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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| Online-Zugang: | https://arxiv.org/abs/2404.16046 |
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| _version_ | 1866909180498018304 |
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| author | Wang, Xia Onwumelu, Sobenna Sprinkle, Jonathan |
| author_facet | Wang, Xia Onwumelu, Sobenna Sprinkle, Jonathan |
| contents | This work describes the use of on-board vehicle data from cars with advanced driver assistance features as a trip summary, with the goal of helping drivers contextualize their driving habits in terms of sustainability. The approach is similar to recent advancements in fitness tracking apps, which leverage smartwatches and other wearable devices to characterize activities during a workout or as part of daily fitness monitoring. Instead of adding new vehicle sensors, the data used for this work is from on-board driving data, namely, signals decoded from the vehicle's Controller Area Network (CAN) bus. With the deepening research of automatic driving technologies, Autonomous Vehicles (AVs) have gradually entered the consumer field, and more users are benefiting from the convenience and safety assistance provided by driving assistance and autonomous driving. However, various technical obstacles persist due to the complex environment, the non-communication of technologies, and users' trust. We propose indicators for evaluating the key characteristics of each drive, to facilitate drivers' familiarity with advanced driver assistance systems and to allow them to consider how different driving styles affect sustainability metrics. Further extensions will allow users to add feedback as part of the driving summary, laying a data foundation for future controller iterations based on real driving data and the attitude of drivers towards it. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_16046 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Using Automated Vehicle Data as a Fitness Tracker for Sustainability Wang, Xia Onwumelu, Sobenna Sprinkle, Jonathan Human-Computer Interaction This work describes the use of on-board vehicle data from cars with advanced driver assistance features as a trip summary, with the goal of helping drivers contextualize their driving habits in terms of sustainability. The approach is similar to recent advancements in fitness tracking apps, which leverage smartwatches and other wearable devices to characterize activities during a workout or as part of daily fitness monitoring. Instead of adding new vehicle sensors, the data used for this work is from on-board driving data, namely, signals decoded from the vehicle's Controller Area Network (CAN) bus. With the deepening research of automatic driving technologies, Autonomous Vehicles (AVs) have gradually entered the consumer field, and more users are benefiting from the convenience and safety assistance provided by driving assistance and autonomous driving. However, various technical obstacles persist due to the complex environment, the non-communication of technologies, and users' trust. We propose indicators for evaluating the key characteristics of each drive, to facilitate drivers' familiarity with advanced driver assistance systems and to allow them to consider how different driving styles affect sustainability metrics. Further extensions will allow users to add feedback as part of the driving summary, laying a data foundation for future controller iterations based on real driving data and the attitude of drivers towards it. |
| title | Using Automated Vehicle Data as a Fitness Tracker for Sustainability |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2404.16046 |