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.
Highlights
In the past decade, remarkable advances have been realized in various fields such as object detection [1,2], tracking [3,4], control [5], and Vehicle-to-Everything (V2X) communication [6,7] to achieve the goal of autonomous driving
We propose a system that broadcasts the predicted behavior through V2X with a 4layer CNN-2 stacked Long Short-Term Memory (LSTM) for driving behavior classification using only 3-axis acceleration
The virtual world driving data was collected by building DGIST campus, urban road and highway based on the connection between IPG CarMaker and a driving simulator that mimics real vehicles
Summary
Remarkable advances have been realized in various fields such as object detection [1,2], tracking [3,4], control [5], and Vehicle-to-Everything (V2X) communication [6,7] to achieve the goal of autonomous driving. V2X provides vehicle-to-vehicle, and vehicle-to-infrastructure connectivity, providing safety applications such as construction section warnings, stop sign violations and curve speed warnings [10]. These driving applications and safety applications have helped reduce the stress on many drivers [11,12]. We aim to improve traffic safety by predicting aggressive driving behavior using only low-cost sensor data and sharing it with the surrounding vehicles to reduce the cycle of adverse impacts. Traffic safety can be improved by notifying the surrounding vehicles of aggressive driving behavior, and there is a possibility of applying various applications through V2X communication.
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