CARLA_0.9.6/PythonAPI/carla/agents/navigation/carla_env.py [634:730]:
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        return next_obs, reward, done, info

    def finish(self):
        print('destroying actors.')
        for actor in self.actor_list:
            actor.destroy()
        print('\ndestroying %d vehicles' % len(self.vehicles_list))
        self.client.apply_batch([carla.command.DestroyActor(x) for x in self.vehicles_list])
        time.sleep(0.5)
        pygame.quit()
        print('done.')


class LocalPlannerModified(LocalPlanner):

    def __del__(self):
        pass  # otherwise it deletes our vehicle object

    def run_step(self):
        return super().run_step(debug=False)  # otherwise by default shows waypoints, that interfere with our camera


class RoamingAgentModified(Agent):
    """
    RoamingAgent implements a basic agent that navigates scenes making random
    choices when facing an intersection.

    This agent respects traffic lights and other vehicles.
    """

    def __init__(self, vehicle, follow_traffic_lights=True):
        """

        :param vehicle: actor to apply to local planner logic onto
        """
        super(RoamingAgentModified, self).__init__(vehicle)
        self._proximity_threshold = 10.0  # meters
        self._state = AgentState.NAVIGATING
        self._follow_traffic_lights = follow_traffic_lights

        # for throttle 0.5, 0.75, 1.0
        args_lateral_dict = {
            'K_P': 1.0,
            'K_D': 0.005,
            'K_I': 0.0,
            'dt': 1.0 / 20.0}
        opt_dict = {'lateral_control_dict': args_lateral_dict}

        self._local_planner = LocalPlannerModified(self._vehicle, opt_dict)

    def run_step(self, debug=False):
        """
        Execute one step of navigation.
        :return: carla.VehicleControl
        """

        # is there an obstacle in front of us?
        hazard_detected = False

        # retrieve relevant elements for safe navigation, i.e.: traffic lights
        # and other vehicles
        actor_list = self._world.get_actors()
        vehicle_list = actor_list.filter("*vehicle*")
        lights_list = actor_list.filter("*traffic_light*")

        # check possible obstacles
        vehicle_state, vehicle = self._is_vehicle_hazard(vehicle_list)
        if vehicle_state:
            if debug:
                print('!!! VEHICLE BLOCKING AHEAD [{}])'.format(vehicle.id))

            self._state = AgentState.BLOCKED_BY_VEHICLE
            hazard_detected = True

        # check for the state of the traffic lights
        light_state, traffic_light = self._is_light_red(lights_list)
        if light_state and self._follow_traffic_lights:
            if debug:
                print('=== RED LIGHT AHEAD [{}])'.format(traffic_light.id))

            self._state = AgentState.BLOCKED_RED_LIGHT
            hazard_detected = True

        if hazard_detected:
            control = self.emergency_stop()
        else:
            self._state = AgentState.NAVIGATING
            # standard local planner behavior
            control = self._local_planner.run_step()

        return control


if __name__ == '__main__':

    env = CarlaEnv(
        render_display=1,  # 0, 1
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CARLA_0.9.8/PythonAPI/carla/agents/navigation/carla_env.py [629:725]:
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        return next_obs, reward, done, info

    def finish(self):
        print('destroying actors.')
        for actor in self.actor_list:
            actor.destroy()
        print('\ndestroying %d vehicles' % len(self.vehicles_list))
        self.client.apply_batch([carla.command.DestroyActor(x) for x in self.vehicles_list])
        time.sleep(0.5)
        pygame.quit()
        print('done.')


class LocalPlannerModified(LocalPlanner):

    def __del__(self):
        pass  # otherwise it deletes our vehicle object

    def run_step(self):
        return super().run_step(debug=False)  # otherwise by default shows waypoints, that interfere with our camera


class RoamingAgentModified(Agent):
    """
    RoamingAgent implements a basic agent that navigates scenes making random
    choices when facing an intersection.

    This agent respects traffic lights and other vehicles.
    """

    def __init__(self, vehicle, follow_traffic_lights=True):
        """

        :param vehicle: actor to apply to local planner logic onto
        """
        super(RoamingAgentModified, self).__init__(vehicle)
        self._proximity_threshold = 10.0  # meters
        self._state = AgentState.NAVIGATING
        self._follow_traffic_lights = follow_traffic_lights

        # for throttle 0.5, 0.75, 1.0
        args_lateral_dict = {
            'K_P': 1.0,
            'K_D': 0.005,
            'K_I': 0.0,
            'dt': 1.0 / 20.0}
        opt_dict = {'lateral_control_dict': args_lateral_dict}

        self._local_planner = LocalPlannerModified(self._vehicle, opt_dict)

    def run_step(self, debug=False):
        """
        Execute one step of navigation.
        :return: carla.VehicleControl
        """

        # is there an obstacle in front of us?
        hazard_detected = False

        # retrieve relevant elements for safe navigation, i.e.: traffic lights
        # and other vehicles
        actor_list = self._world.get_actors()
        vehicle_list = actor_list.filter("*vehicle*")
        lights_list = actor_list.filter("*traffic_light*")

        # check possible obstacles
        vehicle_state, vehicle = self._is_vehicle_hazard(vehicle_list)
        if vehicle_state:
            if debug:
                print('!!! VEHICLE BLOCKING AHEAD [{}])'.format(vehicle.id))

            self._state = AgentState.BLOCKED_BY_VEHICLE
            hazard_detected = True

        # check for the state of the traffic lights
        light_state, traffic_light = self._is_light_red(lights_list)
        if light_state and self._follow_traffic_lights:
            if debug:
                print('=== RED LIGHT AHEAD [{}])'.format(traffic_light.id))

            self._state = AgentState.BLOCKED_RED_LIGHT
            hazard_detected = True

        if hazard_detected:
            control = self.emergency_stop()
        else:
            self._state = AgentState.NAVIGATING
            # standard local planner behavior
            control = self._local_planner.run_step()

        return control


if __name__ == '__main__':

    env = CarlaEnv(
        render_display=1,  # 0, 1
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