Most efficient way to enlarge the active area of a binary series pandas?

I have a pandas dataframe df:

I want to enlarge the active part of the binary series Open by n units, but after grouping the dataframe by Car.

An active part is a group of consecutive 1 that is either surrounded by 0, or having only 0 as previous value, or having only 0 as next values. The case when the series has only 1 as value is ignored.

If n = 1, I want to get the following dataframe :

I can get the index of all active parts using the following code :

df = pd.DataFrame(
   {
      "Car": ["Audi A5"]*10 + ["Mercedes Class A"]*10,
      "Time" : list(range(10)) + list(range(10)),
      "Open" : [0,0,0,1,1,0,0,0,1,1,1,1,1,0,0,1,1,0,0,1]
   }
)

def enlarge(dataframe : pd.DataFrame, sensor : str, n : int = 1) -> pd.DataFrame:

    get_group_indexes = (
        lambda x: x.index[0]
        if x.index[-1] - x.index[0] >= 1
        else None
    )

    groups = (
        dataframe[sensor]
        .eq(0)
        .cumsum()[dataframe[sensor].ne(0)]
        .to_frame()
        .groupby(sensor)
        .apply(get_group_indexes)
        .dropna()
    )

    if groups.empty:
        return dataframe

    for index in groups:
        dataframe.loc[index-n:index, sensor] = 1

    return dataframe

It works when I don't have to group by Car but I want to group by this column before perfoming this transformation. Does someone hqs an idea how to achieve this efficiently using pandas tricks ? Thanks.