TY - GEN
T1 - Vehicle Turning Intention Prediction Based on Data-Driven Method with Roadside Radar and Vision Sensor
AU - He, Jyun Hong
AU - Chen, Yen Lin
AU - Chen, Xiu Zhi
AU - Chiang, Hsin Han
N1 - Funding Information:
ACKNOWLEDGMENT This study is conducted under the “New generation transport deep learning intelligent system” of the Institute for Information Industry which is subsidized by the Ministry of Economic Affairs of the Republic of China, and also financially sponsored by the Ministry of Science and Technology of Taiwan under the granted No. MOST 107-2221-E-003-024-MY3.
Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - The trajectory tracking and turning intention prediction of vehicles at intersections are a vital part of smart traffic safety. With the height limitation, traffic objects are often obscured by other vehicles that easily results in blind spots for the visual sensing system. This paper summarizes the author's practice of a roadside unit composed of monitors and radar sensors to track and predict behavioral intentions of traffic objects, and develop a stable system based on the fusion of radar and image sensing information to reduce the danger caused by the steering of other vehicles that are not predicted by the driving sight and the blind angle of the on-board sensor. The roadside unit is installed at the intersection to collect vehicle data on the road, such as position, speed, and direction. An artificial neural network based on LSTM-GAN is used to process data and predict vehicle turning intention. The research case shows that the proposed model has about 91% prediction accuracy.
AB - The trajectory tracking and turning intention prediction of vehicles at intersections are a vital part of smart traffic safety. With the height limitation, traffic objects are often obscured by other vehicles that easily results in blind spots for the visual sensing system. This paper summarizes the author's practice of a roadside unit composed of monitors and radar sensors to track and predict behavioral intentions of traffic objects, and develop a stable system based on the fusion of radar and image sensing information to reduce the danger caused by the steering of other vehicles that are not predicted by the driving sight and the blind angle of the on-board sensor. The roadside unit is installed at the intersection to collect vehicle data on the road, such as position, speed, and direction. An artificial neural network based on LSTM-GAN is used to process data and predict vehicle turning intention. The research case shows that the proposed model has about 91% prediction accuracy.
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U2 - 10.1109/ICCE-TW52618.2021.9602976
DO - 10.1109/ICCE-TW52618.2021.9602976
M3 - Conference contribution
AN - SCOPUS:85123059506
T3 - 2021 IEEE International Conference on Consumer Electronics-Taiwan, ICCE-TW 2021
BT - 2021 IEEE International Conference on Consumer Electronics-Taiwan, ICCE-TW 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 8th IEEE International Conference on Consumer Electronics-Taiwan, ICCE-TW 2021
Y2 - 15 September 2021 through 17 September 2021
ER -