AIMC Topic: Automobile Driving

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Real-Time Semantic Segmentation with Dual Encoder and Self-Attention Mechanism for Autonomous Driving.

Sensors (Basel, Switzerland) Dec 2, 2021
As the techniques of autonomous driving become increasingly valued and universal, real-time semantic segmentation has become very popular and challenging in the field of deep learning and computer vision in recent years. However, in order to apply th...
Automobile Driving Semantics Neural Networks, Computer Image Processing, Computer-Assisted
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Vision-Based Driver's Cognitive Load Classification Considering Eye Movement Using Machine Learning and Deep Learning.

Sensors (Basel, Switzerland) Nov 30, 2021
Due to the advancement of science and technology, modern cars are highly technical, more activity occurs inside the car and driving is faster; however, statistics show that the number of road fatalities have increased in recent years because of drive...
Eye Movements Cognition Accidents, Traffic Automobile Driving Machine Learning Deep Learning Humans
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Vehicle Trajectory Estimation Based on Fusion of Visual Motion Features and Deep Learning.

Sensors (Basel, Switzerland) Nov 29, 2021
Driver situation awareness is critical for safety. In this paper, we propose a fast, accurate method for obtaining real-time situation awareness using a single type of sensor: monocular cameras. The system tracks the host vehicle's trajectory using s...
Motion Automobile Driving Algorithms Deep Learning Neural Networks, Computer
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An Intelligent Multimode Clustering Mechanism Using Driving Pattern Recognition in Cognitive Internet of Vehicles.

Sensors (Basel, Switzerland) Nov 15, 2021
Connected autonomous vehicles can leverage communication and artificial intelligence technologies to effectively overcome the perceived limitations of individuals and enhance driving safety and stability. However, due to the high dynamics of the vehi...
Cluster Analysis Internet Cognition Automobile Driving Humans Artificial Intelligence
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A Review of Deep Learning-Based Methods for Pedestrian Trajectory Prediction.

Sensors (Basel, Switzerland) Nov 13, 2021
Pedestrian trajectory prediction is one of the main concerns of computer vision problems in the automotive industry, especially in the field of advanced driver assistance systems. The ability to anticipate the next movements of pedestrians on the str...
Technology Deep Learning Humans Automobile Driving Pedestrians Signal Processing, Computer-Assisted Accidents, Traffic
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A Hybrid Deep Learning Model for Recognizing Actions of Distracted Drivers.

Sensors (Basel, Switzerland) Nov 8, 2021
With the rapid spreading of in-vehicle information systems such as smartphones, navigation systems, and radios, the number of traffic accidents caused by driver distractions shows an increasing trend. Timely identification and warning are deemed to b...
Automobile Driving Distracted Driving Accidents, Traffic Humans Neural Networks, Computer Deep Learning
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Feasibility of a Neural Network-Based Virtual Sensor for Vehicle Unsprung Mass Relative Velocity Estimation.

Sensors (Basel, Switzerland) Oct 27, 2021
With the automotive industry moving towards automated driving, sensing is increasingly important in enabling technology. The virtual sensors allow data fusion from various vehicle sensors and provide a prediction for measurement that is hard or too e...
Automobile Driving Neural Networks, Computer Bayes Theorem Feasibility Studies
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LiDAR-driven spiking neural network for collision avoidance in autonomous driving.

Bioinspiration & biomimetics Oct 25, 2021
Facilitated by advances in real-time sensing, low and high-level control, and machine learning, autonomous vehicles draw ever-increasing attention from many branches of knowledge. Neuromorphic (brain-inspired) implementation of robotic control has be...
Neural Networks, Computer Machine Learning Automobile Driving Robotics Autonomous Vehicles
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Deep Q-network-based traffic signal control models.

PloS one Sep 2, 2021
Traffic congestion has become common in urban areas worldwide. To solve this problem, the method of searching a solution using artificial intelligence has recently attracted widespread attention because it can solve complex problems such as traffic s...
Automobile Driving Neural Networks, Computer Motor Vehicles Computer Simulation Humans
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Modeling learner-controlled mental model learning processes by a second-order adaptive network model.

PloS one Aug 24, 2021
Learning knowledge or skills usually is considered to be based on the formation of an adequate internal mental model as a specific type of mental network. The learning process for such a mental model conceptualised as a mental network, is a form of (...
Models, Statistical Automobile Driving Learning Humans Models, Psychological Neural Networks, Computer Adaptation, Physiological
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