In the rapidly evolving landscape of new energy vehicles (NEVs), a groundbreaking study has emerged that could significantly enhance traffic safety and bolster the commercial viability of the sector. Published in the journal “Frontiers in Sustainable Cities,” the research introduces an innovative accident risk prediction method for NEVs, developed by Xiang Zhang and colleagues at the Traffic Management Research Institute of the Ministry of Public Security in Wuxi, China.
As NEVs gain traction in the pursuit of low-carbon and sustainable development, the frequency of accidents involving these vehicles has also been on the rise. This presents a critical challenge for the energy and automotive industries, as safety concerns can hinder consumer adoption and market growth. The study by Zhang and his team aims to address this issue head-on by leveraging advanced machine learning techniques to predict accident risks with unprecedented accuracy.
The researchers utilized a comprehensive dataset of full-year accident data from a province in China in 2021. They extracted both direct and indirect data strongly related to accident risk, including environmental factors such as weather and road type, dynamic operating data like speed, vehicle alarm status, and historical accident characteristics. To capture the potential risk characteristics of the vehicle, the team employed Long Short-Term Memory (LSTM) layers to construct dynamic and static feature vectors representing vehicle accident risk.
“By integrating dynamic and static features, we can quantify and capture the nuanced risk factors associated with NEVs,” explained Xiang Zhang, the lead author of the study. “This holistic approach allows us to predict accident risks more accurately and provide timely warnings to drivers.”
The proposed model calculates the accident risk probability using fully connected layers and the sigmoid activation function. When tested and validated with real accident data, the model achieved an impressive prediction accuracy of 85% for new energy vehicle accidents. This represents a 24% improvement over traditional models that rely solely on weather and road types.
The implications of this research are far-reaching for the energy and automotive sectors. Enhanced accident risk prediction can lead to safer roads, increased consumer confidence, and accelerated adoption of NEVs. This, in turn, can drive demand for advanced batteries, charging infrastructure, and other components critical to the energy sector.
“Our model can timely warn drivers before accidents occur, helping them take necessary safety measures to reduce accident probability,” added Zhang. “This not only saves lives but also contributes to the overall sustainability and commercial success of new energy vehicles.”
As the world continues to transition towards sustainable energy solutions, the integration of advanced technologies like LSTM-based risk prediction models will be crucial. This research paves the way for future developments in traffic safety and energy innovation, shaping a safer and more sustainable future for all.
The study, “Risk prediction of new energy vehicle based on dynamic-static feature fusion,” was published in the journal “Frontiers in Sustainable Cities,” highlighting the intersection of urban sustainability and cutting-edge technology.