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Autonomous Vehicles

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Exploring the artificial intelligence "Trust paradox": Evidence from a survey experiment in the United States.

PloS one
Advances in Artificial Intelligence (AI) are poised to transform society, national defense, and the economy by increasing efficiency, precision, and safety. Yet, widespread adoption within society depends on public trust and willingness to use AI-ena...

An object detection algorithm combining self-attention and YOLOv4 in traffic scene.

PloS one
Automobile intelligence is the trend for modern automobiles, of which environment perception is the key technology of intelligent automobile research. For autonomous vehicles, the detection of object information, such as vehicles and pedestrians in t...

Autonomous Driving Control Based on the Technique of Semantic Segmentation.

Sensors (Basel, Switzerland)
Advanced Driver Assistance Systems (ADAS) are only applied to relatively simple scenarios, such as highways. If there is an emergency while driving, the driver should take control of the car to deal properly with the situation at any time. Obviously,...

Dense reinforcement learning for safety validation of autonomous vehicles.

Nature
One critical bottleneck that impedes the development and deployment of autonomous vehicles is the prohibitively high economic and time costs required to validate their safety in a naturalistic driving environment, owing to the rarity of safety-critic...

Robust Detection, Segmentation, and Metrology of High Bandwidth Memory 3D Scans Using an Improved Semi-Supervised Deep Learning Approach.

Sensors (Basel, Switzerland)
Recent advancements in 3D deep learning have led to significant progress in improving accuracy and reducing processing time, with applications spanning various domains such as medical imaging, robotics, and autonomous vehicle navigation for identifyi...

Data generation for connected and automated vehicle tests using deep learning models.

Accident; analysis and prevention
For the simulation-based test and evaluation of connected and automated vehicles (CAVs), the trajectory of the background vehicle has a direct effect on the performance of CAVs and experiment outcomes. The collected real trajectory data are limited b...

Analysis of Friction Noise Mechanism in Lead Screw System of Autonomous Vehicle Seats and Dynamic Instability Prediction Based on Deep Neural Network.

Sensors (Basel, Switzerland)
This study investigated the squeal mechanism induced by friction in a lead screw system. The dynamic instability in the friction noise model of the lead screw was derived through a complex eigenvalue analysis via a finite element model. A two degree ...

Neuromorphic Sentiment Analysis Using Spiking Neural Networks.

Sensors (Basel, Switzerland)
Over the past decade, the artificial neural networks domain has seen a considerable embracement of deep neural networks among many applications. However, deep neural networks are typically computationally complex and consume high power, hindering the...

Robots at your doorstep: acceptance of near-future technologies for automated parcel delivery.

Scientific reports
The logistics and delivery industry is undergoing a technology-driven transformation, with robotics, drones, and autonomous vehicles expected to play a key role in meeting the growing challenges of last-mile delivery. To understand the public accepta...

Spatiotemporal multi-feature fusion vehicle trajectory anomaly detection for intelligent transportation: An improved method combining autoencoders and dynamic Bayesian networks.

Accident; analysis and prevention
With the continuous development of intelligent transportation systems, traffic safety has become a major societal concern, and vehicle trajectory anomaly detection technology has emerged as a crucial method to ensure safety. However, current technolo...