AIMC Topic: Automobile Driving

Clear Filters Showing 151 to 160 of 272 articles

A Hybrid Deep Learning Model for Recognizing Actions of Distracted Drivers.

Sensors (Basel, Switzerland)
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...

Feasibility of a Neural Network-Based Virtual Sensor for Vehicle Unsprung Mass Relative Velocity Estimation.

Sensors (Basel, Switzerland)
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...

LiDAR-driven spiking neural network for collision avoidance in autonomous driving.

Bioinspiration & biomimetics
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...

Deep Q-network-based traffic signal control models.

PloS one
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...

Modeling learner-controlled mental model learning processes by a second-order adaptive network model.

PloS one
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 (...

Vehicle Trajectory Prediction Using Hierarchical Graph Neural Network for Considering Interaction among Multimodal Maneuvers.

Sensors (Basel, Switzerland)
Predicting the trajectories of surrounding vehicles by considering their interactions is an essential ability for the functioning of autonomous vehicles. The subsequent movement of a vehicle is decided based on the multiple maneuvers of surrounding v...

A Review of Intelligent Driving Pedestrian Detection Based on Deep Learning.

Computational intelligence and neuroscience
Pedestrian detection is a specific application of object detection. Compared with general object detection, it shows similarities and unique characteristics. In addition, it has important application value in the fields of intelligent driving and sec...

Collaborative driving style classification method enabled by majority voting ensemble learning for enhancing classification performance.

PloS one
The classification of driving styles plays a fundamental role in evaluating drivers' driving behaviors, which is of great significance to traffic safety. However, it still suffers from various challenges, including the insufficient accuracy of the mo...

Driving Behaviour Analysis Using Machine and Deep Learning Methods for Continuous Streams of Vehicular Data.

Sensors (Basel, Switzerland)
In the last few decades, vehicles are equipped with a plethora of sensors which can provide useful measurements and diagnostics for both the vehicle's condition as well as the driver's behaviour. Furthermore, the rapid increase for transportation nee...

Vehicle trajectory prediction and generation using LSTM models and GANs.

PloS one
Vehicles' trajectory prediction is a topic with growing interest in recent years, as there are applications in several domains ranging from autonomous driving to traffic congestion prediction and urban planning. Predicting trajectories starting from ...