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Xiangjun Peng

Bio: Xiangjun Peng is an academic researcher from The University of Nottingham Ningbo China. The author has contributed to research in topics: Collaborative filtering & Serendipity. The author has an hindex of 2, co-authored 7 publications receiving 21 citations.

Papers
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Journal ArticleDOI
TL;DR: 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 users’ willingness to trust the system.
Abstract: 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.

40 citations

Book ChapterDOI
24 Jul 2021
TL;DR: In this article, the authors conduct an in-depth study to demystify complicated interactions between driving behaviors and styles, by applying self-clustering algorithms over a state-of-the-art open-sourced dataset of human-vehicle interactions.
Abstract: We argue that driving styles demand adaptive classifications, and such mechanisms are essential for adaptive and personalized Human-Vehicle Interaction systems. To this end, we conduct an in-depth study to demystify complicated interactions between driving behaviors and styles. The key idea behind this study is to enable different numbers of clusters on the fly, when classifying driving behaviors. We achieve so by applying Self-Clustering algorithms (i.e. DBSCAN) over a state-of-the-art open-sourced dataset of Human-Vehicle Interactions. Our results derive 8 key findings, which showcases the complicated interactions between driving behaviors and driving styles. Hence, we conjecture that future Human-Vehicle Interactions systems demand similar approaches for the characterizations of drivers, to enable more adaptive and personalized Human-Vehicle Interaction systems. We believe our findings can stimulate and benefit more future research as well.

12 citations

Proceedings ArticleDOI
21 Sep 2019
TL;DR: This paper introduces the first step in utilizing video-to-video synthesis, which is a deep learning approach, in OpenDS framework,which is an open-source driving simulator software, to present simulated scenes as realistically as possible.
Abstract: Existing programmable simulators enable researchers to customize different driving scenarios to conduct in-lab automotive driver simulations. However, software-based simulators for cognitive research generate and maintain their scenes with the support of 3D engines, which may affect users' experiences to a certain degree since they are not sufficiently realistic. Now, a critical issue is the question of how to build scenes into real-world ones. In this paper, we introduce the first step in utilizing video-to-video synthesis, which is a deep learning approach, in OpenDS framework, which is an open-source driving simulator software, to present simulated scenes as realistically as possible. Off-line evaluations demonstrated promising results from our study, and our future work will focus on how to merge them appropriately to build a close-to-reality, real-time driving simulator.

11 citations

Journal Article
TL;DR: This work-in-progress BROOK is presented, a public multi-modal database with facial video records, which could be used to characterise drivers' affective states and driving styles, and a Neural Network-based predictor is showcased, leveraging BROOK.
Abstract: With the growing popularity of Autonomous Vehicles, more opportunities have bloomed in the context of Human-Vehicle Interactions. However, the lack of comprehensive and concrete database support for such specific use case limits relevant studies in the whole design spaces. In this paper, we present our work-in-progress BROOK, a public multi-modal database with facial video records, which could be used to characterise drivers' affective states and driving styles. We first explain how we over-engineer such database in details, and what we have gained through a ten-month study. Then we showcase a Neural Network-based predictor, leveraging BROOK, which supports multi-modal prediction (including physiological data of heart rate and skin conductance and driving status data of speed) through facial videos. Finally we discuss related issues when building such a database and our future directions in the context of BROOK. We believe BROOK is an essential building block for future Human-Vehicle Interaction Research. More details and updates about the project BROOK is online at https: //unnc-idl-ucc.github.io/BROOK/.

7 citations

Book ChapterDOI
19 Jul 2020
TL;DR: This paper introduces CHESTNUT, a memory-based movie collaborative filtering system to improve serendipity performance and demonstrates a method of successfully injecting insight, unexpectedness and usefulness, which are key metrics for a more comprehensive understanding of serendipsity, into a practical serendIPitous recommender system.
Abstract: The term “serendipity” has been understood narrowly in the Recommender System Applying a user-centered approach, user-friendly serendipitous recommender systems are expected to be developed based on a good understanding of serendipity In this paper, we introduce CHESTNUT, a memory-based movie collaborative filtering system to improve serendipity performance Relying on a proposed Information Theory-based algorithm and previous study, we demonstrate a method of successfully injecting insight, unexpectedness and usefulness, which are key metrics for a more comprehensive understanding of serendipity, into a practical serendipitous recommender system With lightweight experiments, we have revealed a few runtime issues and further optimized the same We have evaluated CHESTNUT in both practicability and effectiveness, and the results show that it is fast, scalable and improves serendipity performance significantly, compared with mainstream memory-based collaborative filtering The source codes of CHESTNUT are online at https://githubcom/unnc-ucc/CHESTNUT

3 citations


Cited by
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Journal ArticleDOI
26 Feb 2021
TL;DR: In this paper, the influence of safety, ethics, liability, regulations, and the recent pandemic on the public acceptance of AVs is discussed. But little is known about public acceptance and perception of the AVs technology or the factors that influence public acceptance.
Abstract: Autonomous vehicles (AVs) or self-driving cars have the potential to provide many benefits such as improving mobility and reducing the energy and emissions consumed, travel time, and vehicle ownership. Thus, in the last few years, both research and industry have put significant efforts to develop AVs. However, laws and regulations are not ready yet for this switch and the legal sector is unable to take the lead but follow the development of AVs. Besides, the social acceptance is considered as a main key factor for the success of any new technology. Despite the enthusiastic speculation of AVs, little is known about the public acceptance and perception of the AVs technology or the factors that influence the public acceptance. This paper reviews the previous studies that focuses on testing the public acceptance and perception of AVs and sketches out the main trends in this area to provide some directions and recommendations for the future. This paper focuses on the influence of safety, ethics, liability, regulations, and the recent pandemic on the public acceptance of AVs.

73 citations

Journal ArticleDOI
TL;DR: In this article , the authors proposed a Turing approach to test the "humanity" of AVs in mixed traffic with partially-autonomous vehicles, and the results showed that participants were unable to distinguish the Artificial Intelligence (AI) from the human driver by observing random responses with a 95% significance level.
Abstract: • If AVs drove like humans, they would reduce interaction problems with drivers and passengers. • The ability of AVs not to be distinguished from a human driver was tested through a Turing approach. • A real on the road experiment with 550 university students was performed in Italy. • In most cases the Artificial Intelligence (AI) was indistinguishable from the human driver. • Artificial Intelligence of the cruise control is less recognizable than that of the lane keeping. Fully automated vehicles (AVs) are set to become a reality in future decades and changes are to be expected in user perceptions and behavior. While AV acceptability has been widely studied, changes in human drivers’ behavior and in passengers’ reactions have received less attention. It is not yet possible to ascertain the risk of driver behavioral changes such as overreaction, and the corresponding safety problems, in mixed traffic with partially AVs. Nor has there been proper investigation of the potential unease of car occupants trained for human control, when exposed to automatic maneuvers. The conjecture proposed in this paper is that automation Level 2 vehicles do not induce potentially adverse effects in traditional vehicle drivers’ behavior or in occupants’ reactions, provided that they are indistinguishable from human-driven vehicles. To this end, the paper proposes a Turing approach to test the “humanity” of automation Level 2 vehicles. The proposed test was applied to the results of an experimental campaign carried out in Italy: 546 car passengers were interviewed on board Level 2 cars in which they could not see the driver. They were asked whether a specific driving action (braking, accelerating, lane keeping) had been performed by the human driver or by the automatic on-board software under different traffic conditions (congestion and speed). Estimation results show that in most cases the interviewees were unable to distinguish the Artificial Intelligence (AI) from the human driver by observing random responses with a 95% significance level (proportion of success statistically equal to 50%). However, in the case of moderate braking and lane keeping at >100 km/h and in high traffic congestion, respondents recognized AI control from the human driver above pure chance, with 62–69% correct response rates. These findings, if confirmed in other case studies, could significantly impact on AVs acceptability, also contributing to their design as well as to long-debated ethical questions. AI driving software could be designed and tested for “humanity”, as long as safety is guaranteed, and autonomous cars could be allowed to circulate as long as they cannot be distinguished from human-driven vehicles in recurrent driving conditions.

25 citations

Journal ArticleDOI
TL;DR: In this article, the authors investigated the effects of the designed driving style of AV (aggressive/defensive) and driver's driving style on driver's trust, acceptance, and take-over behavior in a fully AV.

19 citations

Journal ArticleDOI
TL;DR: In this article , the authors proposed adopting linguistic politeness in vehicle speech interfaces, as active communication between drivers and vehicles will become an essential part of driving with advanced autonomous driving technologies, and two between-subjects experiments were conducted to test the influences of politeness strategies on drivers' perceptions of the vehicles.

16 citations

Journal ArticleDOI
TL;DR: In this paper, the authors proposed to adopt linguistic politeness in vehicle speech interfaces, as active communication between drivers and vehicles will become an essential part of driving with advanced autonomous driving technologies, and two between-subjects experiments were conducted to test the influences of politeness strategies (i.e., asking help, giving a reason, expressing gratitude) on drivers' perceptions of vehicles.

16 citations