Abstract
The purpose of this study is to develop an autonomous lane change control system that adapts to variable surrounding conditions, to ensure vehicle safety and traffic flow stability. In this paper, we propose decision-making and control procedures for realizing autonomous lane changing; to this end, we consider not only behaviors for changing lanes but also those involved in approaching the lane changing state, with a focus on the controller design. A decoupled control structure and longitudinal trajectory-free control approach are suggested. We design a novel inter-vehicle spacing policy and a 3DOF lateral error vehicle dynamics model. To verify the effectiveness of our system, simulation experiments are performed for 12 scenarios, and system assessments are conducted based on four evaluation perspectives. The results confirm that our system can safely control the vehicle amidst various surrounding vehicle conditions and can also ensure vehicle motion stability. Furthermore, we solve the existing dynamic instability problem of lateral control, which arises through longitudinal acceleration variability. Another significant advantage of this model is that the controlled vehicle does not interfere with the target-lane traffic flow and smoothly synchronizes with the flow during lane changing.
Highlights
According to the National Highway Transportation Safety Administration, approximately 90% of traffic accidents are caused by human error; among these, more than 10% of severe accidents occur during lane changing [1], [2]
The remainder of this paper is organized as follows: in Section II, the literature review is presented; in Section III, we describe the procedure and control strategies used for autonomous lane changing; in Section IV, the sliding mode control (SMC) and adaptive model predictive control (AMPC)-based controllers are designed; in Section V, we evaluate our system for various types of surrounding condition scenarios; and in Section VI, we summarize the conclusions of the study
We focus on vehicle control for the Highway Driving Assist (HDA), LCSR, and LC modes in steady state traffic flows, and we simplified the system using the following assumptions: Assumption 1) An algorithm is required for searching and selecting an optimal acceptable lane change space (ALCS) from among several adjacent empty inter-vehicle spaces in the target-lane. We solve this problem by using deep learning models and training with numerous empirical lane changing data; this is not considered in this paper because we focus more on designing a controller that can reach the ALCS robustly
Summary
According to the National Highway Transportation Safety Administration, approximately 90% of traffic accidents are caused by human error; among these, more than 10% of severe accidents occur during lane changing [1], [2]. J. Kim et al.: Decoupled Longitudinal and Lateral Vehicle Control Based Autonomous Lane Change System Adaptable flow breakdowns under heavy traffic conditions [4]. The purpose of this study is to develop an autonomous lane change control system that adapts to the variable surrounding conditions, to ensure vehicle safety and traffic flow stability. The remainder of this paper is organized as follows: in Section II, the literature review is presented; in Section III, we describe the procedure and control strategies used for autonomous lane changing; in Section IV, the SMC and AMPC-based controllers are designed; in Section V, we evaluate our system for various types of surrounding condition scenarios; and, we summarize the conclusions of the study The remainder of this paper is organized as follows: in Section II, the literature review is presented; in Section III, we describe the procedure and control strategies used for autonomous lane changing; in Section IV, the SMC and AMPC-based controllers are designed; in Section V, we evaluate our system for various types of surrounding condition scenarios; and in Section VI, we summarize the conclusions of the study
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