SUMO-idm跟驰模型代码

SUMO-idm跟驰模型是一种基于间距-速度控制的车辆跟驰模型，其代码实现如下：

class IDMVehicle(AVLaneVehicle):
    """
    A vehicle using the IDM acceleration model.

    This vehicle implements the IDM car-following model. Additionally, it may
    use the MOBIL lane-changing model to change lanes.

    Parameters
    ----------
    * see parents *

    idm_params : dict, optional
        Dictionary with parameters for the IDM model. The default value is
        `{'a': 1.3, 'b': 2.0, 'v0': 30, 'T': 1.5, 's0': 2, 'delta': 4}`.
        For a description of the parameters, please refer to the `IDM model
        wikipedia page <https://en.wikipedia.org/wiki/Intelligent_driver_model>`_.

    Notes
    -----
    The MOBIL model is only used if the `lane_change_controller` is set to a
    `MobilLaneChanger` instance.

    Examples
    --------
    >>> from flow.controllers import IDMController, ContinuousRouter
    >>> from flow.core.params import VehicleParams, SumoParams
    >>>
    >>> sumo_params = SumoParams(sim_step=0.1, render=False)
    >>> vehicle_params = VehicleParams()
    >>> vehicle_params.add(
    ...     "human",
    ...     acceleration_controller=(IDMController, {}),
    ...     routing_controller=(ContinuousRouter, {}),
    ...     num_vehicles=20)
    >>>
    >>> # create a network and a scenario
    >>> from flow.networks import HighwayNetwork
    >>> from flow.scenarios import Scenario
    >>> from flow.core.params import NetParams
    >>> from flow.core.params import InitialConfig
    >>> from flow.scenarios import HighwayScenario
    >>> from flow.envs.ring.accel import IDMVehicle
    >>>
    >>> network = HighwayNetwork(
    ...     name='highway',
    ...     vehicles=vehicle_params,
    ...     net_params=NetParams(),
    ...     initial_config=InitialConfig(
    ...         spacing="uniform",
    ...         lanes_distribution=float("inf"),
    ...         lanes_count=2
    ...     )
    ... )
    >>> scenario = HighwayScenario(
    ...     name='highway',
    ...     generator_class=HighwayGenerator,
    ...     vehicles=vehicle_params,
    ...     net_params=NetParams(),
    ...     initial_config=InitialConfig(
    ...         spacing="uniform",
    ...         lanes_distribution=float("inf"),
    ...         lanes_count=2
    ...     )
    ... )
    >>>
    >>> # create the environment
    >>> from flow.envs import HighwayPOEnv
    >>> env = HighwayPOEnv(
    ...     env_params=EnvParams(),
    ...     sim_params=sumo_params,
    ...     scenario=scenario,
    ...     simulator='traci'
    ... )
    >>>
    >>> # run the simulation
    >>> obs = env.reset()
    >>> for i in range(100):
    ...     action = [1, 0]
    ...     obs, rewards, dones, info = env.step(action)
    ...     if i % 10 == 0:
    ...         env.render()
    >>> env.close()
    """

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)

        self.idm_params = kwargs.get(
            "idm_params", {'a': 1.3, 'b': 2.0, 'v0': 30, 'T': 1.5, 's0': 2,
                           'delta': 4})

    def get_accel(self, env):
        """See parent class."""
        leader = self.get_leader(env)

        if leader is None:
            return self.idm_params["a"]

        v = env.k.vehicle.get_speed(self.id)
        v_leader = env.k.vehicle.get_speed(leader)

        dv = max(0, v_leader - v)

        s = env.k.vehicle.get_headway(self.id)
        s_star = self.idm_params["s0"] + max(
            0, v * self.idm_params["T"] + v * dv /
            (2 * np.sqrt(self.idm_params["a"] * self.idm_params["b"])))

        return self.idm_params["a"] * (
            1 - np.power(v / self.idm_params["v0"], self.idm_params["delta"])
            - np.power(s_star / s, 2))

其中，`get_accel`方法是计算车辆加速度的主要函数。在该函数中，根据跟驰模型的公式计算出车辆的期望车头间距`s_star`，然后根据该期望车头间距计算车辆的加速度。具体来说，该模型主要包含以下几个参数：

- `a`：车辆加速度的最大值；
- `b`：车辆减速度的最大值；
- `v0`：车辆的期望速度；
- `T`：期望的时间间隔，即车辆跟前车保持的时间间隔；
- `s0`：车辆的最小车头间距；
- `delta`：车辆速度对加速度的影响因子。

通过调整这些参数，可以对车辆的行为进行调整。