Using an OpenDS Driving Simulator for Car Following

Green, Paul A.; Jeong, Heejin; Kang, Te-Ping

doi:10.1145/2667239.2667295

Cited by 8 publications

(6 citation statements)

References 8 publications

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“…The personalised prototype was built to simulate the automated driving procedures, monitor key categories of data streams, and adapt the personalised driving styles through a comprehensive hardware-software co-design. For the software stack, a modified version of OpenDS Driving Simulator [22] was used in this study. For the hardware stack, we integrated pressure sensors for the throttle and brake, and ported steering wheel to obtain spatial changes and eventspecific behaviours from drivers during the whole period.…”

Section: Development Of a Personalised Avmentioning

confidence: 99%

Exploring Personalised Autonomous Vehicles to Influence User Trust

Sun

Tang

et al. 2020

Cogn Comput

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Trust is a major determinant of acceptance of an autonomous vehicle (AV), and a lack of appropriate trust could prevent drivers and society in general from taking advantage of such technology. This paper makes a new attempt to explore the effects of personalised AVs as a novel approach to the cognitive underpinnings of drivers' trust in AVs. The personalised AV system is able to identify the driving behaviours of users and thus adapt the driving style of the AV accordingly. A prototype of a personalised AV was designed and evaluated in a lab-based experimental study of 36 human drivers, which investigated the impact of the personalised AV on user trust when compared with manual human driving and non-personalised AVs. The findings show that a personalised AV appears to be significantly more reliable through accepting and understanding each driver's behaviour, which could thereby increase a user's willingness to trust the system. Furthermore, a personalised AV brings a sense of familiarity by making the system more recognisable and easier for users to estimate the quality of the automated system. Personalisation parameters were also explored and discussed to support the design of AV systems to be more socially acceptable and trustworthy.

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Section: Development Of a Personalised Avmentioning

confidence: 99%

Exploring Personalised Autonomous Vehicles to Influence User Trust

Sun

Tang

et al. 2020

Cogn Comput

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“…In DE-MAND HARD the leading vehicle changed its speed 86 times between 20 and 110 km/h. We defined our driving tracks according to the recommendations of [9]. It was not possible for participants to develop a feeling of rhythm, as the time between speed changes varied strongly due to the current speed.…”

Section: Methodsmentioning

confidence: 99%

Contact-analog warnings on windshield displays promote monitoring the road scene

Haeuslschmid

Schnurr

Wagner³

et al. 2015

Proceedings of the 7th International Conference on Automotive User Interfaces and Interactive Vehicular Applications

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Drivers attend to a lot of information at various locations inside and outside the car as well as on external devices (e.g. smart phones). Head-Up Displays (HUDs) support keeping drivers' visual focus directed towards the street; as they present virtual information in the windshield area on top of the physical world within the field of view of the driver. Displayed information, however, is often spatially dissociated with its cause in the physical world: for example a warning is displayed, yet drivers still require time searching for the hazard causing it. Windshield displays (WSDs) allow virtual warnings being displayed at the position of the hazard. We compared HUD and WSD with the baseline no-display and found that drivers demonstrate a calm gaze behavior with WSDs; they keep their visual attention in average 1.5 s longer focused on the leading car. However, we also found no significant faster reaction time compared to HUDs. We discuss our findings comparing HUDs to WSDs, present potential limitations of our study and point out future steps in order to further investigate the advantages of WSDs.

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“…As for software, we use OpenDS [17,43], an open-source cognitive driving simulator, for driving scenario generations. OpenDS is widely-used for in-lab simulations for both manual and automated driving.…”

Section: Hardware and Software Supportsmentioning

confidence: 99%

BROOK Dataset: A Playground for Exploiting Data-Driven Techniques in Human-Vehicle Interactive Designs

Jin¹,

Duan²,

Liu³

et al. 2022

Preprint

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Emerging Autonomous Vehicles (AV) breed great potentials to exploit data-driven techniques for adaptive and personalized Human-Vehicle Interactions. However, the lack of high-quality and rich data supports limits the opportunities to explore the design space of data-driven techniques, and validate the effectiveness of concrete mechanisms. Our goal is to initialize the efforts to deliver the building block for exploring data-driven Human-Vehicle Interaction designs. To this end, we present BROOK dataset, a multi-modal dataset with facial video records. We first brief our rationales to build BROOK dataset. Then, we elaborate how to build the current version of BROOK dataset via a year-long study, and give an overview of the dataset. Next, we present three example studies using BROOK to justify the applicability of BROOK dataset. We also identify key learning lessons from building BROOK dataset, and discuss about how BROOK dataset can foster an extensive amount of follow-up studies. INTRODUCTIONRecent advances in data-driven techniques (e.g. Neural Networks) breed an extensive amount of opportunities, to enable adaptive and personalized Human-Vehicle Interaction (HVI). More interestingly, the emerging trends of Autonomous Vehicles relax the conventional burdens of driving, and in-vehicle drivers/passengers are capable to obtain better user experiences through more complex HVI. Incorporating data-driven approaches can greatly improve user experiences during driving processes. For instance, unobtrusive monitors of multi-modal statuses (i.e. by taking alternative data sources as input), rather than directly equipping biosensors, are more user-friendly as data sources for personalized interaction between drivers and vehicles. Therefore, combining data-driven techniques with HVI becomes promising in the near future, and relevant datasets become essential and highly demanded.Though there are already several datasets for HVI, there are four major limitations of existing datasets. First, the design purposes of existing datasets focus on a specific type of study purposes (e.g. Stress Detection [20], Driving Workload [40] and etc.), which naturally limits their potentials due to the narrowly-selected types of data streams. Second, existing datasets only collect information

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Using an OpenDS Driving Simulator for Car Following

Cited by 8 publications

References 8 publications

Exploring Personalised Autonomous Vehicles to Influence User Trust

Exploring Personalised Autonomous Vehicles to Influence User Trust

Contact-analog warnings on windshield displays promote monitoring the road scene

BROOK Dataset: A Playground for Exploiting Data-Driven Techniques in Human-Vehicle Interactive Designs

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