Machine Learning-Based Vehicle Trajectory Prediction Using V2V Communications and On-Board Sensors

Predicting the trajectories of surrounding vehicles is important to avoid or mitigate collision with traffic participants. However, due to limited past information and the uncertainty in future driving maneuvers, trajectory prediction is a challenging task. Recently, trajectory prediction models usi...

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Bibliographic Details
Published in:Electronics (Basel) 2021-02, Vol.10 (4), p.420
Main Authors: Choi, Dongho, Yim, Janghyuk, Baek, Minjin, Lee, Sangsun
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Autonomous vehicles
Cameras
Coders
Collision avoidance
Communication
Computer architecture
Driving
Electronic devices
Encoders-Decoders
Global positioning systems
GPS
Kinematics
Machine learning
Maneuvers
Neural networks
Occupancy
Prediction models
Predictions
Sensors
Test vehicles
Velocity
Wireless communications
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Summary:Predicting the trajectories of surrounding vehicles is important to avoid or mitigate collision with traffic participants. However, due to limited past information and the uncertainty in future driving maneuvers, trajectory prediction is a challenging task. Recently, trajectory prediction models using machine learning algorithms have been addressed solve to this problem. In this paper, we present a trajectory prediction method based on the random forest (RF) algorithm and the long short term memory (LSTM) encoder-decoder architecture. An occupancy grid map is first defined for the region surrounding the target vehicle, and then the row and the column that will be occupied by the target vehicle at future time steps are determined using the RF algorithm and the LSTM encoder-decoder architecture, respectively. For the collection of training data, the test vehicle was equipped with a camera and LIDAR sensors along with vehicular wireless communication devices, and the experiments were conducted under various driving scenarios. The vehicle test results demonstrate that the proposed method provides more robust trajectory prediction compared with existing trajectory prediction methods.
ISSN:2079-9292
2079-9292
DOI:10.3390/electronics10040420