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Automobile Driving

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DPSSD: Dual-Path Single-Shot Detector.

Sensors (Basel, Switzerland)
Object detection is one of the most important and challenging branches of computer vision. It has been widely used in people's lives, such as for surveillance security and autonomous driving. We propose a novel dual-path multi-scale object detection ...

Vehicle Safety-Assisted Driving Technology Based on Computer Artificial Intelligence Environment.

Computational intelligence and neuroscience
In this paper, we propose an assisted driving system implemented with a Jetson nano-high-performance embedded platform by using machine vision and deep learning technologies. The vehicle dynamics model is established under multiconditional assumption...

Evaluation of 1D and 2D Deep Convolutional Neural Networks for Driving Event Recognition.

Sensors (Basel, Switzerland)
Driving event detection and driver behavior recognition have been widely explored for many purposes, including detecting distractions, classifying driver actions, detecting kidnappings, pricing vehicle insurance, evaluating eco-driving, and managing ...

Bayesian optimization and deep learning for steering wheel angle prediction.

Scientific reports
Automated driving systems (ADS) have undergone a significant improvement in the last years. ADS and more precisely self-driving cars technologies will change the way we perceive and know the world of transportation systems in terms of user experience...

Design of Proactive Interaction for In-Vehicle Robots Based on Transparency.

Sensors (Basel, Switzerland)
Based on the transparency theory, this study investigates the appropriate amount of transparency information expressed by the in-vehicle robot under two channels of voice and visual in a proactive interaction scenario. The experiments are to test and...

Content Swapping: A New Image Synthesis for Construction Sign Detection in Autonomous Vehicles.

Sensors (Basel, Switzerland)
Construction signs alert drivers to the dangers of abnormally blocked roads. In the case of autonomous vehicles, construction signs should be detected automatically to prevent accidents. One might think that we can accomplish the goal easily using th...

Research on Vehicle Lane Change Warning Method Based on Deep Learning Image Processing.

Sensors (Basel, Switzerland)
In order to improve vehicle driving safety in a low-cost manner, we used a monocular camera to study a lane-changing warning algorithm for highway vehicles based on deep learning image processing technology. We improved the mask region-based convolut...

A hybrid deep learning approach for driver anomalous lane changing identification.

Accident; analysis and prevention
Reliable knowledge of driving states is of great importance to ensure road safety. Anomaly detection in driving behavior means recognizing anomalous driving states as a direct result of either environmental or psychological factors. This paper provid...

The Modeling of Super Deep Learning Aiming at Knowledge Acquisition in Automatic Driving.

Computational intelligence and neuroscience
In this paper, we proposed a new theory of solving the multitarget control problem by introducing a machine learning framework in automatic driving and implementing the acquisition of excellent drivers' knowledge. Nowadays, there still exist some cor...

DGInet: Dynamic graph and interaction-aware convolutional network for vehicle trajectory prediction.

Neural networks : the official journal of the International Neural Network Society
This paper investigates vehicle trajectory prediction problems in real traffic scenarios by fully harnessing the spatio-temporal dependencies between multiple vehicles. The existing GCN-based trajectory predictions are often considered in a single tr...