HDBD - Honda Research Institute USA

HDBD HRI Driver Behavior Dataset

The HRI Driver Behavior Dataset (HDBD) contains driver behavior collected using simulator and real scene videos. The dataset contains videos of vehicle and driver (participant) behavioral and physiology data.

Introduction

Signals collected from 28 participants
- Behavioral – gaze position and takeover intention
- Physiology - heart rate (HR), galvanic skin response (GSR)
Environmental (weather, HMI, traffic conditions) and vehicle sensory information (speed, brake) corresponding to the videos
Sensors
- Tobii nano Pro (eye gaze related information)
- Shimmer3
- Tactile glove
- Vehicle/driving simulator sensory data from CAN-bus

Figures

Real-world driving snapshot

Driver point of view in adaptive driving style expeirment

Estimated semantics segmentation

Driving style preference survey

Trust survey

Download the dataset

The dataset is available for non-commercial usage. You must be affiliated with a university and use your university email address to make the request. Use this link to make the download request.

Citation

Please kindly cite our papers, if you find this work useful or interesting:

@inproceedings{qiu2022incorporating,

  title={Incorporating Gaze Behavior Using Joint Embedding With Scene Context for Driver Takeover Detection},

  author={Qiu, Yuning and Busso, Carlos and Misu, Teruhisa and Akash, Kumar},

  booktitle={ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},

  pages={4633--4637},

  year={2022},

  organization={IEEE}

}



@inproceedings{zheng2022identification,

  title={Identification of Adaptive Driving Style Preference through Implicit Inputs in SAE L2 Vehicles},

  author={Zheng, Zhaobo and Akash, Kumar and Misu, Teruhisa and Krishnamoorthy, Vidya and Dong, Miaomiao and Lee, Yuni and Huang, Gaojian},

  booktitle={Proceedings of the 2022 International Conference on Multimodal Interaction},

  pages={468--475},

  year={2022}

}



@inproceedings{zheng2022detection,

  title={Detection of Perceived Discomfort in SAE L2 Automated Vehicles through Driver Takeovers and Physiological Spikes},

  author={Zheng, Zhaobo K and Akash, Kumar and Misu, Teruhisa},

  booktitle={2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)},

  pages={1717--1722},

  year={2022},

  organization={IEEE}

}

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