Abstract
An accurate prediction of future trajectories of surrounding vehicles can ensure safe and reasonable interaction between intelligent vehicles and other types of vehicles. Vehicle trajectories are not only constrained by a priori knowledge about road structure, traffic signs, and traffic rules but also affected by posterior knowledge about different driving styles of drivers. The existing prediction models cannot fully combine the prior and posterior knowledge in the driving scene and perform well only in a specific traffic scenario. This paper presents a long short-term memory (LSTM) neural network driven by knowledge. First, a driving knowledge base is constructed to describe the prior knowledge about a driving scenario. Then, the prediction reference baseline (PRB) based on driving knowledge base is determined by using the rule-based online reasoning system. Finally, the future trajectory of the target vehicle is predicted by an LSTM neural network based on the prediction reference baseline, while the predicted trajectory considers both posterior and prior knowledge without increasing the computation complexity. The experimental results show that the proposed trajectory prediction model can adapt to different driving scenarios and predict trajectories with high accuracy due to the unique combination of the prior and posterior knowledge in the driving scene.
Highlights
Since the 1980s, autonomous vehicles have been regarded as effective solutions to the problems of road safety, traffic congestion, and energy crisis
In a real urban traffic scenario, the vehicle’s driving trajectory is constrained by prior knowledge, such as that about the road structure, traffic signs, and traffic rules, and by uncertain posterior knowledge, including subjective driving intentions of the driver. e influence of driving knowledge on vehicle trajectory is shown in Figure 1, where it can be seen that when the road structure constraints are not considered, the predicted future trajectory, denoted as the red curve, is incorrect
Data Preparation. e vehicle positioning data in the next generation simulation (NGSIM) dataset are obtained by video analysis, so the recorded trajectory contains a lot of noise [26]. erefore, the vehicle kinematics model and the road geometric are used to filter the original data, which is expresses as 0 < κi < κmax, θmin < θ < θmax, θri > θrate, (23)
Summary
Since the 1980s, autonomous vehicles have been regarded as effective solutions to the problems of road safety, traffic congestion, and energy crisis. E physics-based models use vehicle kinematics and dynamics model to predict the future position of a target vehicle, and they include the constant turn rate and acceleration model [3], switching Kalman filters [4], and Monte Carlo simulation [5] These models ignore the prior and posterior knowledge about a driving scenario, such as road structure, traffic rules, and driver’s subjective intentions, which limits these models to short-term prediction (less than 1 s) [6]. Dai et al [23] proposed a spatiotemporal LSTM-based model, which considers the spatial interactions of the surrounding vehicles, but the constraints of other prior knowledge such as road structure, traffic rules, and driving experience are not considered.
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