# Predictive Analysis of Vehicular Lane Changes: An Integrated LSTM Approach
## 1 Introduction  
In today's traffic environment, marked by its high density, lane changes have become crucial maneuvers at the microscopic level. For automated vehicles, anticipating the immediate driving state is essential for informed decision making. Accurate foresight into lane changes can significantly reduce crash risks. Moreover, it can also boost traffic efficiency and improve driving comfort by eliminating unnecessary maneuvers, thereby directly enhancing traffic safety. To enhance urban traffic safety and efficiency, this study aims to develop a precise and efficient model for classifying vehicle lane change maneuvers and predicting trajectories. This objective will be achieved through a comprehensive analysis of traffic conditions, an in-depth review of the relevant literature, and the application of advanced artificial intelligence modeling techniques, ultimately refining vehicle behavior prediction. Several scholars have approached lane change predictions using diverse methodologies, with each offering a unique perspective. [PERSON] (2019) [1] employed the affinity propagation clustering algorithm to design a lane-changing model, accounting for driver heterogeneity. Utilizing data from vehicle sensors, [PERSON] (2018) [2] proposed a lane change predictor based on the adaptive fuzzy neural network (AFNN) to forecast steering angles. [PERSON]. (2019) [3] presented a data-driven lane change (LC) model utilizing deep learningtechniques. The model leverages both a deep belief network (DBN) and long short-term memory (LSTM) neural networks to represent the LC process, which encompasses lane change decisions (LCDs) and lane change implementation (LCI). [PERSON] et al. (2019) [4] concentrated on forecasting lane changes by analyzing surrounding vehicles on highways, harnessing both the SVM and Bayesian filter methodologies. [PERSON] (2018) [5] introduced an LSTM model that integrated historical vehicle trajectories with the present state of neighboring vehicles to predict future trajectories. [PERSON] (2019) [6] presented a decision-making model for autonomous lane changes, drawing on the principles of benefit, safety, and tolerance. This model was crafted through an in-depth analysis of the factors influencing lane transitions in autonomous vehicles. [PERSON]. (2020) [7] introduced an end-to-end machine learning model designed to predict lane change maneuvers using a minimal set of features from unlabeled data. [PERSON] (2022) [8] developed a neural network architecture that integrates the attention mechanism, bidirectional long-short-term memory (BiLSTM), and a temporal convolutional neural network (TCN) to emulate and forecast joint behaviors. Several researchers have employed diverse methodologies to predict vehicle lane changes, viewing the problem from multiple angles and achieving noteworthy results. Additionally, researchers have attempted to improve the accuracy of vehicle lane-changing predictions through hybrid modeling approaches, as demonstrated by [PERSON] et al. (2019) [9; 10] and [PERSON] et al. (2022) [8]. These efforts have resulted in a notable enhancement in predictive accuracy. In addition to this, lane change prediction in autonomous driving is a burgeoning area of research. [PERSON] proposed a framework that focuses on feature learning for lane change prediction. The objective is to gain a deep and comprehensive understanding of lane change behaviors while achieving high performance based on the chosen features [11]. Similarly, [PERSON] introduced AutoPreview, a framework that allows consumers to preview an autopilot system's potential actions in real-world driving conditions before they are deployed [12]. Despite the notable accomplishments of the aforementioned research, a majority failed to segment data based on distinct behavioral patterns during lane changes. Additionally, their reliance on a singular prediction model restricts adaptability across varied scenarios.