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In autonomous vehicles, recognizing different maneuvering behaviors of surrounding vehicles is crucial to reduce traffic risks and achieve safe path planning. Conventional vehicle behavior recognition methods adopt mainly supervised learning methods and assume that many sample labels are available. However, manual sample labeling is often time-consuming and laborious. Also, onboard sensors collecting surrounding vehicle movement information in data streams often cannot process information in real-time. To tackle these problems, we propose a semi-supervised approach using K-nearest neighbor- (K-NN)-based ensemble learning to classify the maneuvering behaviors of surrounding vehicles. The framework is divided into three parts: initial model training, online classification, and online model updating. First, k-means clustering of the maneuvering behavior is performed, cluster features are calculated, and a set of micro-clusters is obtained to establish the initial model. Second, the ensemble K-NN-based learning method is used to classify the incoming instances. Finally, the model is updated online using error-driven representative learning and an exponential decay function. Typical lane-changing and turning maneuvers are used as representatives to verify the performance of the proposed method. The data are provided by a next-generation simulation project. The results show that the proposed model achieves highest average recognition accuracy compared with other benchmark methods for the lane-changing and turning maneuvers shortly after the maneuver begins, even for a small sample size.
Hailun Zhang; Rui Fu. An Ensemble Learning-Online Semi-Supervised Approach for Vehicle Behavior Recognition. IEEE Transactions on Intelligent Transportation Systems 2021, PP, 1 -17.
AMA StyleHailun Zhang, Rui Fu. An Ensemble Learning-Online Semi-Supervised Approach for Vehicle Behavior Recognition. IEEE Transactions on Intelligent Transportation Systems. 2021; PP (99):1-17.
Chicago/Turabian StyleHailun Zhang; Rui Fu. 2021. "An Ensemble Learning-Online Semi-Supervised Approach for Vehicle Behavior Recognition." IEEE Transactions on Intelligent Transportation Systems PP, no. 99: 1-17.
The lack of visual information from the driver of the target vehicle makes it difficult to detect the intention of the target vehicle early before the start of the lane-change maneuver. Moreover, in current studies, the prerequisite for the successful application of intention detection models is to obtain a sufficient number of lane-change maneuver samples covering various scenarios and characteristics. Thus, a universal detector may not be optimal for lane-change intention detection in different scenarios in the real world. In this paper, we propose an intent detection method based on online transfer learning (OTL). First, a passive–aggressive (PA) algorithm is adopted to construct a lane-change source classifier with a large number of lane-change maneuvers as training samples. These samples contain the motion parameters of the vehicle before changing lanes, and the relative motion relationship between the target vehicle and the surrounding vehicles. Then, an OTL strategy that automatically and dynamically updates the weights is designed to detect the intention of the target vehicle to change lanes. The construction of the source classifier is supported by Next Generation Simulation (NGSIM) natural driving data, and the verification of the intention predictions exploits data collected from real natural driving experiments. The performance analysis results demonstrate that the proposed method can successfully detect lane-change intention 3 s before the start of the lane-change maneuver, with an accuracy of 93.0%.
Hailun Zhang; Rui Fu. Target vehicle lane-change intention detection: An approach based on online transfer learning. Computer Communications 2021, 172, 54 -63.
AMA StyleHailun Zhang, Rui Fu. Target vehicle lane-change intention detection: An approach based on online transfer learning. Computer Communications. 2021; 172 ():54-63.
Chicago/Turabian StyleHailun Zhang; Rui Fu. 2021. "Target vehicle lane-change intention detection: An approach based on online transfer learning." Computer Communications 172, no. : 54-63.
This study aimed to develop a coach state estimation and prediction system to enhance driving safety. Different from existing vehicle stability estimation studies, the authors propose a hybrid method to estimate and predict the state of a coach in real time. First, the vehicle sideslip angle and yaw rate are estimated by a three-degrees-of-freedom vehicle model combined with an extended Kalman filter (EKF) estimation algorithm. Then, a steering system is established that replaces the front-wheel angle with the steering wheel input torque. Next, a seven-degrees-of-freedom vehicle model analyses the effects of various driving influencing factors on the vehicle sideslip angle and the boundary of the stable region of the phase plane of the vehicle sideslip angle rate, and a boundary value parameter database is obtained. A back propagation neural network (BPNN) model is then established to obtain the boundary function parameter values under multifactor coupling conditions. Furthermore, an online prediction of the steering wheel input torque in a time series is done, and the prediction value is input to the steering system and neural network model. The effectiveness of the proposed method was evaluated via simulations based on MATLAB/Simulink and TruckSim software.
Rui Fu; Hailun Zhang; Yingshi Guo; Fei Yang; Yuping Lu. Real‐time estimation and prediction of lateral stability of coaches: a hybrid approach based on EKF, BPNN, and online autoregressive integrated moving average algorithm. IET Intelligent Transport Systems 2020, 14, 1892 -1902.
AMA StyleRui Fu, Hailun Zhang, Yingshi Guo, Fei Yang, Yuping Lu. Real‐time estimation and prediction of lateral stability of coaches: a hybrid approach based on EKF, BPNN, and online autoregressive integrated moving average algorithm. IET Intelligent Transport Systems. 2020; 14 (13):1892-1902.
Chicago/Turabian StyleRui Fu; Hailun Zhang; Yingshi Guo; Fei Yang; Yuping Lu. 2020. "Real‐time estimation and prediction of lateral stability of coaches: a hybrid approach based on EKF, BPNN, and online autoregressive integrated moving average algorithm." IET Intelligent Transport Systems 14, no. 13: 1892-1902.
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.
Hailun Zhang; Rui Fu. A Hybrid Approach for Turning Intention Prediction Based on Time Series Forecasting and Deep Learning. Sensors 2020, 20, 4887 .
AMA StyleHailun Zhang, Rui Fu. A Hybrid Approach for Turning Intention Prediction Based on Time Series Forecasting and Deep Learning. Sensors. 2020; 20 (17):4887.
Chicago/Turabian StyleHailun Zhang; Rui Fu. 2020. "A Hybrid Approach for Turning Intention Prediction Based on Time Series Forecasting and Deep Learning." Sensors 20, no. 17: 4887.