Assuring Fully Autonomous Vehicles Safety by Design: The Autonomous Vehicle Control (AVC) Module Strategy

Molina, Caroline; Almeida, Jorge Rady de; Vismari, Lúcio F.; Gonzalez, Rodrigo Ignacio R.; Naufal, Jamil Kalil; Camargo, João Batista

doi:10.1109/dsn-w.2017.14

Cited by 35 publications

(12 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although system limitations are part of the design of a system and refer to its scope of application, system malfunctions include mechanical failures, errors in data acquisition, hazardous weather conditions, and hardware failures (Emzivat, Ibanez-Gutman, Martinet, & Roux, 2017; Molina et al, 2017). Thus, while system limitations are preceded by a TOR of the system and a controlled transition of control, system malfunctions occur suddenly and prevent further safe functioning of the automation.…”

Section: Malfunctionsmentioning

confidence: 99%

“…Thus, while system limitations are preceded by a TOR of the system and a controlled transition of control, system malfunctions occur suddenly and prevent further safe functioning of the automation. To protect the vehicle from collision, excessive speed, or other hazardous conditions, such as a malfunction, is resolved through a fallback strategy, for example, leading to a safe halt in SAE level 4 (Emzivat et al, 2017;Molina et al, 2017).…”

Section: Malfunctionsmentioning

confidence: 99%

See 1 more Smart Citation

The More You Know: Trust Dynamics and Calibration in Highly Automated Driving and the Effects of Take-Overs, System Malfunction, and System Transparency

et al. 2019

View full text Add to dashboard Cite

Objective This paper presents a theoretical model and two simulator studies on the psychological processes during early trust calibration in automated vehicles. Background The positive outcomes of automation can only reach their full potential if a calibrated level of trust is achieved. In this process, information on system capabilities and limitations plays a crucial role. Method In two simulator experiments, trust was repeatedly measured during an automated drive. In Study 1, all participants in a two-group experiment experienced a system-initiated take-over, and the occurrence of a system malfunction was manipulated. In Study 2 in a 2 × 2 between-subject design, system transparency was manipulated as an additional factor. Results Trust was found to increase during the first interactions progressively. In Study 1, take-overs led to a temporary decrease in trust, as did malfunctions in both studies. Interestingly, trust was reestablished in the course of interaction for take-overs and malfunctions. In Study 2, the high transparency condition did not show a temporary decline in trust after a malfunction. Conclusion Trust is calibrated along provided information prior to and during the initial drive with an automated vehicle. The experience of take-overs and malfunctions leads to a temporary decline in trust that was recovered in the course of error-free interaction. The temporary decrease can be prevented by providing transparent information prior to system interaction. Application Transparency, also about potential limitations of the system, plays an important role in this process and should be considered in the design of tutorials and human-machine interaction (HMI) concepts of automated vehicles.

show abstract

Section: Malfunctionsmentioning

confidence: 99%

Section: Malfunctionsmentioning

confidence: 99%

The More You Know: Trust Dynamics and Calibration in Highly Automated Driving and the Effects of Take-Overs, System Malfunction, and System Transparency

et al. 2019

View full text Add to dashboard Cite

show abstract

“…In the perspective of autonomous vehicles, a digital twin refers to the efforts placed in the design and implementation of processes needed to enhance the safety, security, and privacy of driverless cars [11]. The acquired data can be integrated to generate information which, when analyzed, leads to the generation of an automatic response.…”

Section: Digital Twin Approachmentioning

confidence: 99%

Digital Twin Analysis to Promote Safety and Security in Autonomous Vehicles

Almeaibed

Al‐Rubaye

Tsourdos

et al. 2021

IEEE Comm. Stand. Mag.

View full text Add to dashboard Cite

With the new industrial revolution of digital transformation, more intelligence and autonomous systems can be adopted in the manufacturing transportation processes. Safety and security of autonomous vehicles (AV) have obvious advantages of reducing accidents and maintaining a cautious environment to drivers and pedestrians. Therefore, the transformation to data-driven vehicles is associated with the concept of digital twin, especially within the context of autonomous vehicle design. This also raises the need to adopt new safety designs to increase the resiliency and security of the whole autonomous vehicle system. To enable secure autonomous systems for smart manufacturing transportation in an end-to-end fashion, this article presents the main challenges and solutions considering safety and security functions. This article aims to identify a standard framework for vehicular digital twins that facilitate data collection, data processing, and analytics phases. To demonstrate the effectiveness of the proposed approach, a case study for vehicle follower model is analyzed when radar sensor measurements are manipulated in an attempt to cause a collision. Perceptive findings of this article can pave the way for future research aspects related to employing digital twins in the autonomous vehicle industry.

show abstract

“…It comprises 11 papers, of which the majority is concerned with safety (seven papers). Most of these studies propose design strategies for AI systems in safety-critical domains where unsafe behaviour can have immense negative consequences, like the use of architectural components as a protection layer for safety in autonomous vehicles [137],…”

Section: Software Design (34 Studies)mentioning

confidence: 99%

Software Engineering for AI-Based Systems: A Survey

Martínez-Fernández,

Bogner,

Franch

et al. 2021

Preprint

View full text Add to dashboard Cite

AI-based systems are software systems with functionalities enabled by at least one AI component (e.g., for image-and speechrecognition, and autonomous driving). AI-based systems are becoming pervasive in society due to advances in AI. However, there is limited synthesized knowledge on Software Engineering (SE) approaches for building, operating, and maintaining AI-based systems.To collect and analyze state-of-the-art knowledge about SE for AI-based systems, we conducted a systematic mapping study. We considered 248 studies published between January 2010 and March 2020. SE for AI-based systems is an emerging research area, where more than 2/3 of the studies have been published since 2018. The most studied properties of AI-based systems are dependability and safety. We identified multiple SE approaches for AI-based systems, which we classified according to the SWEBOK areas. Studies related to software testing and software quality are very prevalent, while areas like software maintenance seem neglected. Data-related issues are the most recurrent challenges. Our results are valuable for: researchers, to quickly understand the state of the art and learn which topics need more research; practitioners, to learn about the approaches and challenges that SE entails for AI-based systems; and, educators, to bridge the gap among SE and AI in their curricula. CCS Concepts: • Software and its engineering → Software creation and management; • Computing methodologies → Machine learning;

show abstract

Assuring Fully Autonomous Vehicles Safety by Design: The Autonomous Vehicle Control (AVC) Module Strategy

Cited by 35 publications

References 5 publications

The More You Know: Trust Dynamics and Calibration in Highly Automated Driving and the Effects of Take-Overs, System Malfunction, and System Transparency

The More You Know: Trust Dynamics and Calibration in Highly Automated Driving and the Effects of Take-Overs, System Malfunction, and System Transparency

Digital Twin Analysis to Promote Safety and Security in Autonomous Vehicles

Software Engineering for AI-Based Systems: A Survey

Contact Info

Product

Resources

About