Privacy-Preserved In-Cabin Monitoring System for Autonomous Vehicles

Mishra, Ashutosh; Cha, Jaekwang; Kim, Shiho

doi:10.1155/2022/5389359

Cited by 5 publications

(4 citation statements)

References 49 publications

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“…However, there is significant potential in making, e.g., computer vision and audio algorithms run efficiently on embedded hardware [177], which has the added benefit of saving costs and energy consumption. In situations where visual data has to be sent to a backend, facial anonymisation via GANs [165], [166], [178] is an interesting research direction.…”

Section: Personalisation and Privacymentioning

confidence: 99%

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Audiovisual Affect Recognition for Autonomous Vehicles: Applications and Future Agendas

Karas,

Schuller,

Schuller

2024

IEEE Trans. Intell. Transport. Syst.

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Emotion and a broader range of affective and cognitive states play an important role on the road. While this has been predominantly investigated in terms of driver safety, the approaching advent of autonomous vehicles (AVs) is expected to bring a fundamental shift in focus for emotion recognition in the car, from the driver to the passengers. This work presents a number of affect-enabled applications, including adapting the driving style for an emotional experience or tailoring the infotainment to personal preferences. It attempts to foresee upcoming challenges and provides suggestions for multimodal affect modelling, with a focus on the audio and visual modalities. In particular, this includes context awareness, reliable diarisation of multiple passengers, group affect, and personalisation. Finally, we provide some recommendations on future research directions, including explainability, privacy, and holistic modelling.

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Section: Personalisation and Privacymentioning

confidence: 99%

“…Edge computing is highly relevant here [30], [182], as are distributed methods like federated learning with differential privacy [164]. Privacy preservation in AVs is a highly topical research field [166], [183], as is fairness in machine learning [184].…”

Section: E Theoretical Frameworkmentioning

confidence: 99%

Audiovisual Affect Recognition for Autonomous Vehicles: Applications and Future Agendas

Karas,

Schuller,

Schuller

2024

IEEE Trans. Intell. Transport. Syst.

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“…Encryption: As a foundational method in information security, encryption transforms data into a code to impede unauthorized access. Various algorithms-both symmetric (e.g., AES and DES) and asymmetric (e.g., RSA and ECC)-are employed to safeguard sensitive data [184].…”

Section: Privacy Preservation Techniques In Vehicular Communicationsmentioning

confidence: 99%

Autonomous Vehicles: Sophisticated Attacks, Safety Issues, Challenges, Open Topics, Blockchain, and Future Directions

Giannaros,

Karras,

Theodorakopoulos

et al. 2023

JCP

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Autonomous vehicles (AVs), defined as vehicles capable of navigation and decision-making independent of human intervention, represent a revolutionary advancement in transportation technology. These vehicles operate by synthesizing an array of sophisticated technologies, including sensors, cameras, GPS, radar, light imaging detection and ranging (LiDAR), and advanced computing systems. These components work in concert to accurately perceive the vehicle’s environment, ensuring the capacity to make optimal decisions in real-time. At the heart of AV functionality lies the ability to facilitate intercommunication between vehicles and with critical road infrastructure—a characteristic that, while central to their efficacy, also renders them susceptible to cyber threats. The potential infiltration of these communication channels poses a severe threat, enabling the possibility of personal information theft or the introduction of malicious software that could compromise vehicle safety. This paper offers a comprehensive exploration of the current state of AV technology, particularly examining the intersection of autonomous vehicles and emotional intelligence. We delve into an extensive analysis of recent research on safety lapses and security vulnerabilities in autonomous vehicles, placing specific emphasis on the different types of cyber attacks to which they are susceptible. We further explore the various security solutions that have been proposed and implemented to address these threats. The discussion not only provides an overview of the existing challenges but also presents a pathway toward future research directions. This includes potential advancements in the AV field, the continued refinement of safety measures, and the development of more robust, resilient security mechanisms. Ultimately, this paper seeks to contribute to a deeper understanding of the safety and security landscape of autonomous vehicles, fostering discourse on the intricate balance between technological advancement and security in this rapidly evolving field.

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“…Facial anonymization serves to effectively overcome this issue, as the anonymous face protects personal information during in-cabin monitoring. In our previous works, we have proposed an efficient algorithm for face anonymization of the occupants [5,6,52]. The generative adversarial network (GAN) is utilized for facial swapping and reenactment.…”

Section: Face Anonymization: Privacy-preserved Monitoring In Publicmentioning

confidence: 99%

In-Cabin Monitoring System for Autonomous Vehicles

Mishra

Lee

Kim

et al. 2022

Sensors

Self Cite

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In this paper, we have demonstrated a robust in-cabin monitoring system (IMS) for safety, security, surveillance, and monitoring, including privacy concerns for personal and shared autonomous vehicles (AVs). It consists of a set of monitoring cameras and an onboard device (OBD) equipped with artificial intelligence (AI). Hereafter, this combination of a camera and an OBD is referred to as the AI camera. We have investigated the issues for mobility services in higher levels of autonomous driving, what needs to be monitored, how to monitor, etc. Our proposed IMS is an on-device AI system that indigenously has improved the privacy of the users. Furthermore, we have enlisted the essential actions to be considered in an IMS and developed an appropriate database (DB). Our DB consists of multifaced scenarios important for monitoring the in-cabin of the higher-level AVs. Moreover, we have compared popular AI models applied for object and occupant recognition. In addition, our DB is available on request to support the research on the development of seamless monitoring of the in-cabin higher levels of autonomous driving for the assurance of safety and security.

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Privacy-Preserved In-Cabin Monitoring System for Autonomous Vehicles

Cited by 5 publications

References 49 publications

Audiovisual Affect Recognition for Autonomous Vehicles: Applications and Future Agendas

Audiovisual Affect Recognition for Autonomous Vehicles: Applications and Future Agendas

Autonomous Vehicles: Sophisticated Attacks, Safety Issues, Challenges, Open Topics, Blockchain, and Future Directions

In-Cabin Monitoring System for Autonomous Vehicles

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