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Reliable prediction of vehicle trajectories at signalized intersections is crucial to urban traffic management and autonomous driving systems. However, it presents unique challenges, due to the complex roadway layout at intersections, involvement of traffic signal controls, and interactions among different types of road users. To address these issues, we present in this paper a novel model called Knowledge-Informed Generative Adversarial Network (KI-GAN), which integrates both traffic signal information and multi-vehicle interactions to predict vehicle trajectories accurately. Additionally, we propose a specialized attention pooling method that accounts for vehicle orientation and proximity at intersections. Based on the SinD dataset, our KI-GAN model is able to achieve an Average Displacement Error (ADE) of 0.05 and a Final Displacement Error (FDE) of 0.12 for a 6-second observation and 6-second prediction cycle. When the prediction window is extended to 9 seconds, the ADE and FDE values are further reduced to 0.11 and 0.26, respectively. These results demonstrate the effectiveness of the proposed KI-GAN model in vehicle trajectory prediction under complex scenarios at signalized intersections, which represents a significant advancement in the target field. 

In this paper, we examine the problem of the Dilemma Zone (DZ) in depth, weaving together the various influences that span the environment, the ego-vehicle, and ultimately the characteristics of the driver. Driver behavior in dilemma zone situations is crucial, and more research is urgently needed in this area. The journey through various modeling approaches and data acquisition techniques sheds new light on driver behavior within the dilemma zone context. Our thorough examination of the current research landscape has revealed that several significant areas remain overlooked. As well as the dynamic impact of vehicles, vehicle interactions, and a strong tendency to over-rely on infrastructure information, there are also concerns about the lack of comprehensive evaluation tools. However, we do not see these gaps as stumbling blocks, but rather as steppingstones for future research opportunities. A more focused study of cooperative solutions is required considering the potential of personalized modeling, the untapped power of machine learning techniques, and the importance of personalized modeling. It is our hope that by embracing innovative approaches that can capture and simulate personalized behavioral data using “everything-in-the-loop” simulations, future research endeavors will be guided. To effectively mitigate the DZ problem, we also point out the research gaps and opportunities for further research in the DZ. 

Despite numerous studies on trajectory prediction, existing approaches often fail to adequately capture the multifaceted and individual nature of driving behavior. In recognition of this gap and based on DenseTNT, an end-to-end and goal-based trajectory prediction method, our study developed a new version of DenseTNT that incorporates personalized nodes within the graph neural network in VectorNet as context encoder. Throughout the neural network computations, these nodes represent individual driver labels, allowing a more granular understanding of diverse driving behaviors to be gained. Based on comparative analysis, our model has a 11.4% reduction in minADE when compared to baseline models that do not have personalized labels. 

Advanced Driver Assistance Systems (ADAS) are increasingly important in improving driving safety and comfort, with Adaptive Cruise Control (ACC) being one of the most widely used. However, pre-defined ACC settings may not always align with driver's preferences and habits, leading to discomfort and potential safety issues. Personalized ACC (P-ACC) has been proposed to address this problem, but most existing research uses historical driving data to imitate behaviors that conform to driver preferences, neglecting real-time driver feedback. To bridge this gap, we propose a cloud-vehicle collaborative P-ACC framework that incorporates driver feedback adaptation in real time. The framework is divided into offline and online parts. The offline component records the driver's naturalistic car-following trajectory and uses inverse reinforcement learning (IRL) to train the model on the cloud. In the online component, driver feedback is used to update the driving gap preference in real time. The model is then retrained on the cloud with driver's takeover trajectories, achieving incremental learning to better match driver's preference. Human-in-the-loop (HuiL) simulation experiments demonstrate that our proposed method significantly reduces driver intervention in automatic control systems by up to 62.8%. By incorporating real-time driver feedback, our approach enhances the comfort and safety of P-ACC, providing a personalized and adaptable driving experience. 

Adaptive Cruise Control (ACC) has become increasingly popular in modern vehicles, providing enhanced driving safety, comfort, and fuel efficiency. However, predefined ACC settings may not always align with a driver's preferences, leading to discomfort and possible safety hazards. To address this issue, Personalized ACC (P-ACC) has been studied by scholars. However, existing research mostly relies on historical driving data to imitate driver styles, which ignores real-time feedback from the driver. To overcome this limitation, we propose a cloud-vehicle collaborative P-ACC framework, which integrates real-time driver feedback adaptation. This framework consists of offline and online modules. The offline module records the driver's naturalistic car-following trajectory and uses inverse reinforcement learning (IRL) to train the model on the cloud. The online module utilizes the driver's real-time feedback to update the driving gap preference in real-time using Gaussian process regression (GPR). By retraining the model on the cloud with the driver's takeover trajectories, our approach achieves incremental learning to better match the driver's preference. In human-in-the-loop (HuiL) simulation experiments, the proposed framework results in a significant reduction of driver intervention in automatic control systems, up to 70.9%. 

Connected and automated vehicles (CAVs) are sup- posed to share the road with human-driven vehicles (HDVs) in a foreseeable future. Therefore, considering the mixed traffic envi- ronment is more pragmatic, as the well-planned operation of CAVs may be interrupted by HDVs. In the circumstance that human behaviors have significant impacts, CAVs need to under- stand HDV behaviors to make safe actions. In this study, we develop a driver digital twin (DDT) for the online prediction of personalized lane-change behavior, allowing CAVs to predict surrounding vehicles’ behaviors with the help of the digital twin technology. DDT is deployed on a vehicle-edge–cloud architec- ture, where the cloud server models the driver behavior for each HDV based on the historical naturalistic driving data, while the edge server processes the real-time data from each driver with his/her digital twin on the cloud to predict the personalized lane- change maneuver. The proposed system is first evaluated on a human-in-the-loop co-simulation platform, and then in a field implementation with three passenger vehicles driving along an on/off ramp segment connecting to the edge server and cloud through the 4G/LTE cellular network. The lane-change intention can be recognized in 6 s on average before the vehicle crosses the lane separation line, and the Mean Euclidean Distance between the predicted trajectory and GPS ground truth is 1.03 m within a 4-s prediction window. Compared to the general model, using a personalized model can improve prediction accuracy by 27.8%. The demonstration video of the proposed system can be watched at https://youtu.be/5cbsabgIOdM.