AIMC Topic: Automobile Driving

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Daily motionless activities: A dataset with accelerometer, magnetometer, gyroscope, environment, and GPS data.

Scientific data
The dataset presented in this paper presents a dataset related to three motionless activities, including driving, watching TV, and sleeping. During these activities, the mobile device may be positioned in different locations, including the pants pock...

Multimodal driver state modeling through unsupervised learning.

Accident; analysis and prevention
Naturalistic driving data (NDD) can help understand drivers' reactions to each driving scenario and provide personalized context to driving behavior. However, NDD requires a high amount of manual labor to label certain driver's state and behavioral p...

Discriminative Transfer Learning for Driving Pattern Recognition in Unlabeled Scenes.

IEEE transactions on cybernetics
Driving pattern recognition based on features, such as GPS, gear, and speed information, is essential to develop intelligent transportation systems. However, it is usually expensive and labor intensive to collect a large amount of labeled driving dat...

A Hybrid Model for Driver Emotion Detection Using Feature Fusion Approach.

International journal of environmental research and public health
Machine and deep learning techniques are two branches of artificial intelligence that have proven very efficient in solving advanced human problems. The automotive industry is currently using this technology to support drivers with advanced driver as...

Deep-Learning-Based Parking Area and Collision Risk Area Detection Using AVM in Autonomous Parking Situation.

Sensors (Basel, Switzerland)
In this paper, I propose a bird eye view image detection method for parking areas and collision risk areas at the same time in parking situations. Deep learning algorithms using area detection and semantic segmentation were used. The main architectur...

E2DR: A Deep Learning Ensemble-Based Driver Distraction Detection with Recommendations Model.

Sensors (Basel, Switzerland)
The increasing number of car accidents is a significant issue in current transportation systems. According to the World Health Organization (WHO), road accidents are the eighth highest top cause of death around the world. More than 80% of road accide...

Outracing champion Gran Turismo drivers with deep reinforcement learning.

Nature
Many potential applications of artificial intelligence involve making real-time decisions in physical systems while interacting with humans. Automobile racing represents an extreme example of these conditions; drivers must execute complex tactical ma...

Hybrid Deep Learning Approach for Traffic Speed Prediction.

Big data
Traffic speed prediction plays a fundamental role in traffic management and driving route planning. However, timely accurate traffic speed prediction is challenging as it is affected by complex spatial and temporal correlations. Most existing works c...

Driver Behavior Profiling and Recognition Using Deep-Learning Methods: In Accordance with Traffic Regulations and Experts Guidelines.

International journal of environmental research and public health
The process of collecting driving data and using a computational model to generate a safety score for the driver is known as driver behavior profiling. Existing driver profiles attempt to categorize drivers as either safe or aggressive, which some ex...

Fast Panoptic Segmentation with Soft Attention Embeddings.

Sensors (Basel, Switzerland)
Panoptic segmentation provides a rich 2D environment representation by unifying semantic and instance segmentation. Most current state-of-the-art panoptic segmentation methods are built upon two-stage detectors and are not suitable for real-time appl...