AIMC Topic: Automobiles

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Human decision-making biases in the moral dilemmas of autonomous vehicles.

Scientific reports
The development of artificial intelligence has led researchers to study the ethical principles that should guide machine behavior. The challenge in building machine morality based on people's moral decisions, however, is accounting for the biases in ...

The Retribution-Gap and Responsibility-Loci Related to Robots and Automated Technologies: A Reply to Nyholm.

Science and engineering ethics
Automated technologies and robots make decisions that cannot always be fully controlled or predicted. In addition to that, they cannot respond to punishment and blame in the ways humans do. Therefore, when automated cars harm or kill people, for exam...

Significance of Softmax-Based Features in Comparison to Distance Metric Learning-Based Features.

IEEE transactions on pattern analysis and machine intelligence
End-to-end distance metric learning (DML) has been applied to obtain features useful in many computer vision tasks. However, these DML studies have not provided equitable comparisons between features extracted from DML-based networks and softmax-base...

AEB effectiveness evaluation based on car-to-cyclist accident reconstructions using video of drive recorder.

Traffic injury prevention
OBJECTIVE: Though autonomous emergency braking (AEB) systems for car-to-cyclist collisions have been under development, an estimate of the benefit of AEB systems based on an analysis of accident data is needed for further enhancing their development....

Pedestrian's risk-based negotiation model for self-driving vehicles to get the right of way.

Accident; analysis and prevention
Negotiations among drivers and pedestrians are common on roads, but it is still challenging for a self-driving vehicle to negotiate for its right of way with other human road users, especially pedestrians. Currently, the self-driving vehicles are pro...

A novel approach to predicting human ingress motion using an artificial neural network.

Journal of biomechanics
Due to the increased availability of digital human models, the need for knowing human movement is important in product design process. If the human motion is derived rapidly as design parameters change, a developer could determine the optimal paramet...

Analysis of body pressure distribution on car seats by using deep learning.

Applied ergonomics
This study aimed to extract information from body pressure distribution, including comfort, participant body size, and seat characteristics by using supervised deep learning, and body pressure characteristics corresponding to sensory evaluation by us...

A spatiotemporal deep learning approach for citywide short-term crash risk prediction with multi-source data.

Accident; analysis and prevention
The primary objective of this study is to investigate how the deep learning approach contributes to citywide short-term crash risk prediction by leveraging multi-source datasets. This study uses data collected from Manhattan in New York City to illus...

Multi-features taxi destination prediction with frequency domain processing.

PloS one
The traditional taxi prediction methods model the taxi trajectory as a sequence of spatial points. It cannot represent two-dimensional spatial relationships between trajectory points. Therefore, many methods transform the taxi GPS trajectory into a t...

Deep Learning-Based Gaze Detection System for Automobile Drivers Using a NIR Camera Sensor.

Sensors (Basel, Switzerland)
A paradigm shift is required to prevent the increasing automobile accident deaths that are mostly due to the inattentive behavior of drivers. Knowledge of gaze region can provide valuable information regarding a driver's point of attention. Accurate ...