src.applications.UNECE_R79.unecer79_event_finder module

This module is responsible for finding events valid according to UNECE regulation 79.

It includes one class for the event finding. See details in its own documentation.

Contact person: Stefan Riedmaier Creation date: 25.06.2020 Python version: 3.8

class src.applications.UNECE_R79.unecer79_event_finder.UNECEr79EventFinder

Bases: object

This class is responsible for finding events valid according to UNECE regulation 79.

It includes two main functions called “find_lkft_events” and “cluster_lkft_events”. The former calls the remaining methods. See the respective documentations for more details.

check_lkft_conditions(responses_da, scenarios_kpi_da)

This function checks the validity of LKFT test scenarios according to UNECE-R79.

It checks that: - the lateral acceleration is between 80 and 90 % of aysmax, specified by the manufacturer. - the velocity is between vmin and vmax of the LKAS, specified by the manufacturer. - the LKAS was turned on by the driver - the LKAS was active (lane recognized, etc.)

Parameters:

  • responses_da (xr.DataArray) – array of response signals

  • scenarios_kpi_da (xr.DataArray) – data-array with scenario parameter values

Returns:

dictionary of mask arrays, array of absolute lateral acceleration

Return type:

(dict(np.ndarray), np.ndarray)

cluster_lkft_events(scenarios_mean_da)

This function clusters lkft events into bins to extract repetitions.

Parameters:

scenarios_mean_da (xr.DataArray) – array of scenario parameters (mean values of the time signals)

Returns:

array of clustered scenarios

Return type:

xr.DataArray

find_lkft_events(quantities_ts_da, qois_ts_da, scenarios_ts_da, scenarios_kpi_da)

This function extracts events from signals that satisfy the UNECE-R79 Lane Keeping Functional Test conditions.

Parameters:

  • quantities_ts_da (xr.DataArray) – data-array with quantity signals

  • qois_ts_da (xr.DataArray) – data-array with qoi signals

  • scenarios_ts_da (xr.DataArray) – data-array with scenario signals

  • scenarios_kpi_da (xr.DataArray) – data-array with scenario parameter values

Returns:

data-array with events of qoi resp. scenario signals

Return type:

(xr.DataArray, xr.DataArray)

pullup_lkft_glitches(mask_dict, idx_time, ay_abs)

This function pulls up short glitches in binary LKFT signals / conditions.

According to UNECE-R79, the ay condition can be torn under certain circumstances: “Notwithstanding the sentence above, for time periods of not more than 2 s the lateral acceleration of the system may exceed the specified value aysmax by not more than 40 %, while not exceeding the maximum value specified in the table in paragraph 5.6.2.1.3. of this Regulation by more than 0.3 m / s2.”

Parameters:

  • mask_dict (dict) – dictionary of mask arrays

  • idx_time – index of the time dimension in the mask arrays

  • ay_abs (np.ndarray) – array of absolute lateral acceleration

Returns:

dictionary of mask arrays without short glitches

Return type:

dict(np.ndarray)

static trim_lkft_signals(start_idx, event_length, idx_time, qois_ts_da, scenarios_ts_da)

This function trims the desired LKFT signals by using the event start indices and lengths.

Parameters:

  • start_idx (tuple) – start indices

  • event_length (np.ndarray) – length of the events

  • idx_time (int) – index of the time dimension

  • qois_ts_da (xr.DataArray) – data-array with qoi signals

  • scenarios_ts_da (xr.DataArray) – data-array with scenario signals

Returns:

data-array with events of qoi resp. scenario signals

Return type:

(xr.DataArray, xr.DataArray)