[HTML][HTML] Machine learning applied to road safety modeling: A systematic literature review
Road safety modeling is a valuable strategy for promoting safe mobility, enabling the
development of crash prediction models (CPM) and the investigation of factors contributing …
development of crash prediction models (CPM) and the investigation of factors contributing …
Road traffic accidents: An overview of data sources, analysis techniques and contributing factors
Road traffic accidents are one among the world's leading causes of injuries and fatalities
and hence represent an important field of research towards the use of traffic accident …
and hence represent an important field of research towards the use of traffic accident …
Deep spatio-temporal graph convolutional network for traffic accident prediction
Traffic accidents usually lead to severe human casualties and huge economic losses in real-
world scenarios. Timely accurate prediction of traffic accidents has great potential to protect …
world scenarios. Timely accurate prediction of traffic accidents has great potential to protect …
Road safety assessment and risks prioritization using an integrated SWARA and MARCOS approach under spherical fuzzy environment
S Jafarzadeh Ghoushchi… - Neural computing and …, 2023 - Springer
There are a lot of elements that make road safety assessment situations unpredictable and
hard to understand. This could put people's lives in danger, hurt the mental health of a …
hard to understand. This could put people's lives in danger, hurt the mental health of a …
[HTML][HTML] A study on road accident prediction and contributing factors using explainable machine learning models: analysis and performance
Road accidents are increasing worldwide and are causing millions of deaths each year.
They impose significant financial and economic expenses on society. Existing research has …
They impose significant financial and economic expenses on society. Existing research has …
Self-driving vehicles—an ethical overview
The introduction of self-driving vehicles gives rise to a large number of ethical issues that go
beyond the common, extremely narrow, focus on improbable dilemma-like scenarios. This …
beyond the common, extremely narrow, focus on improbable dilemma-like scenarios. This …
Deep learning-based speed bump detection model for intelligent vehicle system using raspberry Pi
DK Dewangan, SP Sahu - IEEE sensors journal, 2020 - ieeexplore.ieee.org
Artificial intelligence in vision based approaches have proven to be effective in various
phases of intelligent vehicle system (IVS). An IVS has to intelligently take many critical …
phases of intelligent vehicle system (IVS). An IVS has to intelligently take many critical …
Blockchain-enabled certificate-based authentication for vehicle accident detection and notification in intelligent transportation systems
As the communications among the vehicles, the Road-Side Units (RSU) and the Edge
Servers (ES) take place via wireless communication and the Internet, an adversary may take …
Servers (ES) take place via wireless communication and the Internet, an adversary may take …
A new safe lane-change trajectory model and collision avoidance control method for automatic driving vehicles
T Peng, L Su, R Zhang, Z Guan, H Zhao, Z Qiu… - Expert Systems with …, 2020 - Elsevier
Lane change maneuvers, are important contributors to road traffic accidents on highway. In
this paper, we propose a new safe lane change trajectory model and collision avoidance …
this paper, we propose a new safe lane change trajectory model and collision avoidance …
Inferring heterogeneous treatment effects of crashes on highway traffic: A doubly robust causal machine learning approach
Accurate estimating causal effects of crashes on highway traffic is crucial for mitigating the
negative impacts of crashes. Previous studies have built up a series of methods via …
negative impacts of crashes. Previous studies have built up a series of methods via …