Teaming with Your Car: Redefining the Driver–Automation Relationship in Highly Automated Vehicles

Lee, Joonbum; Rheem, Hansol; Lee, John D.; Szczerba, Joseph; Tsimhoni, Omer

doi:10.1177/15553434221132636

Cited by 10 publications

(6 citation statements)

References 87 publications

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“…(1) Cooperation interface Walch et al [10] suggest that interfaces for human-machine collaboration should fulfill the requirements of mutual predictability, directability, shared situational awareness, and calibrated trust. They emphasize that predictability plays a crucial role in this context.…”

Section: Conceptmentioning

confidence: 99%

Conceptual Design of Transparent Machine Behavior to Facilitate Human-Machine Driving

Fang,

Yang

2024

ssr

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To facilitate human-machine driving, the functions of the intelligent system need to be displayed clearly to the driver. In this way, the driver will be able to understand the goals, plans, and decisions of the intelligent system. In addition to internal states, the transparency of external states such as the level of control during automated driving is also important. However, the current autonomous driving systems do not allow visibility of the tangible state of the intelligent system. Therefore, we designed a Human Machine Interface (HMI) concept that makes the machine’s behavior visible through virtual changes on the steering wheel, foot pedals, and turn signals. In this way, the lack of awareness of the driver due to the loss of tactile feedback from the vehicle can be compensated, and the driver will be able to observe and predict the behavior of the system, resulting in a better human-machine driving experience.

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Section: Conceptmentioning

confidence: 99%

Conceptual Design of Transparent Machine Behavior to Facilitate Human-Machine Driving

Fang,

Yang

2024

ssr

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“…With this in mind, we propose to promote cooperation across human-driven vehicles to aid AVs in their merging. Cooperation has not been uniquely defined in the literature [24]. In the context of this work, we define cooperation as the behavior exhibited by human-driven vehicles towards other vehicles including conventional and autonomous vehicles, by slowing down near a merging area to facilitate the merging task.…”

Section: Cooperative Driving and Observability Models In A Highway Mergementioning

confidence: 99%

Leveraging Autonomous Vehicles to Tally Cooperative Driving Behavior

et al. 2023

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The number of Autonomous Vehicles (AVs) coexisting with conventional human-driven vehicles is expected to increase significantly in the coming years. This coexistence will last decades before full AV adoption is achieved worldwide. However, the cautious nature of AVs and the aggressive behavior of some human drivers could create unprecedentedly challenging scenarios for AVs, such as being stuck on merge lanes and blocked by human-driven vehicles. On the other hand, the cooperative behavior of other human drivers could assist AVs in avoiding deadlock situations. In this paper, we propose to leverage AVs to tally the cooperative driving behavior of human-driven vehicles. To this end, we model cooperative driving behavior in a ''highway merge'' scenario, which tends to be challenging for AVs. We vary the percentage of cooperative human-driven vehicles and estimate the percentage of AVs required to tally cooperative acts. Results show that when fifty percent of the human drivers cooperate, cooperation leads to statistically significant reductions of up to 68%, 46%, 38%, and 5% in stop delay, number of stops, vehicle delay, and travel time, respectively. Finally, we demonstrate that a 30% penetration of AVs is sufficient to tally up to 78% of cooperative behavior in highway scenarios. To promote cooperation across the population, our future work revolves around the construction of vehicular profiles based on their cooperative behavior. These profiles will be regularly updated and disseminated among AVs to aid their cooperative decisions toward human-driven vehicles in the upcoming interactions.INDEX TERMS Human-driven vehicles (HVs), cooperative human-driven vehicles (CHVs), autonomous vehicles (AVs), highway merge, modeling.

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“…Rather than providing value to the driver, they are used as an easier alternative to safe algorithms and some DMS are made as simple as possible to avoid the cost of creating more effective ones. However, there will always be changing edge cases in autonomous driving systems and human-vehicle teaming needs to be better at adapting to these changes (Lee et al, 2023). Adding an attention monitor is hardly sufficient for ensuring that the driver accurately understands the system and where it is most likely to need help.…”

Section: To the Person With A Hammer Everything Looks Like A Nailmentioning

confidence: 99%

Human involvement in autonomous decision-making systems. Lessons learned from three case studies in aviation, social care and road vehicles

Salvini,

Reinmund,

Hardin

et al. 2023

Front. Polit. Sci.

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This paper draws on three case studies to examine some of the challenges and tensions involved in the use of Autonomous Decision-Making Systems (ADMS). In particular, the paper highlights: (i) challenges around the shifting “locale” of the decision, and the associated consequences for stakeholders; (ii) potential implications for stakeholders from regulation such as the General Data Protection Regulation (GDPR); (iii) the different values that stakeholder groups bring to the “decision” question; (iv) how complex pre-existing webs of stakeholders and decision-making authorities may be disrupted or disempowered by the use of an automated system and the lack of evaluation of possible consequences; (v) how ADMS for non-technical users can lead to circumvention of the boundaries of intended system use. We illustrate these challenges through case studies in three domains: adult social care, aviation, and vehicle driver monitoring systems. The paper closes with recommendations for both practice and policy in the deployment of ADMS.

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Teaming with Your Car: Redefining the Driver–Automation Relationship in Highly Automated Vehicles

Cited by 10 publications

References 87 publications

Conceptual Design of Transparent Machine Behavior to Facilitate Human-Machine Driving

Conceptual Design of Transparent Machine Behavior to Facilitate Human-Machine Driving

Leveraging Autonomous Vehicles to Tally Cooperative Driving Behavior

Human involvement in autonomous decision-making systems. Lessons learned from three case studies in aviation, social care and road vehicles

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