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The emergence of vehicle-to-everything (V2X) technology offers new insights into intersection management. This, however, has also presented new challenges, such as the need to understand and model the interactions of traffic participants, including their competition and cooperation behaviors. Game theory has been widely adopted to study rationally selfish or cooperative behaviors during interactions and has been applied to advanced intersection management. In this paper, we review the application of game theory to intersection management and sort out relevant studies under various levels of intelligence and connectivity. First, the problem of urban intersection management and its challenges are briefly introduced. The basic elements of game theory specifically for intersection applications are then summarized. Next, we present the game-theoretic models and solutions that have been applied to intersection management. Finally, the limitations and potential opportunities for subsequent studies within the game-theoretic application to intersection management are discussed. 

Freeway ramp merging is a challenging task for an individual vehicle (in particular a truck) and a critical aspect of traffic management that often leads to bottlenecks and accidents. While connected and automated vehicle (CAV) technology has yielded efficient merging strategies, most of them overlook the differentiation of vehicle types and assume uniform CAV presence. To address this gap, our study focuses on enhancing the merging efficiency of heavy-duty trucks in mixed traffic environments. We introduce a novel multi-human-in-the-loop (MHuiL) simulation framework, integrating the SUMO traffic simulator with two game engine-based driving simulators, enabling the investigation of interactions between human drivers in diverse traffic scenarios. Through a comprehensive case study analyzing eight scenarios, we assess the performance of a connectivity-based cooperative ramp merging system for heavy-duty trucks, considering safety, comfort, and fuel consumption. Our results demonstrate that guided trucks exhibit advantageous characteristics, including an enhanced safety margin with larger gaps by 23.2%, a decreased speed deviation by 30.4% facilitating smoother speed patterns, and a reduction in fuel consumption by 3.4%, when compared with non-guided trucks. This research offers valuable insights for the development of innovative approaches to improve truck merging efficiency, enhancing overall traffic flow and safety. 

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.