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

Federated Learning for Connected and Automated Vehicles: A Survey of Existing Approaches and Challenges

Machine learning (ML) is widely used for key tasks in Connected and Automated Vehicles (CAV), including perception, planning, and control. However, its reliance on vehicular data for model training presents significant challenges related to in-vehicle user privacy and communication overhead generated by massive data volumes. Federated learning (FL) is a decentralized ML approach that enables multiple vehicles to collaboratively develop models, broadening learning from various driving environments, enhancing overall performance, and simultaneously securing local vehicle data privacy and security. This survey paper presents a review of the advancements made in the application of FL for CAV (FL4CAV). First, centralized and decentralized frameworks of FL are analyzed, highlighting their key characteristics and methodologies. Second, diverse data sources, models, and data security techniques relevant to FL in CAVs are reviewed, emphasizing their significance in ensuring privacy and confidentiality. Third, specific and important applications of FL are explored, providing insight into the base models and datasets employed for each application. Finally, existing challenges for FL4CAV are listed and potential directions for future work are discussed to further enhance the effectiveness and efficiency of FL in the context of CAV

Extreme Gradient Boosting (XGBoost) Model for Vehicle Trajectory Prediction in Connected and Autonomous Vehicle Environment

Connected and autonomous vehicles (CAVs) have the ability to receive information on their leading vehicles through multiple sensors and vehicle-to-vehicle (V2V) technology and then predict their future behaviour thus to improve roadway safety and mobility. This study presents an innovative algorithm for connected and autonomous vehicles to determine their trajectory considering surrounding vehicles. For the first time, the XGBoost model is developed to predict the acceleration rate that the object vehicle should take based on the current status of both the object vehicle and its leading vehicle. Next Generation Simulation (NGSIM) datasets are utilised for training the proposed model. The XGBoost model is compared with the Intelligent Driver Model (IDM), which is a prior state-of-the-art model. Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) are applied to evaluate the two models. The results show that the XGBoost model outperforms the IDM in terms of prediction errors. The analysis of the feature importance reveals that the longitudinal position has the greatest influence on vehicle trajectory prediction results

How machine learning informs ride-hailing services: A survey

In recent years, online ride-hailing services have emerged as an important component of urban transportation system, which not only provide significant ease for residents’ travel activities, but also shape new travel behavior and diversify urban mobility patterns. This study provides a thorough review of machine-learning-based methodologies for on-demand ride-hailing services. The importance of on-demand ride-hailing services in the spatio-temporal dynamics of urban traffic is first highlighted, with machine-learning-based macro-level ride-hailing research demonstrating its value in guiding the design, planning, operation, and control of urban intelligent transportation systems. Then, the research on travel behavior from the perspective of individual mobility patterns, including carpooling behavior and modal choice behavior, is summarized. In addition, existing studies on order matching and vehicle dispatching strategies, which are among the most important components of on-line ride-hailing systems, are collected and summarized. Finally, some of the critical challenges and opportunities in ride-hailing services are discussed

Human Motion Trajectory Prediction: A Survey

With growing numbers of intelligent autonomous systems in human environments, the ability of such systems to perceive, understand and anticipate human behavior becomes increasingly important. Specifically, predicting future positions of dynamic agents and planning considering such predictions are key tasks for self-driving vehicles, service robots and advanced surveillance systems. This paper provides a survey of human motion trajectory prediction. We review, analyze and structure a large selection of work from different communities and propose a taxonomy that categorizes existing methods based on the motion modeling approach and level of contextual information used. We provide an overview of the existing datasets and performance metrics. We discuss limitations of the state of the art and outline directions for further research.Comment: Submitted to the International Journal of Robotics Research (IJRR), 37 page

Selective Trajectory Memory Network andits application in Vehicle DestinationPrediction

학위논문 (석사)-- 서울대학교 대학원 : 공과대학 산업공학과, 2019. 2. Cho, Sungzoon.Predicting efficiently the final destinations of moving vehicles can be of significant usefulness for several applications. Many probabilistic methods have been developed to address it but often include heavy feature engineering and do not generalize well to new datasets. To face these limitations, Deep-Learning models present the advantage of automating processing steps and can therefore be easily adapted to new input data. De Brébisson et al. proposed clustering based deep-learning approaches to solve it in the specific case of the prediction of Taxis destinations with remarkable performances, alongside with a proposition of a novel architecture inspired by Memory-Networks used in Natural Language Processing, and requiring no preliminary clustering. A large room for improvement was however left for the latter approach : the necessity of a relevant selection function retrieving historical trajectories similar to partial trips to predict was indeed outlined by the authors. In this work we propose to use the Segment-Path distance, introduced by Besse et al. in former works on trajectory clustering, to come up with an improved architecture of this memory model. A review of several Memory Networks architecture and their applications in time-series prediction is provided to give an overview of the different structural alternatives existing for the design of our model architecture. Finally, our model is confronted to individual car data and we propose a personalized user-by-user prediction of destinations. We discuss the suitability and limits of the type of model in this specific problem and conclude that the promising obtained results are penalized by infrequent destinations cases inducing noise whose effect could be reduced by turning our approach into a classification problem.Abstract i Contents List of Tables vi List of Figures viii Chapter 1 Introduction 1 1.1 Motivations, background . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Problem Description : destination forecasting problem . . . . . . . . 2 1.2.1 General context . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.2.2 Specific problem tackled . . . . . . . . . . . . . . . . . . . . . 2 1.3 Existing models and methods . . . . . . . . . . . . . . . . . . . . . . 3 1.4 Research Motivation and Contributions . . . . . . . . . . . . . . . . 6 1.5 Organization of the Thesis . . . . . . . . . . . . . . . . . . . . . . . . 7 Chapter 2 Related works 8 2.1 Artificial neural network models for trajectory prediction . . . . . . 8 2.1.1 Encoding and clustering approach . . . . . . . . . . . . . . . 8 2.1.2 "Memory network" model for taxi trajectory prediction . . . 11 2.2 Memory networks and applications . . . . . . . . . . . . . . . . . . . 13 2.2.1 MemNN models . . . . . . . . . . . . . . . . . . . . . . . . . 14 2.2.2 End-to-end memory networks (MemN2N) . . . . . . . . . . . 16 2.2.3 Memory networks for multi-dimensional time-series forecasting (MTNnet) . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 2.3 Analogies and comparisons between the memory models introduced . 19 2.4 Distances measures for vehicle trajectories . . . . . . . . . . . . . . . 22 2.4.1 Segment-Path Distance (SPD) . . . . . . . . . . . . . . . . . 23 2.5 Personalized predictions on car manufacturer data . . . . . . . . . . 26 2.5.1 Problem approach and redefinition . . . . . . . . . . . . . . . 26 2.5.2 Method and model . . . . . . . . . . . . . . . . . . . . . . . . 27 Chapter 3 Proposed Model 28 3.1 Overall architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 3.2 Input . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 3.3 Memory storage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 3.4 Trajectory encoding . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 3.4.1 Encoding architecture . . . . . . . . . . . . . . . . . . . . . . 30 3.4.2 Metadata and embedding . . . . . . . . . . . . . . . . . . . . 31 3.4.3 Distinctions between encoders, weight-sharing . . . . . . . . . 31 3.5 Memory selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 3.5.1 Attention mechanism . . . . . . . . . . . . . . . . . . . . . . 32 3.5.2 Data used . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 3.6 Query-memory association . . . . . . . . . . . . . . . . . . . . . . . . 33 3.7 Final prediction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 Chapter 4 Experiments 35 4.1 Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 4.2 Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 4.2.1 Variability and predictability . . . . . . . . . . . . . . . . . . 36 4.2.2 Considered vehicles . . . . . . . . . . . . . . . . . . . . . . . . 37 4.3 Experimental settings . . . . . . . . . . . . . . . . . . . . . . . . . . 39 4.3.1 Training and testing set . . . . . . . . . . . . . . . . . . . . . 39 4.3.2 Test methodology and parameters . . . . . . . . . . . . . . . 40 4.3.3 Baseline model : simple encoding . . . . . . . . . . . . . . . . 42 4.4 Experimental results . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 4.4.1 General results . . . . . . . . . . . . . . . . . . . . . . . . . . 42 4.4.2 Factors of influence on models performances . . . . . . . . . . 45 4.4.3 Case studies : 5 example vehicles analysis . . . . . . . . . . . 49 4.4.4 Baseline model . . . . . . . . . . . . . . . . . . . . . . . . . . 51 4.5 Discussions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 Chapter 5 Conclusion 56 5.1 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 5.2 Future Directions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 Bibliography 58 감사의 글 62Maste