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As of early 2023, only a limited number of Society of Automotive Engineers (SAE) Level 3 (L3) automated driving systems are available on the market, and they are primarily offered by luxury vehicle brands. SAE L3 automated driving systems are classified as conditional automation (CA), meaning that the vehicle can undertake some well-defined driving tasks under specific conditions, but the driver must be ready to assume control of the vehicle when prompted by the system. It is anticipated that an increasing number of L3 CA systems will be introduced on public roads in the next few years. However, L3 systems pose unique Human Factors (HF) challenges that require thoughtful consideration to ensure that production systems are feasible without compromising driver or road safety. This panel discussion brings together HF researchers and practitioners with expertise in human behavior and usability design for automotive applications to discuss and delineate key issues specifically related to L3 systems, as well as potential approaches to tackle these issues.
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MAConAuto: Framework for Mobile-Assisted Human-in-the-Loop Automotive System
Automotive is becoming more and more sensor-equipped. Collision avoidance, lane departure warning, and self-parking are examples of applications becoming possible with the adoption of more sensors in the automotive industry. Moreover, the driver is now equipped with sensory systems like wearables and mobile phones. This rich sensory environment and the real-time streaming of contextual data from the vehicle make the human factor integral in the loop of computation. By integrating the human’s behavior and reaction into the advanced driver-assistance systems (ADAS), the vehicles become a more context-aware entity. Hence, we propose MAConAuto, a framework that helps design human-in-the-loop automotive systems by providing a common platform to engage the rich sensory systems in wearables and mobile to have context-aware applications. By personalizing the context adaptation in automotive applications, MAConAuto learns the behavior and reactions of the human to adapt to the personalized preference where interventions are continuously tuned using Reinforcement Learning. Our general framework satisfies three main design properties, adaptability, generalizability, and conflict resolution. We show how MAConAuto can be used as a framework to design two applications as human-centric applications, forward collision warning, and vehicle HVAC system with negligible time overhead to the average human response time.
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- Award ID(s):
- 2105084
- PAR ID:
- 10417998
- Publisher / Repository:
- IEEE
- Date Published:
- Journal Name:
- 2022 IEEE Intelligent Vehicles Symposium (IV)
- ISBN:
- 978-1-6654-8821-1
- Page Range / eLocation ID:
- 740 to 749
- Format(s):
- Medium: X
- Location:
- Aachen, Germany
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/IV51971.2022.9827415
