Interaction-Aware Personalized Vehicle Trajectory Prediction Using
  Temporal Graph Neural Networks
Accurate prediction of vehicle trajectories is vital for advanced driver
assistance systems and autonomous vehicles. Existing methods mainly rely on
generic trajectory predictions derived from large datasets, overlooking the
personalized driving patterns of individual drivers. To address this gap, we
propose an approach for interaction-aware personalized vehicle trajectory
prediction that incorporates temporal graph neural networks. Our method
utilizes Graph Convolution Networks (GCN) and Long Short-Term Memory (LSTM) to
model the spatio-temporal interactions between target vehicles and their
surrounding traffic. To personalize the predictions, we establish a pipeline
that leverages transfer learning: the model is initially pre-trained on a
large-scale trajectory dataset and then fine-tuned for each driver using their
specific driving data. We employ human-in-the-loop simulation to collect
personalized naturalistic driving trajectories and corresponding surrounding
vehicle trajectories. Experimental results demonstrate the superior performance
of our personalized GCN-LSTM model, particularly for longer prediction
horizons, compared to its generic counterpart. Moreover, the personalized model
outperforms individual models created without pre-training, emphasizing the
significance of pre-training on a large dataset to avoid overfitting. By
incorporating personalization, our approach enhances trajectory prediction
accuracy.
Amr Abdelraouf, Rohit Gupta, Kyungtae Han
# Interaction-Aware Personalized Vehicle Trajectory Prediction Using Temporal Graph Neural Networks

###### Abstract

Accurate prediction of vehicle trajectories is vital for advanced driver assistance systems and autonomous vehicles. Existing methods mainly rely on generic trajectory predictions derived from large datasets, overlooking the personalized driving patterns of individual drivers. To address this gap, we propose an approach for interaction-aware personalized vehicle trajectory prediction that incorporates temporal graph neural networks. Our method utilizes Graph Convolution Networks (GCN) and Long Short-Term Memory (LSTM) to model the spatio-temporal interactions between target vehicles and their surrounding traffic. To personalize the predictions, we establish a pipeline that leverages transfer learning: the model is initially pre-trained on a large-scale trajectory dataset and then fine-tuned for each driver using their specific driving data. We employ human-in-the-loop simulation to collect personalized naturalistic driving trajectories and corresponding surrounding vehicle trajectories. Experimental results demonstrate the superior performance of our personalized GCN-LSTM model, particularly for longer prediction horizons, compared to its generic counterpart. Moreover, the personalized model outperforms individual models created without pre-training, emphasizing the significance of pre-training on a large dataset to avoid overfitting. By incorporating personalization, our approach enhances trajectory prediction accuracy.

## I Introduction

The advent of vehicle-generated big data has sparked considerable interest in data-driven personalized advanced driver assistance systems (ADAS) [1]. By leveraging the wealth of personalized driving patterns and insights expressed by drivers, personalization has the potential to enhance the performance of ADAS systems greatly. This, in turn, leads to improved driving experiences, increased driver acceptance, and greater utilization of ADAS functionalities. In recent years, many personalized ADAS applications have been proposed, including Adaptive Cruise Control [2], Forward Collision Warning [3], Lane Keeping Assistance [4], among many others. Furthermore, personalization extends to battery electric vehicle (BEV) ADAS applications. Personalized range estimation, in particular, holds significant promise in mitigating range anxiety, as it enables more accurate predictions of available driving range for individual drivers' given their unique driving styles [5].

Vehicle trajectory prediction is a foundational component in many ADAS applications and autonomous vehicle (AV) systems [6]. It plays a significant role in various safety-critical ADAS applications, such as collision warning [7], automated braking [8], [9], and lane change prediction [10]. The accuracy of trajectory prediction is crucial for enhancing the effectiveness of these safety-critical applications [11], enabling them to provide timely warnings while minimizing false positives. Moreover, in the context of autonomous vehicles, trajectory prediction plays a vital role in ensuring safe motion planning and overall system safety [12]. Anticipating and responding to dynamic traffic situations helps to reduce the risk of accidents and instills trust in AV systems. Additionally, trajectory prediction can significantly contribute to Vehicle-to-Vehicle (V2V) communication, particularly in intent-sharing [13] and negotiation applications [14]. By facilitating vehicular communication of their intentions to others, trajectory prediction for intent-sharing promotes cooperative behavior in mixed traffic, improves traffic flow, and enhances overall safety.

Recent advancements in trajectory prediction have emphasized the significance of vehicle interactions [12]. Interaction-aware trajectory prediction considers the spatio-temporal relationship between the target vehicle and its surrounding neighbors. In the recent past, some of the most effective approaches for short-term trajectory prediction combined graph neural networks (GNNs) to model the spatial domain and recurrent neural networks (RNNs) to capture the temporal domain for vehicle interactions [15], [16].

Despite the substantial body of research dedicated to vehicle trajectory prediction, a notable research gap persists in personalized vehicle trajectory prediction. Specifically, there is limited exploration of interaction-aware personalized trajectory prediction methods [17]. A major challenge stems from the lack of individual driver trajectory data which captures the driver's trajectory in addition to the driver's surrounding vehicle trajectories for an extended period of time.

This paper presents a novel approach for generating personalized interaction-aware vehicle trajectory predictions. Our method builds upon state-of-the-art techniques and combines graph convolution networks (GCN) with Long Short-Term Memory (LSTM) models to effectively capture the dynamics of vehicle interactions. To obtain individual driver trajectories which can support interaction-aware modeling, we utilized human-in-the-loop simulation using the CARLA driving simulator [18]. The proposed personalization frame