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Cannot extract the features (columns) for the split 'train' of the config 'default' of the dataset.
Error code:   FeaturesError
Exception:    UnicodeDecodeError
Message:      'utf-8' codec can't decode byte 0xd3 in position 223: invalid continuation byte
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 243, in compute_first_rows_from_streaming_response
                  iterable_dataset = iterable_dataset._resolve_features()
                                     ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 3496, in _resolve_features
                  features = _infer_features_from_batch(self.with_format(None)._head())
                                                        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2257, in _head
                  return next(iter(self.iter(batch_size=n)))
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2461, in iter
                  for key, example in iterator:
                                      ^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 1952, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 1974, in _iter_arrow
                  yield from self.ex_iterable._iter_arrow()
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 503, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 350, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/csv/csv.py", line 188, in _generate_tables
                  csv_file_reader = pd.read_csv(file, iterator=True, dtype=dtype, **self.config.pd_read_csv_kwargs)
                                    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/streaming.py", line 73, in wrapper
                  return function(*args, download_config=download_config, **kwargs)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/utils/file_utils.py", line 1199, in xpandas_read_csv
                  return pd.read_csv(xopen(filepath_or_buffer, "rb", download_config=download_config), **kwargs)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/pandas/io/parsers/readers.py", line 1026, in read_csv
                  return _read(filepath_or_buffer, kwds)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/pandas/io/parsers/readers.py", line 620, in _read
                  parser = TextFileReader(filepath_or_buffer, **kwds)
                           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/pandas/io/parsers/readers.py", line 1620, in __init__
                  self._engine = self._make_engine(f, self.engine)
                                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/pandas/io/parsers/readers.py", line 1898, in _make_engine
                  return mapping[engine](f, **self.options)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/pandas/io/parsers/c_parser_wrapper.py", line 93, in __init__
                  self._reader = parsers.TextReader(src, **kwds)
                                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "pandas/_libs/parsers.pyx", line 574, in pandas._libs.parsers.TextReader.__cinit__
                File "pandas/_libs/parsers.pyx", line 663, in pandas._libs.parsers.TextReader._get_header
                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 2053, in pandas._libs.parsers.raise_parser_error
                File "<frozen codecs>", line 322, in decode
              UnicodeDecodeError: 'utf-8' codec can't decode byte 0xd3 in position 223: invalid continuation byte

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Interactive behavior analysis relies on high-quality time-series or event-based data to derive actionable insights—whether for performance optimization, behavior pattern recognition, or trend forecasting. This dataset, stored in CSV format, provides structured records of interaction modes, interaction actions, operation objectives, application names or IDs, interface descriptions, and more, enabling users to conduct in-depth analysis, build predictive models, or visualize key metrics.

Dataset Details

This dataset is presented in standard CSV (comma separated values) format, ensuring broad compatibility with data processing tools such as Pandas, Excel, Python. It contains over 10,000+ rows of time stamped records, covering the entire process of multiple users interacting with the intelligent car cockpit system from boarding to disembarking.

Data Format

  • Primary Format: CSV (Comma-Separated Values)
  • Encoding: GBK
  • Compression: uncompressed

Uses

  • Data Visualization: Generate time-series plots (e.g., “user step count over 30 days”), spatial maps (e.g., “player movement heatmaps”), or event distribution charts (e.g., “frequency of different event types”).
  • Predictive Modeling: Train machine learning models to forecast outcomes (e.g., “predict user activity level based on historical data”“classify event types using quantitative indicators”).
  • Pattern Recognition: Identify hidden trends (e.g., “peak activity hours of users”“common movement paths of players”“anomaly patterns in equipment logs”).
  • Educational Purposes: Serve as a teaching material for data cleaning, exploratory data analysis (EDA), or CSV format processing (suitable for beginners in data science).

Dataset Structure

app_source front_page source action_type screen_id operate_target input voice_dc vin format_time_ms json_all event_key original_timestamp
Setting Setting 触控 点击 中控屏 Bluetooth zxy 2025-08-01T08:16:01.117Z {"action":"Click","widget_type":"SettingSwitch","widget_id":"Bluetooth","value":"off"} Bluetooth 1754036161117
Voice Voice 语音 对话 中控屏 VoiceASR 开始播放 zxy 2025-08-01T08:16:31.934Z {"action":"Speech","widget_type":null,"widget_id":null,"value":"开始播放"} asr 1754036189348
Map Map 系统 响应 中控屏 SearchInput 国家体育馆 zxy 2025-08-01T08:19:37.740Z {"action":"Input","widget_type":"SearchInput","widget_id":"SearchInput","value":"国家体育馆"} SearchInput 1754036377740
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