Related papers: Crash Severity Prediction Using Deep Learning Appr…

One debatable issue in traffic safety research is that cognitive load from sec-ondary tasks reduces primary task performance, such as driving. Although physiological signals have been extensively used in driving-related research to assess…

The deep learning technique has been applied for the first time to investigate the possibility of centrality determination in terms of the number of participants ($N_{\mathrm{part}}$) in high-energy heavy-ion collisions. For this purpose,…

This study investigates crash severity risk modeling strategies for work zones involving large vehicles (i.e., trucks, buses, and vans) under crash data imbalance between low-severity (LS) and high-severity (HS) crashes. We utilized crash…

Remaining Useful Life (RUL) of a component or a system is defined as the length from the current time to the end of the useful life. Accurate RUL estimation plays a crucial role in Predictive Maintenance applications. Traditional regression…

Lane detection for autonomous vehicles is an important concept, yet it is a challenging issue of driver assistance systems in modern vehicles. The emergence of deep learning leads to significant progress in self-driving cars. Conventional…

Deep neural networks, albeit their great success on feature learning in various computer vision tasks, are usually considered as impractical for online visual tracking because they require very long training time and a large number of…

We report the largest scale deep learning with High Performance Computing (HPC) to physics analysis with the CMS simulation data in proton-proton collisions at 13 TeV. We build a Convolutional Neural Network (CNN) model that takes low-level…

Traffic accidents pose a significant threat to public safety, resulting in numerous fatalities, injuries, and a substantial economic burden each year. The development of predictive models capable of real-time forecasting of post-accident…

In this paper, we propose deep learning architectures (FNN, CNN and LSTM) to forecast a regression model for time dependent data. These algorithm's are designed to handle Floating Car Data (FCD) historic speeds to predict road traffic data.…

Deep learning-based approaches have been widely used for training controllers for autonomous vehicles due to their powerful ability to approximate nonlinear functions or policies. However, the training process usually requires large labeled…

A rise in popularity of Deep Neural Networks (DNNs), attributed to more powerful GPUs and widely available datasets, has seen them being increasingly used within safety-critical domains. One such domain, self-driving, has benefited from…

Accurate prediction of flight-level passenger traffic is of paramount importance in airline operations, influencing key decisions from pricing to route optimization. This study introduces a novel, multimodal deep learning approach to the…

Highway traffic crashes exert a considerable impact on both transportation systems and the economy. In this context, accurate and dependable emergency responses are crucial for effective traffic management. However, the influence of crashes…

Road accidents have significant economic and societal costs, with a small number of severe accidents accounting for a large portion of these costs. Predicting accident severity can help in the proactive approach to road safety by…

Road roughness is a very important road condition for the infrastructure, as the roughness affects both the safety and ride comfort of passengers. The roads deteriorate over time which means the road roughness must be continuously monitored…

Urban traffic flow prediction using data-driven models can play an important role in route planning and preventing congestion on highways. These methods utilize data collected from traffic recording stations at different timestamps to…

A deep learning based method with the convolutional neural network (CNN) algorithm for determining the impact parameters is developed using the constrained molecular dynamics model simulations, focusing on the heavy-ion collisions at the…

To assist human drivers and autonomous vehicles in assessing crash risks, driving scene analysis using dash cameras on vehicles and deep learning algorithms is of paramount importance. Although these technologies are increasingly available,…

This paper investigates the application of the latest machine learning technique deep neural networks for classifying road surface conditions (RSC) based on images from smartphones. Traditional machine learning techniques such as support…

Vehicle trajectory prediction is essential for enabling safety-critical intelligent transportation systems (ITS) applications used in management and operations. While there have been some promising advances in the field, there is a need for…