|
--- |
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dataset_info: |
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features: |
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- name: index |
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dtype: int64 |
|
- name: v_kmph |
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dtype: float64 |
|
- name: ax_mpss |
|
dtype: float64 |
|
- name: ay_mpss |
|
dtype: float64 |
|
- name: yaw_rate_radps |
|
dtype: float64 |
|
- name: frame |
|
dtype: image |
|
- name: d_lanecenter_m |
|
dtype: float64 |
|
- name: alias |
|
dtype: string |
|
- name: steering_rack_pos_m |
|
dtype: float64 |
|
- name: steering_torque_N |
|
dtype: float64 |
|
- name: lane_curvature_radpm |
|
dtype: float64 |
|
- name: stationary |
|
dtype: float64 |
|
- name: segment |
|
dtype: int64 |
|
- name: split |
|
dtype: string |
|
- name: road_type |
|
dtype: string |
|
- name: driving_situation_rural |
|
dtype: string |
|
- name: driving_situation_federal |
|
dtype: string |
|
- name: driving_situation_highway |
|
dtype: string |
|
- name: rep_id |
|
dtype: int64 |
|
- name: frame_nr |
|
dtype: int64 |
|
splits: |
|
- name: val_val |
|
num_bytes: 9160076169.901 |
|
num_examples: 34767 |
|
- name: val_train |
|
num_bytes: 41105223625.104 |
|
num_examples: 138572 |
|
- name: pretrain |
|
num_bytes: 73729563090.513 |
|
num_examples: 304287 |
|
- name: pretrain_train |
|
num_bytes: 59523614752.871 |
|
num_examples: 242887 |
|
- name: pretrain_val |
|
num_bytes: 14759288492.4 |
|
num_examples: 61400 |
|
download_size: 193239069632 |
|
dataset_size: 198277766130.789 |
|
configs: |
|
- config_name: default |
|
data_files: |
|
- split: val_val |
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path: data/val_val-* |
|
- split: val_train |
|
path: data/val_train-* |
|
- split: pretrain |
|
path: data/pretrain-* |
|
- split: pretrain_train |
|
path: data/pretrain_train-* |
|
- split: pretrain_val |
|
path: data/pretrain_val-* |
|
license: cc-by-4.0 |
|
pretty_name: SADC |
|
size_categories: |
|
- 1M<n<10M |
|
--- |
|
|
|
# Dataset Card for Dataset SADC |
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|
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There is evidence that the driving style of an |
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autonomous vehicle is important to increase the acceptance |
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and trust of the passengers. The driving situation has been |
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found to have a significant influence on human driving behavior. |
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However, current driving style models only partially incorporate |
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driving environment information, limiting the alignment between |
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an agent and the given situation. |
|
|
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Therefore, we propose a dataset for situation-aware driving style modeling. |
|
|
|
[](https://arxiv.org/abs/2403.19595) |
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[](https://github.com/jHaselberger/SADC-Situation-Awareness-for-Driver-Centric-Driving-Style-Adaptation) |
|
|
|
## Dataset Details |
|
|
|
### Dataset Description |
|
|
|
The dataset is composed as follows: the pretrain |
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set DP is split into a training subset DP,T with 242 887 |
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samples, and a validation subset DP,V with 61 400 samples. |
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Similarly, the validation set DV is split into a training subset |
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DV,T and a validation subset DV,V with 138 572 and 34 767 |
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samples. Each subset consists of 1280 × 960 images, driving |
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behavior indicators like the distance to the lane center, vehicle signals like velocity |
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or accelerations, as well as traffic conditions and road type labels. |
|
|
|
- **Curated by:** Johann Haselberger |
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- **License:** CC-BY-4.0 |
|
|
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### Dataset Sources |
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We collected over 16 hours of driving data from single test driver as pretrain data. |
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For the driving style adaptation, we collected driving behavior data from five different subjects driving on the same route for one hour, denoted as validation data. |
|
|
|
|
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## Usage |
|
|
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### Download Script |
|
For an easy usage of our dataset, we provide a download script with our repo: [https://github.com/jHaselberger/SADC-Situation-Awareness-for-Driver-Centric-Driving-Style-Adaptation/blob/master/utils/download_dataset.py](https://github.com/jHaselberger/SADC-Situation-Awareness-for-Driver-Centric-Driving-Style-Adaptation/blob/master/utils/download_dataset.py). |
|
|
|
```sh |
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python download_dataset.py --target_dir ../data --split pretrain_train |
|
``` |
|
|
|
### List Available Split Names |
|
```python |
|
from datasets import load_dataset, get_dataset_split_names |
|
|
|
split_names = get_dataset_split_names("jHaselberger/SADC-Situation-Awareness-for-Driver-Centric-Driving-Style-Adaptation") |
|
print(f"Available split names: {split_names}") |
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``` |
|
|
|
### Inspect some Samples |
|
```python |
|
from datasets import load_dataset, get_dataset_split_names |
|
from matplotlib import pyplot as plt |
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import pandas as pd |
|
|
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dataset = load_dataset("jHaselberger/SADC-Situation-Awareness-for-Driver-Centric-Driving-Style-Adaptation", split="val_val", streaming=True) |
|
|
|
samples = dataset.take(50) |
|
df = pd.DataFrame.from_dict([s for s in samples]) |
|
print(df.head()) |
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``` |
|
|
|
#### Visualize some Time-Series |
|
```python |
|
fig, ax1 = plt.subplots() |
|
ax2 = ax1.twinx() |
|
ax1.plot(df["frame_nr"],df["v_kmph"],"ko-",label="velocity") |
|
ax2.plot(df["frame_nr"],df["steering_torque_N"],"ro-",label="steering torque") |
|
|
|
ax1.set_xlabel('Frame') |
|
ax1.set_ylabel('Velocity in km/h', color='k') |
|
ax2.set_ylabel('Steering Torque in N', color='r') |
|
plt.show() |
|
``` |
|
|
|
#### Visualize the Camera Image |
|
```python |
|
plt.imshow(df["frame"].iloc[-1]) |
|
plt.axis('off') |
|
plt.show() |
|
``` |
|
|
|
## Dataset Structure |
|
|
|
### Dataset Splits |
|
|
|
| **Split** | **Number of Samples** | **Description** | |
|
|---------------------|-------------------|---------------------------------------------------------------------------------------------------------| |
|
| | | | |
|
| **Used for the Experiments in the Paper** | | | |
|
| pretrain | 304287 | The full pretrain dataset. | |
|
| pretrain_train | 242887 | Subset of `pretrain` used for training. | |
|
| pretrain_val | 61400 | Subset of `pretrain` used for validation. | |
|
| val_train | 138572 | Subset of `validation` used for training. | |
|
| val_val | 34767 | Subset of `validation` used for validation. | |
|
| | | | |
|
| **Additional Data** | | | |
|
| pretrain_unfiltered | 1180252 | The full unfiltered pretrain dataset. | |
|
| val_unfiltered | 686328 | The full unfiltered validation dataset. | |
|
|
|
### Files |
|
- The folder `driving_data` contains the vehicle signals. Downloading these files is optional and is only required if you do not want to download the entire image data set. |
|
- The folder `image_lists` contains the image lists used for training of the featrue encoders and NN-based behavior predictors. Downloading these files is optional. |
|
|
|
#### Personal and Sensitive Information |
|
|
|
To blur vehicle license plates and human faces in the camera frames, we utilize EgoBlur [https://github.com/facebookresearch/EgoBlur](https://github.com/facebookresearch/EgoBlur). |
|
Furthermore, all subject-related data, including the socio-demographics, are anonymized. |
|
|
|
|
|
## Bias, Risks, and Limitations |
|
|
|
Considering the limitations of our dataset, real-world tests should be conducted with care in a safe environment. |
|
To publish the data concerning privacy policies, we utilized a state-of-the-art anonymization framework to blur human faces and vehicle license plates to mitigate |
|
privacy concerns. |
|
|
|
|
|
## Citation [optional] |
|
|
|
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> |
|
|
|
**BibTeX:** |
|
``` |
|
@misc{haselberger2024situation, |
|
title={Situation Awareness for Driver-Centric Driving Style Adaptation}, |
|
author={Johann Haselberger and Bonifaz Stuhr and Bernhard Schick and Steffen Müller}, |
|
year={2024}, |
|
eprint={2403.19595}, |
|
archivePrefix={arXiv}, |
|
primaryClass={cs.CV} |
|
} |
|
``` |
|
|
|
**APA:** |
|
``` |
|
Johann Haselberger, Bonifaz Stuhr, Bernhard Schick, & Steffen Müller. (2024). Situation Awareness for Driver-Centric Driving Style Adaptation. |
|
``` |
