Abstract
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.
Highlights
With the widespread implementation of advanced driver assistance systems (ADAS) and the rapid development of artificial intelligence, autonomous driving has become a reality [1,2,3,4]
Many scholars have begun to study the characteristics of safe driving of mixed traffic and its impact on drivers [5,6,7,8,9,10,11]
We we propose propose aa method method for for predicting predicting and and recognizing recognizing vehicle turning behavior at intersections using a combination of time series prediction and deep learning networks, which can predict the intention of the vehicle before the turning maneuver is is initiated
Summary
With the widespread implementation of advanced driver assistance systems (ADAS) and the rapid development of artificial intelligence, autonomous driving has become a reality [1,2,3,4]. This development means that in the future, a mixed environment will be inevitable. Many scholars have begun to study the characteristics of safe driving of mixed traffic and its impact on drivers [5,6,7,8,9,10,11] It is well-known that intersections represent bottlenecks in urban traffic, reducing traffic efficiency. Due to the complex characteristics of intersections, the accident rate at or near this location is relatively high [12,13]
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