Abnormal Driving Detection With Normalized Driving Behavior Data: A Deep Learning Approach

Abnormal Behavior Detection In Intelligent Transport System For Intelligent Driving

Vehicle Ad hoc Networks (VANET) emerged as an application of Mobile Ad hoc Networks (MANET), which use Dedicated Short Range Communication (DSRC) to allow vehicles in close proximity to communicate with each other, or to communicate with roadside equipment. Applying wireless access technology in vehicular environments has led to the improvement of road safety and a reduction in the number of fa...

Characterizing Driving Styles with Deep Learning

Characterizing driving styles of human drivers using vehicle sensor data, e.g., GPS, is an interesting research problem and an important real-world requirement from automotive industries. A good representation of driving features can be highly valuable for autonomous driving, auto insurance, and many other application scenarios. However, traditional methods mainly rely on handcrafted features, ...

Deep learning for driving detection on mobile phones

Sensor based activity recognition is a critical component of mobile phone based applications aimed at driving detection. Current methodologies consist of hand-engineered features input into discriminative models, and experiments to date have been restricted to small scale studies of O(10) users. Here we show how convolutional neural networks can be used to learn features from raw and spectrogra...

Object Detection for Autonomous Driving Using Deep Learning

Melanoma detection with a deep learning model

Background: Skin cancer is one of the most common forms of cancer in the world and melanoma is the deadliest type of skin cancer. Both melanoma and melanocytic nevi begin in melanocytes (cells that produce melanin). However, melanocytic nevi are benign whereas melanoma is malignant. This work proposes a deep learning model for classification of these two lesions.    Methods: In this analytic s...