Driving Behavior Classification and Sharing System Using CNN-LSTM Approaches and V2X Communication

Abstract

Despite advances in autonomous driving technology, traffic accidents remain a problem to be solved in the transportation system. More than half of traffic accidents are due to unsafe driving. In addition, aggressive driving behavior can lead to traffic jams. To reduce this, we propose a 4-layer CNN-2 stack LSTM-based driving behavior classification and V2X sharing system that uses time-series data as an input to reflect temporal changes. The proposed system classifies driving behavior into defensive, normal, and aggressive driving using only the 3-axis acceleration of the driving vehicle and shares it with the surroundings. We collect a training dataset by composing a road that reflects various environmental factors using a driving simulator that mimics a real vehicle and IPG CarMaker, an autonomous driving simulation. Additionally, driving behavior datasets are collected by driving real-world DGIST campus to augment training data. The proposed network has the best performance compared to the state-of-the-art CNN, LSTM, and CNN-LSTM. Finally, our system shares the driving behavior classified by 4-layer CNN-2 stacked LSTM with surrounding vehicles through V2X communication. The proposed system has been validated in ACC simulations and real environments. For real world testing, we configure NVIDIA Jetson TX2, IMU, GPS, and V2X devices as one module. We performed the experiments of the driving behavior classification and V2X transmission and reception in a real world by using the prototype module. As a result of the experiment, the driving behavior classification performance was confirmed to be ~98% or more in the simulation test and 97% or more in the real-world test. In addition, the V2X communication delay through the prototype was confirmed to be an average of 4.8 ms. The proposed system can contribute to improving the safety of the transportation system by sharing the driving behaviors of each vehicle.

Handover mechanisms in VMC systems: Evaluating the reliability of V2X as an alternative to fiber networks in handover areas

“…In this section we describe the concept of our model, which is a VMC system based on the idea [1] of utilizing fog computing to compute the resources of nearby vehicles to enable distributed computing This enables vehicles to share resources, such as processing power and storage, and collaborate to perform tasks more efficiently.…”

“…As the demand for more intelligent and autonomous cars show a significant increasing trend, auto manufacturers are more motivated to renew communication equipment. In the past decade, Vehicle-to-Vehicle Communication (V2V) and Vehicle-to-Infrastructure Communication (V2I) is developed [1], which is the first light of morning for autonomous driving. To make cars smarter, Vehicle-to-Everything Communication (V2X) is developed.…”