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The dataset viewer is not available for this split.
Cannot extract the features (columns) for the split 'train' of the config 'default' of the dataset.
Error code:   FeaturesError
Exception:    ParserError
Message:      Error tokenizing data. C error: Expected 1 fields in line 4, saw 2

Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 244, in compute_first_rows_from_streaming_response
                  iterable_dataset = iterable_dataset._resolve_features()
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 4408, in _resolve_features
                  features = _infer_features_from_batch(self.with_format(None)._head())
                                                        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2679, in _head
                  return next(iter(self.iter(batch_size=n)))
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2861, in iter
                  for key, pa_table in ex_iterable.iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2395, in _iter_arrow
                  yield from self.ex_iterable._iter_arrow()
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/csv/csv.py", line 198, in _generate_tables
                  for batch_idx, df in enumerate(csv_file_reader):
                                       ~~~~~~~~~^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/pandas/io/parsers/readers.py", line 1843, in __next__
                  return self.get_chunk()
                         ~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/pandas/io/parsers/readers.py", line 1985, in get_chunk
                  return self.read(nrows=size)
                         ~~~~~~~~~^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/pandas/io/parsers/readers.py", line 1923, in read
                  ) = self._engine.read(  # type: ignore[attr-defined]
                      ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                      nrows
                      ^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/pandas/io/parsers/c_parser_wrapper.py", line 234, in read
                  chunks = self._reader.read_low_memory(nrows)
                File "pandas/_libs/parsers.pyx", line 850, in pandas._libs.parsers.TextReader.read_low_memory
                File "pandas/_libs/parsers.pyx", line 905, in pandas._libs.parsers.TextReader._read_rows
                File "pandas/_libs/parsers.pyx", line 874, in pandas._libs.parsers.TextReader._tokenize_rows
                File "pandas/_libs/parsers.pyx", line 891, in pandas._libs.parsers.TextReader._check_tokenize_status
                File "pandas/_libs/parsers.pyx", line 2061, in pandas._libs.parsers.raise_parser_error
              pandas.errors.ParserError: Error tokenizing data. C error: Expected 1 fields in line 4, saw 2

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COMMTR Figure 6 — Agent-Pilot vs Human Pilots Landing Comparison

This package contains the data used to produce Figure 6 of the manuscript "Agent-Pilot" (submitted to Communications in Transportation Research, manuscript ID COMMTR-2026-0064), specifically Section 4.3.3 "Comparison with Human Pilots".

Figure 6 compares Agent-Pilot against ten trained human pilots on attitude control (pitch and bank angles, Fig. 6a–6b) and lateral tracking accuracy (cross-track error, XTE, Fig. 6c) during the final approach (0.2 nm / last ~0.3 nm segment) at the urban vertiport under clear and fog conditions, in Microsoft Flight Simulator (MSFS) 2024.

Directory structure

commtr_fig6_data/
├── README.md
├── SHA256SUMS
├── human_pilots_landing/          # Figure 6 comparison package (agent + human + figures)
│   ├── city_reset_pitch_bank.csv  # Agent-Pilot, Urban Clear, per-trial pitch/bank
│   ├── city_fog_pitch_bank.csv    # Agent-Pilot, Urban Fog, per-trial pitch/bank
│   ├── city_reset_xte.csv         # Agent-Pilot, Urban Clear, per-trial XTE
│   ├── city_fog_xte.csv           # Agent-Pilot, Urban Fog, per-trial XTE
│   ├── time_raw_statistics_0.3nm.csv  # HUMAN pilots, last-0.3-nm segment statistics
│   │                               #   (filename suffix -1 = Clear, -2 = Fog)
│   ├── compare_landing_data.py    # script that generates the comparison plots
│   ├── calculate_time_raw_statistics.py
│   ├── pitch_bank_comparison.png/.pdf/.svg   # Figure 6a–6b
│   └── xte_comparison.png/.pdf/.svg          # Figure 6c
├── agent_pitch_bank_results/      # Agent-Pilot per-trial pitch/bank for ALL 16 scenarios
│   ├── city_reset.csv, city_fog.csv, ... (16 scenario CSVs)
│   └── summary_statistics.csv
└── agent_xte_result/              # Agent-Pilot per-trial XTE for ALL 16 scenarios
    ├── city_reset.csv, city_fog.csv, ... (16 scenario CSVs)
    └── summary_statistics.csv

1. Agent-Pilot data (per-trial metrics)

  • agent_pitch_bank_results/ — per-trial pitch and bank statistics (pitch_abs_mean, pitch_abs_sd, bank_abs_mean, bank_abs_sd, total_rows) for the 16 vertiport–weather scenarios, plus summary_statistics.csv (scenario-level averages).
  • agent_xte_result/ — per-trial cross-track-error statistics (MAE, RMSE, SD, Max_XTE, Min_XTE, Max_Left, Max_Right, baseline_length) for the 16 scenarios, plus summary_statistics.csv.

The Urban Clear (city_reset) and Urban Fog (city_fog) rows are the Agent-Pilot data plotted in Figure 6; they are also copied into human_pilots_landing/ for the comparison.

2. Human pilots data (n = 10 per condition)

human_pilots_landing/time_raw_statistics_0.3nm.csv — statistics over the last 0.3 nautical mile segment of each human landing:

  • pitch_abs_mean, pitch_abs_sd, bank_abs_mean, bank_abs_sd
  • baseline_length_m, xte_abs_mean_m, xte_abs_sd_m, xte_abs_rmse_m
  • filename convention: YYYY-MM-DD-HH-MM<initials>-<n>.csv where -1 = Clear (Reset) condition and -2 = Fog condition.

compare_landing_data.py selects the first 10 trials of each condition (Clear, Fog) to match the ten human pilots reported in the manuscript and produces the two comparison figures.

3. Figures

  • pitch_bank_comparison.png/.pdf/.svg — Figure 6a–6b (pitch & bank, four sub-panels).
  • xte_comparison.png/.pdf/.svg — Figure 6c (XTE MAE / SD / RMSE).

4. Provenance

  • Server: sc2i (10.30.58.120), directory /data2/Chenglin/agent/flight_recorder/.
  • Raw per-trial flight telemetry (35 Hz recorder) for the full 173 Agent-Pilot trials is available at /data2/Chenglin/agent/flight_recorder/agent-pilot-recorder_All173/ (5.5 GB) and the successful subset at agent-pilot-recorder_successfull/ (4.7 GB); these are not included in this package but can be provided on request.

5. License / citation / contact

  • License: to be confirmed before release.
  • Citation: please cite the Agent-Pilot manuscript (COMMTR-2026-0064).
  • Contact: [author email to be added].
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