0', self.training_light_phase)
random_traffic = self.generate_random_traffic()
self.add_self_car(init_n_ego_dict)
# move ego to the given position and remove conflict cars
for egoID, ego_dict in self.n_ego_dict.items():
ego_x, ego_y, ego_v_x, ego_v_y, ego_phi, ego_l, ego_w = ego_dict['x'], ego_dict['y'], ego_dict['v_x'],\
ego_dict['v_y'], ego_dict['phi'], ego_dict['l'], \
ego_dict['w']
for veh in random_traffic:
x_in_sumo, y_in_sumo = random_traffic[veh][traci.constants.VAR_POSITION]
a_in_sumo = random_traffic[veh][traci.constants.VAR_ANGLE]
veh_l = random_traffic[veh][traci.constants.VAR_LENGTH]
veh_v = random_traffic[veh][traci.constants.VAR_SPEED]
# veh_sig = random_traffic[veh][traci.constants.VAR_SIGNALS]
# 10: left and brake 9: right and brake 1: right 8: brake 0: no signal 2: left
x, y, a = _convert_sumo_coord_to_car_coord(x_in_sumo, y_in_sumo, a_in_sumo, veh_l)
x_in_ego_coord, y_in_ego_coord, a_in_ego_coord = shift_and_rotate_coordination(x, y, a, ego_x,
ego_y, ego_phi)
ego_x_in_veh_coord, ego_y_in_veh_coord, ego_a_in_veh_coord = shift_and_rotate_coordination(0, 0, 0,
x_in_ego_coord,
y_in_ego_coord,
a_in_ego_coord)
if (-5 < x_in_ego_coord < 1 * (ego_v_x) + ego_l/2. + veh_l/2. + 2 and abs(y_in_ego_coord) < 3) or \
(-5 < ego_x_in_veh_coord < 1 * (veh_v) + ego_l/2. + veh_l/2. + 2 and abs(ego_y_in_veh_coord) <3):
traci.vehicle.moveToXY(veh, '4i', 1, -80, 1.85, 180, 2)
# traci.vehicle.remove(vehID=veh)
# if 0<x_in_sumo<3.5 and -22<y_in_sumo<-15:# and veh_sig!=1 and veh_sig!=9:
# traci.vehicle.moveToXY(veh, '4o', 1, -80, 1.85, 180,2)
# traci.vehicle.remove(vehID=veh)
def _get_vehicles(self):
self.n_ego_vehicles = defaultdict(list)
veh_infos = traci.vehicle.getContextSubscriptionResults('collector')
for egoID in self.n_ego_dict.keys():
veh_info_dict = copy.deepcopy(veh_infos)
for i, veh in enumerate(veh_info_dict):
if veh != egoID:
length = veh_info_dict[veh][traci.constants.VAR_LENGTH]
width = veh_info_dict[veh][traci.constants.VAR_WIDTH]
route = veh_info_dict[veh][traci.constants.VAR_EDGES]
if route[0] == '4i':
continue
x_in_sumo, y_in_sumo = veh_info_dict[veh][traci.constants.VAR_POSITION]
a_in_sumo = veh_info_dict[veh][traci.constants.VAR_ANGLE]
# transfer x,y,a in car coord
x, y, a = _convert_sumo_coord_to_car_coord(x_in_sumo, y_in_sumo, a_in_sumo, length)
v = veh_info_dict[veh][traci.constants.VAR_SPEED]
self.n_ego_vehicles[egoID].append(dict(x=x, y=y, v=v, phi=a, l=length,
w=width, route=route))
def _get_traffic_light(self):
self.v_light = traci.trafficlight.getPhase('0')
def sim_step(self):
self.sim_time += SIM_PERIOD
if self.mode == 'training':
traci.trafficlight.setPhase('0', self.training_light_phase)
traci.simulationStep()
self._get_vehicles()
self._get_traffic_light()
self.collision_check()
for egoID, collision_flag in self.n_ego_collision_flag.items():
if collision_flag:
self.collision_flag = True
self.collision_ego_id = egoID
def set_own_car(self, n_ego_dict_):
assert len(self.n_ego_dict) == len(n_ego_dict_)
for egoID in self.n_ego_dict.keys():
self.n_ego_dict[egoID]['v_x'] = ego_v_x = n_ego_dict_[egoID]['v_x']
self.n_ego_dict[egoID]['v_y'] = ego_v_y = n_ego_dict_[egoID]['v_y']
self.n_ego_dict[egoID]['r'] = ego_r = n_ego_dict_[egoID]['r']
self.n_ego_dict[egoID]['x'] = ego_x = n_ego_dict_[egoID]['x']
self.n_ego_dict[egoID]['y'] = ego_y = n_ego_dict_[egoID]['y']
self.n_ego_dict[egoID]['phi'] = ego_phi = n_ego_dict_[egoID]['phi']
ego_x_in_sumo, ego_y_in_sumo, ego_a_in_sumo = _convert_car_coord_to_sumo_coord(ego_x, ego_y, ego_phi,
self.n_ego_dict[egoID]['l'])
egdeID, lane = xy2_edgeID_lane(ego_x, ego_y)
keeproute = 1
# if self.training_task == 'left': # TODO
# keeproute = 2 if ego_x > 0 and ego_y > -7 else 1
try:
traci.vehicle.moveToXY(egoID, egdeID, lane, ego_x_in_sumo, ego_y_in_sumo, ego_a_in_sumo, keeproute)
except traci.exceptions.TraCIException:
print(egoID, egdeID, lane, ego_x_in_sumo, ego_y_in_sumo, ego_a_in_sumo, keeproute)
traci.vehicle.moveToXY(egoID, egdeID, lane, ego_x_in_sumo, ego_y_in_sumo, ego_a_in_sumo, keeproute)
traci.vehicle.setSpeed(egoID, math.sqrt(ego_v_x**2+ego_v_y**2))
def collision_check(self): # True: collision
flag_dict = dict()
for egoID, list_of_veh_dict in self.n_ego_vehicles.items():
ego_x = self.n_ego_dict[egoID]['x']
ego_y = self.n_ego_dict[egoID]['y']
ego_phi = self.n_ego_dict[egoID]['phi']
ego_l = self.n_ego_dict[egoID]['l']
ego_w = self.n_ego_dict[egoID]['w']
ego_lw = (ego_l - ego_w) / 2
ego_x0 = (ego_x + cos(ego_phi / 180 * pi) * ego_lw)
ego_y0 = (ego_y + sin(ego_phi / 180 * pi) * ego_lw)
ego_x1 = (ego_x - cos(ego_phi / 180 * pi) * ego_lw)
ego_y1 = (ego_y - sin(ego_phi / 180 * pi) * ego_lw)
flag_dict[egoID] = False
for veh in list_of_veh_dict:
if fabs(veh['x'] - ego_x) < 10 and fabs(veh['y'] - ego_y) < 10:
surrounding_lw = (veh['l'] - veh['w']) / 2
surrounding_x0 = (veh['x'] + cos(veh['phi'] / 180 * pi) * surrounding_lw)
surrounding_y0 = (veh['y'] + sin(veh['phi'] / 180 * pi) * surrounding_lw)
surrounding_x1 = (veh['x'] - cos(veh['phi'] / 180 * pi) * surrounding_lw)
surrounding_y1 = (veh['y'] - sin(veh['phi'] / 180 * pi) * surrounding_lw)
collision_check_dis = ((veh['w'] + ego_w) / 2 + 0.5) ** 2
if (ego_x0 - surrounding_x0) ** 2 + (ego_y0 - surrounding_y0) ** 2 < collision_check_dis:
flag_dict[egoID] = True
elif (ego_x0 - surrounding_x1) ** 2 + (ego_y0 - surrounding_y1) ** 2 < collision_check_dis:
flag_dict[egoID] = True
elif (ego_x1 - surrounding_x1) ** 2 + (ego_y1 - surrounding_y1) ** 2 < collision_check_dis:
flag_dict[egoID] = True
elif (ego_x1 - surrounding_x0) ** 2 + (ego_y1 - surrounding_y0) ** 2 < collision_check_dis:
flag_dict[egoID] = True
self.n_ego_collision_flag = flag_dict
def test_traffic():
import numpy as np
from dynamics_and_models import ReferencePath
def _reset_init_state():
ref_path = ReferencePath('straight')
random_index = int(np.random.random()*(900+500)) + 700
x, y, phi = ref_path.indexs2points(random_index)
v = 8 * np.random.random()
return dict(ego=dict(v_x=v,
v_y=0,
r=0,
x=x.numpy(),
y=y.numpy(),
phi=phi.numpy(),
l=4.8,
w=2.2,
routeID='du',
))
init_state = dict(ego=dict(v_x=8., v_y=0, r=0, x=-30, y=1.5, phi=180, l=4.8, w=2.2, routeID='dl',))
# init_state = _reset_init_state()
traffic = Traffic(100., mode='training', init_n_ego_dict=init_state, training_task='left')
traffic.init_traffic(init_state)
traffic.sim_step()
for i in range(100000000):
# for j in range(50):
# traffic.set_own_car(init_state)
# traffic.sim_step()
# init_state = _reset_init_state()
# traffic.init_traffic(init_state)
traffic.sim_step()
if __name__ == "__main__":
test_traffic()