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. 2022 Jun 20;22(12):4654.
doi: 10.3390/s22124654.

Conceptual Modeling of Extended Collision Warning System from the Perspective of Smart Product-Service System

Chunlong Wu  1   2   3 , Hanyu Lv  1   2 , Tianming Zhu  1   2 , Yunhe Liu  1   2 , Marcus Vinicius Pereira Pessôa  4
Affiliations

Conceptual Modeling of Extended Collision Warning System from the Perspective of Smart Product-Service System

Chunlong Wu et al. Sensors (Basel). .

Abstract

While Product-Service Systems (PSS) have a potential sustainability impact by increasing a product's life and reducing resource consumption, the lack of ownership might lead to less responsible user behavior. Smart PSS can overcome this obstacle and guarantee correct and safe PSS use. In this context, intelligent connected vehicles (ICVs) with PSS can effectively reduce traffic accidents and ensure the safety of vehicles and pedestrians by guaranteeing optimal and safe vehicle operation. A core subsystem to support that is the collision-warning system (CWS). Existing CWSs are, however, limited to in-car warning; users have less access to the warning information, so the result of CWS for collision avoidance is insufficient. Therefore, CWS needs to be extended to include more elements and stakeholders in the collision scenario. This paper aims to provide a novel understanding of extended CWS (ECWS), outline the conceptual framework of ECWS, and contribute a conceptual modeling approach of ECWS from the smart PSS perspective at the functional level. It defines an integrated solution of intelligent products and warning services. The function is modeled based on the Theory of Inventive Problem Solving (TRIZ). Functions of an ECWS from the perspective of smart PSS can be comprehensively expressed to form an overall solution of integrated intelligent products, electronic services, and stakeholders. Based on the case illustration, the proposed method can effectively help function modeling and development of the ECWS at a conceptual level. This can effectively avoid delays due to traffic accidents and ensure the safety of vehicles and pedestrians.

Keywords: TRIZ function model; conceptual modeling; extended collision-warning system; smart product-service system.

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Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Figure 20
Figure 20
Visualization interface based on CANape17.0.
Figure 1
Figure 1
Stakeholders–Intelligent Product–Intelligent Service system.
Figure 2
Figure 2
Conceptual framework of extended CWS.
Figure 3
Figure 3
Elements of the TRIZ function model—fill contents in the chart.
Figure 4
Figure 4
Proposed modeling process of the extended CWS.
Figure 5
Figure 5
Functional decomposition process.
Figure 6
Figure 6
Cloud platform structure.
Figure 7
Figure 7
Overall architecture of extended CWS from the perspective of smart PSS.
Figure 8
Figure 8
(a) Dongfeng S50 EV. (b) Sightseeing vehicle.
Figure 9
Figure 9
Experimental Area.
Figure 10
Figure 10
Extended CWS from the perspective of smart PSS.
Figure 11
Figure 11
Functions of extended CWS from the perspective of smart PSS.
Figure 12
Figure 12
Function model of the intelligent product system.
Figure 13
Figure 13
Function model of stakeholders.
Figure 14
Figure 14
Function model of vehicle collision-warning service system.
Figure 15
Figure 15
Function model of vehicle–pedestrian collision-warning service system.
Figure 16
Figure 16
The cloud platform of the extended CWS.
Figure 17
Figure 17
The function model of the extended CWS.
Figure 18
Figure 18
Parts of the indoor bench installation of the system.
Figure 19
Figure 19
Monitoring platform.
Figure 21
Figure 21
Relative_Dist_Forward of two vehicles.
Figure 22
Figure 22
TTC_Forward of two vehicles.
Figure 23
Figure 23
Traffic lights recognition based on V2X.
Figure 24
Figure 24
Road information publishing based on V2X.
Figure 25
Figure 25
Pedestrian recognition and avoidance based on V2X.
Figure 26
Figure 26
Intersection collision warning based on V2X.
Figure 27
Figure 27
Forward collision warning based on V2X.
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