Constraining Design: Applying the Insights of Cognitive Work Analysis to the Design of Novel In-Car Interfaces to Support Eco-Driving

Allison, Craig K.; Stanton, Neville A.

doi:10.1007/s42154-020-00090-5

Cited by 15 publications

(6 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This information is then used to provide accurate navigation guidance. In-car navigation systems rely on detailed map data, including road networks, landmarks, points of interest (POIs), and traffic information [12][13]. In-car navigation systems often provide voice-guided instructions to drivers, ensuring hands-free operation and minimizing distractions.…”

Section: In-car Navigation Systemsmentioning

confidence: 99%

Federated Learning-Based Detection and Control Mechanism of In-Car Navigation Safety System

Gao,

Zhang,

2024

J Multimed Inf Syst

View full text Add to dashboard Cite

In-car navigation systems face challenges in ensuring safety while preserving data privacy. This paper proposes PrivNav, a federated learning scheme integrating differential privacy and secure multi-party computation for privacy-preserving learning. PrivNav enables vehicles to collaboratively train a model by aggregating locally computed updates without sharing raw data. Perturbations and secret sharing protect sensitive information and prevent inference attacks. PrivNav outperforms existing federated learning schemes by accommodating user dropouts, supporting customizable aggregation methods beyond FedAvg, and extending to decentralized scenarios without a trusted authority. Experiments demonstrate PrivNav's strong privacy guarantees and high accuracy, significantly enhancing the detection and control capabilities of in-car navigation safety systems. Precise event detection, abnormal situation differentiation, and reduced false alarms are achieved, improving overall system safety, trust, and performance.

show abstract

Section: In-car Navigation Systemsmentioning

confidence: 99%

Federated Learning-Based Detection and Control Mechanism of In-Car Navigation Safety System

Gao,

Zhang,

2024

J Multimed Inf Syst

View full text Add to dashboard Cite

show abstract

“…This information is then used to provide accurate navigation guidance. In-car navigation systems rely on detailed map data, including road networks, landmarks, points of interest (POIs), and tra c information [12,13]. Maps are preloaded in the system or accessed through online connectivity, enabling real-time updates and dynamic routing.…”

Section: In-car Navigation Systemsmentioning

confidence: 99%

Federated learning-based detection and control mechanism of in-car navigation safety system

Gao

Zhang

2023

Preprint

View full text Add to dashboard Cite

The advancement of in-car navigation systems has dramatically improved driving experiences. However, ensuring the safety of these systems remains a critical concern. Federated learning provides a new solution for cooperative learning between non-mutually trusted entities. Through the mode of local training and central aggregation, the local data privacy of each entity is protected while training the global model. To achieve this, a federated learning method for deep learning that preserves privacy is developed by integrating differential privacy with secure multi-party computing. In this scheme, vehicles add perturbations to the local models obtained by local training and secretly share them with multiple central servers. The scheme protects the local information uploaded by users from being stolen and prevents the adversary from malicious inference from globally shared information such as the aggregation model. Additionally, the scheme enables users dropping out and implements a variety of aggregating methods. The aforementioned system may also easily be expanded to decentralized scenarios for real-world applications devoid of a trustworthy center. The experimental findings show that, in order to protect sensitive data obtained from in-car navigation systems during learning, the suggested strategy heavily emphasizes privacy protection. Simultaneously, the high accuracy achieved through the proposed federated learning scheme significantly enhances in-car navigation safety systems' detection and control capabilities. It enables precise and reliable event detection, differentiation of abnormal situations, and reduces false alarms, improving overall safety, user trust, and system performance.

show abstract

“…Several studies have introduced the human factor to autonomous driving systems [286]. For instance, [287] presented a proof-of-concept investigation to demonstrate that the autonomous driving design can be benefited from the cognitive work analysis. [288] presented an autonomous vehicle model based on dynamic human behavior.…”

Section: Ddt In Automated Vehicle Developmentmentioning

confidence: 99%

Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles

Lou

Xing

et al. 2022

IEEE Trans. Intell. Veh.

View full text Add to dashboard Cite

Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the design of the automated vehicle, whereas the digitization of the human driver, who plays an important role in driving, is largely ignored. Furthermore, previous driver-related tasks are limited to specific scenarios and have limited applicability. Thus, a novel concept of a driver digital twin (DDT) is proposed in this study to bridge the gap between existing automated driving systems and fully digitized ones and aid in the development of a complete driving human cyber-physical system (H-CPS). This concept is essential for constructing a harmonious human-centric intelligent driving system that considers the proactivity and sensitivity of the human driver. The primary characteristics of the DDT include multimodal state fusion, personalized modeling, and time variance. Compared with the original DT, the proposed DDT emphasizes on internal personality and capability with respect to the external physiological-level state. This study systematically illustrates the DDT and outlines its key enabling aspects. The related technologies are comprehensively reviewed and discussed with a view to improving them by leveraging the DDT. In addition, the potential applications and unsettled challenges are considered. This study aims to provide fundamental theoretical support to researchers in determining the future scope of the DDT system.

show abstract

Constraining Design: Applying the Insights of Cognitive Work Analysis to the Design of Novel In-Car Interfaces to Support Eco-Driving

Cited by 15 publications

References 38 publications

Federated Learning-Based Detection and Control Mechanism of In-Car Navigation Safety System

Federated Learning-Based Detection and Control Mechanism of In-Car Navigation Safety System

Federated learning-based detection and control mechanism of in-car navigation safety system

Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles

Contact Info

Product

Resources

About