Earticle

초록

영어

In autonomous driving, environmental perception and decision-making are technologies that acquire signals based on various sensors and generate information that enables obstacle avoidance, emergency stops, and path planning. Such environmental perception technologies are limited in that they depend on expensive sensors such as LIDAR and RADAR, so research on environmental perception technologies using only cameras is actively being conducted. Studies predicting traffic accidents based on dashcam footage have also been performed as part of such research. This is challenging because only limited forward-looking footage can be acquired, and the surrounding environment is dynamic and changes quickly, making analysis difficult. Existing research has focused on learning the spatialtemporal feature representation to solve these problems. This paper extracts the trajectories of the ego-vehicle and surrounding vehicles using Visual SLAM and Multiple Object Tracking algorithms and uses them as inputs to the graph convolutional neural network(GCN) to learn the spatialtemporal feature representation. In addition, the features learned through the GCN are used as inputs to a bayesian neural network(BNN) to predict the probability of accidents, and its ability to predict accidents in advance has been verified by comparison with existing studies.

목차

Abstract
I. INTRODUCTION
II. RELATED WORK
A. Visual SLAM
B. Multi-Object Tracking
III. PROPOSED METHOD
A. Camera calibration
B. Ego-Motion estimation
C. Detection and Tracking of Surrounding Vehicles
D. Global Frame Trajectory
IV. EXPERIMENTS
A. USED DATASET
B. EXPERIMENTAL RESULTS
V. CONCLUSION
ACKNOWLEDGMENT
REFERENCES

저자

• Jikyu Park [ Department of Electrical and Computer Engineering Inha University Incheon, South Korea ]
• Jongho Won [ Department of Electrical and Computer Engineering Inha University Incheon, South Korea ]
• Deok-Hwan Kim [ Department of Electrical and Computer Engineering Inha University Incheon, South Korea ] Corresponding Author

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