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Article

A Hybrid Approach for Turning Intention Prediction Based on Time Series Forecasting and Deep Learning

by 1 and 1,2,*
1
School of Automobile, Chang’an University, Xi’an 710064, China
2
Key Lab of Vehicle Transportation Safety Technology, Ministry of Transport, Chang’an University, Xi’an 710064, China
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(17), 4887; https://doi.org/10.3390/s20174887
Received: 24 June 2020 / Revised: 25 August 2020 / Accepted: 26 August 2020 / Published: 28 August 2020
(This article belongs to the Special Issue Smartphone Sensors for Driver Behavior Monitoring Systems)
At an intersection with complex traffic flow, the early detection of the intention of drivers in surrounding vehicles can enable advanced driver assistance systems (ADAS) to warn the driver in advance or prompt its subsystems to assess the risk and intervene early. Although different drivers show various driving characteristics, the kinematic parameters of human-driven vehicles can be used as a predictor for predicting the driver’s intention within a short time. In this paper, we propose a new hybrid approach for vehicle behavior recognition at intersections based on time series prediction and deep learning networks. First, the lateral position, longitudinal position, speed, and acceleration of the vehicle are predicted using the online autoregressive integrated moving average (ARIMA) algorithm. Next, a variant of the long short-term memory network, called the bidirectional long short-term memory (Bi-LSTM) network, is used to detect the vehicle’s turning behavior using the predicted parameters, as well as the derived parameters, i.e., the lateral velocity, lateral acceleration, and heading angle. The validity of the proposed method is verified at real intersections using the public driving data of the next generation simulation (NGSIM) project. The results of the turning behavior detection show that the proposed hybrid approach exhibits significant improvement over a conventional algorithm; the average recognition rates are 94.2% and 93.5% at 2 s and 1 s, respectively, before initiating the turning maneuver. View Full-Text
Keywords: advanced driver assistance system; autonomous vehicle; driving intention prediction; online time series prediction; bidirectional long short-term memory network advanced driver assistance system; autonomous vehicle; driving intention prediction; online time series prediction; bidirectional long short-term memory network
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MDPI and ACS Style

Zhang, H.; Fu, R. A Hybrid Approach for Turning Intention Prediction Based on Time Series Forecasting and Deep Learning. Sensors 2020, 20, 4887. https://doi.org/10.3390/s20174887

AMA Style

Zhang H, Fu R. A Hybrid Approach for Turning Intention Prediction Based on Time Series Forecasting and Deep Learning. Sensors. 2020; 20(17):4887. https://doi.org/10.3390/s20174887

Chicago/Turabian Style

Zhang, Hailun, and Rui Fu. 2020. "A Hybrid Approach for Turning Intention Prediction Based on Time Series Forecasting and Deep Learning" Sensors 20, no. 17: 4887. https://doi.org/10.3390/s20174887

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