ORIAS: On-The-Fly Object Identification and Action Selection for Highly Automated Vehicles

Colley, Mark; Askari, Ali; Walch, Marcel; Woide, Marcel; Rukzio, Enrico

doi:10.1145/3409118.3475134

Cited by 9 publications

(6 citation statements)

References 45 publications

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“…ACM Digital Library: [10,20,21,26,[34][35][36][37][38][39][40][41][42] Scopus: [22,[43][44][45][46][47][48][49][50][51][52][53][54][55][56][57][58][59][60] IEEE Xplore: [61][62][63][64][65][66][67][68][69] Google Scholar: [70][71][72][73][74][75][76][77][78]…”

Section: Screeningmentioning

confidence: 99%

“…In our literature survey, we did not come across research work on this topic, despite teleoperation in automated vehicles becoming a more and more relevant research area. Discrete and Deterministic [26,[34][35][36][47][48][49][50]54,59,63,65,71,75,76] Continuous and Negotiated [10,[20][21][22]37,[39][40][41]43,45,[51][52][53]55,56,58,[60][61][62]64,[66][67][68]70,77,78] Discrete and Negotiated [38,44,46,69] Agents Task Residuals Task Level Strategical -Tactical [20][21][22]26,…”

Section: Categorization Of Related Literaturementioning

confidence: 99%

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How to Design Human-Vehicle Cooperation for Automated Driving: A Review of Use Cases, Concepts, and Interfaces

Peintner,

Escher,

Detjen

et al. 2024

MTI

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Currently, a significant gap exists between academic and industrial research in automated driving development. Despite this, there is common sense that cooperative control approaches in automated vehicles will surpass the previously favored takeover paradigm in most driving situations due to enhanced driving performance and user experience. Yet, the application of these concepts in real driving situations remains unclear, and a holistic approach to driving cooperation is missing. Existing research has primarily focused on testing specific interaction scenarios and implementations. To address this gap and offer a contemporary perspective on designing human–vehicle cooperation in automated driving, we have developed a three-part taxonomy with the help of an extensive literature review. The taxonomy broadens the notion of driving cooperation towards a holistic and application-oriented view by encompassing (1) the “Cooperation Use Case”, (2) the “Cooperation Frame”, and (3) the “Human–Machine Interface”. We validate the taxonomy by categorizing related literature and providing a detailed analysis of an exemplar paper. The proposed taxonomy offers designers and researchers a concise overview of the current state of driver cooperation and insights for future work. Further, the taxonomy can guide automotive HMI designers in ideation, communication, comparison, and reflection of cooperative driving interfaces.

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Section: Screeningmentioning

confidence: 99%

Section: Categorization Of Related Literaturementioning

confidence: 99%

How to Design Human-Vehicle Cooperation for Automated Driving: A Review of Use Cases, Concepts, and Interfaces

Peintner,

Escher,

Detjen

et al. 2024

MTI

View full text Add to dashboard Cite

show abstract

“…Also, quite some studies focus on the execution-level, where the system shares control with the human driver normally via specific haptic devices [1,8,35]. Recently, some studies tried to extend the cooperation to the perception-level [7,22]. One typical example is the ORIAS concept, which allows humans to label unrecognized objects on-the-fly by speech input [7].…”

Section: Human-vehicle Cooperation In Perception- Plan-and Execution-...mentioning

confidence: 99%

“…Recently, some studies tried to extend the cooperation to the perception-level [7,22]. One typical example is the ORIAS concept, which allows humans to label unrecognized objects on-the-fly by speech input [7]. Results from a driving simulator study showed highly usability and input correctness.…”

Section: Human-vehicle Cooperation In Perception- Plan-and Execution-...mentioning

confidence: 99%

“…Besides, this concept is based on the assumption that the system is "capable of realizing that it is not able to classify an object" [7]. In many situations, object detection algorithms fail to accurately classify the low confidence of the recognition (e.g., as demonstrated by adversarial attacks [28] ), making it difficult to inform the human driver to label an ADS correctly.…”

Section: Human-vehicle Cooperation In Perception- Plan-and Execution-...mentioning

confidence: 99%

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Hybrid Eyes: Design and Evaluation of the Prediction-level Cooperative Driving with a Real-world Automated Driving System

Wang,

Chu,

Martens

et al. 2022

Preprint

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Fig. 1. Prototype setup. a) GUI output. b) eyetracker. c) sensor for detecting the double-tapping confirmation Currently, there are still various situations in which automated driving systems (ADS) cannot perform as well as a human driver, particularly in predicting the behaviour of surrounding traffic. As humans are still surpassing state-of-the-art ADS in this task, a new concept enabling human driver to help ADS to better anticipate the behaviour of other road users was developed. Preliminary results suggested that the collaboration at the prediction level can effectively enhance the experience and comfort of ADS. For an in-depth investigation of the concept, we implemented an interactive prototype, called Prediction-level Cooperative Automated Driving system (PreCoAD), adapting an existing ADS that has been previously validated on the public road. The results of a driving simulator study among 15 participants in different highway scenarios showed that PreCoAD could enhance automated driving performance and provide a positive user experience. Follow-up interviews with participants also provided insights into the improvement of the system. CCS Concepts: • Human-centered computing → HCI theory, concepts and models.

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Effective remote automated vehicle operation: a mixed reality contextual comparison study

Gafert,

Mirnig,

Fröhlich

et al. 2023

Pers Ubiquit Comput

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ORIAS: On-The-Fly Object Identification and Action Selection for Highly Automated Vehicles

Cited by 9 publications

References 45 publications

How to Design Human-Vehicle Cooperation for Automated Driving: A Review of Use Cases, Concepts, and Interfaces

How to Design Human-Vehicle Cooperation for Automated Driving: A Review of Use Cases, Concepts, and Interfaces

Hybrid Eyes: Design and Evaluation of the Prediction-level Cooperative Driving with a Real-world Automated Driving System

Effective remote automated vehicle operation: a mixed reality contextual comparison study

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