Abstract
This paper presents a probabilistic trajectory prediction of cut-in vehicles exploiting the information of interacting vehicles. First, a probability distribution of behavioral parameters, which represents the characteristics of lane-change motion, is obtained via Gaussian Process Regression (GPR). For this purpose, Gaussian Process (GP) models are trained using real-world trajectories of lane-changing vehicles and adjacent vehicles. Subsequently, the future states of the lane-change vehicle are probabilistically estimated using a path-following model, which introduces virtual measurements based on the information of behavioral parameters. The proposed predictor is applied to the motion planning and control of autonomous vehicles. A Model Predictive Control (MPC) is designed to achieve predictive maneuvering of autonomous vehicles against cut-in preceding vehicles. The proposed predictor has been evaluated in terms of its prediction accuracy. Also, the performance of the proposed predictor-based control has been validated via computer simulations and autonomous driving vehicle tests. Compared to conventional prediction methods, it is shown that the interaction-aware proposed predictor provides improved prediction of cut-in vehicles’ motion in multi-vehicle scenarios. Furthermore, the control results indicate that the proposed predictor helps the autonomous vehicle to reduce the control effort and improve ride quality for passengers in cut-in scenarios, while guaranteeing safety.
Highlights
Autonomous driving technology has been rapidly developed as the performance of electronic and mechanical devices and computer software improves [1]
4) Applicability of the proposed predictor-based proactive control is validated by vehicle tests based on autonomous driving in the real world
Where α and β are the coefficients of the affine mean function; σf is the standard deviation of the signal; and M is the positive-definite diagonal matrix that implicitly determines the relevance of each dimension of the input feature vector
Summary
Autonomous driving technology has been rapidly developed as the performance of electronic and mechanical devices and computer software improves [1]. Many studies have dealt with the control methodology of autonomous driving by reflecting the prediction results of surrounding vehicles [8]–[11]. These studies have reported that the application of the prediction algorithm to the control helps to improve safety and establish natural behavior. This study focuses on improving the trajectory-level prediction accuracy of lane-changing vehicles and performing suitable control against cut-in maneuvers of the side lane vehicle. The proposed predictor applies a machine learning-based approach to estimate the crucial parameters of lane-change motion. The main contributions of this work are summarized as follows: 1) Lane-change motion of cut-in vehicles is predicted with consideration of the interaction among surrounding vehicles. 4) Applicability of the proposed predictor-based proactive control is validated by vehicle tests based on autonomous driving in the real world
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