Human drivers’ control quality in the first seconds after a handover is critical to shared-driving safety; potentially unsafe steering or pedal inputs therefore require detection and correction by the automated vehicle’s safety-fallback system. Yet performance in this window is vulnerable because cognitive states fluctuate rapidly, causing purely rationality-driven, cognition-unaware models to miss early control dynamics. We present an interpretable driver model grounded in bounded rationality with online adaptation that predicts early-stage control quality. We encode boundedness by embedding cognitive constraints in reinforcement learning and adapt latent cognitive parameters in real time via particle filtering from observations of driver actions. In a vehicle-in-the-loop study (n=41), we evaluated predictive performance and physiological validity. The adaptive model not only anticipated hazardous takeovers with higher coverage and longer lead times than non-adaptive baselines but also demonstrated strong alignment between inferred cognitive parameters and real-time eye-tracking metrics. These results confirm that the model captures genuine fluctuations in driver risk perception, enabling timely and cognitively grounded assistance.

During the ongoing development and proliferation of autonomous driving, human-driven vehicles (HDVs) and connected automated vehicles (CAVs) will coexist in mixed traffic environments for the foreseeable future. However, current autonomous driving systems often face challenges in ensuring optimal safety and efficiency, particularly in complex conflict scenarios. To address these shortcomings and improve cooperation in mixed traffic environments, this paper presents a game theoretic decision-making method. The proposed framework accounts for both CAV-CAV cooperation and CAV-HDV interaction in mixed traffic at un-signalized intersections. It introduces a parameter updating mechanism based on twin games to dynamically adjust HDVs’ parameters to better predict and respond to variable human driving behaviors. To validate the effectiveness of the proposed cooperative driving framework, the comparative analysis of its safety and efficiency with other established methods is conducted. The results demonstrate that our method successfully ensures both safety and efficiency in mixed traffic environments. Compared with reinforcement learning approaches such as IPPO, it achieves a 35–55% improvement in success rate while maintaining decision stability and traffic efficiency. In contrast to methods that enforce strict safety guarantees, our approach improves the average vehicle speed by 0.1–0.6 m/s and the average CAV speed by 0.7–1.7 m/s, without compromising safety. Additionally, several validation experiments are conducted using a hardware-in-the-loop and human-in-the-loop experimental platform, confirming the practical applicability of the method.

High-speed autonomous truck platooning with short inter-vehicle spacing is essential for improving freight efficiency. However, in mixed traffic environments, safety and stability are strongly affected by sudden human-driven disturbances and stochastic communication delays. This study proposes a two-layer Koopman-based framework that combines open-loop and closed-loop data-driven predictors with delay-aware compensation and safety assurance for distributed platoon control. In the first layer, an open-loop Koopman predictor is integrated with a robust control barrier function based safety filter formulated as a constrained quadratic optimization. This formulation ensures that nonlinear vehicle dynamics and model uncertainty are handled with explicit safety margins. In the second layer, a closed-loop Koopman predictor performs forward state estimation to compensate delayed predecessor information and reduce delay-induced observation bias in the control loop. The method is evaluated in three representative scenario classes, with random communication delays up to 500 ms and platoon sizes from 3 to 10 vehicles. Under uniformly distributed 300 ms delay, the proposed strategy limits the maximum absolute spacing error to 0.8 m (versus 5.5 m for baseline MPC and 1.94 m for robustH∞), while preserving string-stable car-following behavior. In emergency sudden-braking scenarios, it increases the minimum following distance to 5.9 m, compared with 1.3 m without a safety filter and 2.4 m with a linear control barrier function, demonstrating effective safety correction under critical disturbances.

Autonomous Vehicles (AVs) have entered the stage of commercialization, yet their performance in interactive scenarios remains unsatisfactory due to challenges such as decision interpretability, human driver (HV) heterogeneity, and scenario diversity. Recent advances in Large Language Models (LLMs) provide a promising avenue to enhance AV interaction capabilities, but their high computational demand hinders practical deployment. To address these challenges, this paper introduces a parallel Actor–Reasoner framework designed to enable explicit and real-time bidirectional AV-HV interactions. First, the Reasoner employs a localized LLM with CoT reasoning and human instructions to progressively infer HV intent, style, AV action, and eHMI displays during the training stage. During testing, it continues to infer he above information except AV action. The Actor, in turn, is constructed as an interaction memory through the Reasoner’s interactions with heterogeneous simulated HVs across diverse scenarios, where the memory partition and two-layer retrieval modules are employed in the construction process. During testing, the Actor is used to retrieve feasible actions for the AV. Ablation studies across multiple scenarios demonstrate that the proposed modules improve interaction success rates by an average of 15% and 12%, respectively. Moreover, comparison studies in multi-vehicle scenarios further show that the proposed Actor–Reasoner framework achieves superior safety while simultaneously improving efficiency. Finally, with the integration of external Human–Machine Interface (eHMI) information derived from the Reasoner’s reasoning and feasible actions retrieved from the Actor, the framework is validated in real-world field interactions. Our code is available athttps://github.com/FanGShiYuu/Actor-Reasoner

With the development of autonomous driving, understanding the behavior of traffic participants has become increasingly important. Vehicle trajectories contain rich information, however, they are often contaminated with noise in real-world environments. While existing trajectory processing methods may incorporate physical models to some extent, they typically struggle to enforce strict adherence to physical constraints. As a result, the processed trajectories may become distorted, making it difficult to accurately infer the driver’s control actions. In this paper, the vehicle kinematic model is embedded into a learnable neural network. By leveraging backpropagation through the physical model, the network can be trained without the need for explicit labels of driving actions. The design of intermediate network layers ensures that the output trajectories strictly satisfy physical constraints and enables robust inference of the driver’s inputs. Cross-dataset experiments demonstrate that the proposed method significantly outperforms direct computation approaches: errors in acceleration and deceleration estimation are reduced by approximately 84%, while steering action errors are reduced by approximately 43%. Real-world vehicle data further validate the trajectory reconstruction method, achieving a 5% reduction in prediction error when applied to fixed-point trajectory prediction models compared to unprocessed trajectories.

Autonomous driving has entered the testing phase, but due to the limited decision-making capabilities of individual vehicle algorithms, safety and efficiency issues have become more apparent in complex scenarios. With the advancement of connected communication technologies, autonomous vehicles equipped with connectivity can leverage vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications, offering a potential solution to the decision-making challenges from individual vehicle's perspective. We propose a multi-level vehicle-infrastructure cooperative decision-making framework for complex conflict scenarios at unsignalized intersections. First, based on vehicle states, we define a method for quantifying vehicle impacts and their propagation relationships, using accumulated impact to group vehicles through motif-based graph clustering. Next, within and between vehicle groups, a pass order negotiation process based on Large Language Models (LLM) is employed to determine the vehicle passage order, resulting in planned vehicle actions. Simulation results from ablation experiments show that our approach reduces negotiation complexity and ensures safer, more efficient vehicle passage at intersections, aligning with natural decision-making logic.

A lack of understanding of interactions and the inability to effectively resolve conflicts continue to impede the progress of Connected Autonomous Vehicles (CAVs) in their interactions with Human-Driven Vehicles (HDVs). To address this challenge, we propose the Recognize then Resolve (RtR) framework. First, a Bilateral Intention Progression Graph (BIPG) is constructed based on CAV-HDV interaction data to model the evolution of interactions and identify potential HDV intentions. Three typical interaction breakdown scenarios are then categorized, and key moments are defined for triggering cooperative conflict resolution. On this basis, a constrained Monte Carlo Tree Search (MCTS) algorithm is introduced to determine the optimal passage order while accommodating HDV intentions. Experimental results demonstrate that the proposed RtR framework outperforms other cooperative approaches in terms of safety and efficiency across various penetration rates, achieving results close to consistent cooperation while significantly reducing computational resources. Our code and data are available at: https://github.com/FanGShiYuu/RtR-Recognize-then-Resolve/.

Autonomous vehicles (AVs) often display overly conservative behavior in mixed traffic environments with human-driven vehicles (HVs). This is primarily due to the complexity of interactions, diverse conflict points, and the heterogeneity of human driving behaviors. These factors pose significant challenges to AVs' decision-making. To effectively solve the problem, this paper proposes a virtual game-theoretic framework based on the interaction influence weight quantification for multi-vehicle interaction. The upper layer of the framework employs an attention mechanism based on Graph Convolutional Network (GCN) and Long Short-Term Memory (LSTM) networks to capture the interaction effects between vehicles. The lower layer estimates the Social Value Orientation (SVO) of each interactive vehicle according to the historical trajectories to identify heterogeneity and models the interaction process as a virtual game among vehicles to solve the optimal decisions. Finally, simulation experiments show that the proposed algorithm outperforms existing benchmark decision-making algorithms in terms of safety and efficiency, showing good robustness in interactions with surrounding vehicles of different driving styles.

With the advancement of autonomous driving technology, trajectory prediction has become a critical task for ensuring traffic safety and intelligent decision-making. Existing motion forecasting models suffer from HD (High-Definition) map dependency, leading to high costs and poor adaptability. Furthermore, their accuracy sharply declines when maps are unavailable, motivating research into map-free alternatives. However, map-free models typically exhibit lower accuracy. To address this issue, we propose a universal enhancement framework that employs trajectory-map contrastive learning, utilizing a trajectory-to-map encoder to extract implicit map representations from raw trajectories, thereby improving performance. Extensive experiments on the Argoverse dataset demonstrate that, after incorporating our trajectory-to-map encoder into map-free models, the average minADE and minFDE are improved by 2.7% and 3.5%, respectively. These results underscore our method’s robustness and generalizability in enhancing map-free models, confirming the efficacy of implicit map representation learning and offering a promising solution for HD-map-free autonomous driving in dynamic open-road environments.

Accurate and efficient prediction of the future trajectories of surrounding vehicles is of utmost importance in motion planning for autonomous driving. The ability to predict longer-term trajectories provides valuable information for effective motion planning. Numerous studies have contributed to the prediction of long-term vehicle trajectories. However, it is important to note that longer-term predictions can potentially lead to a trade-off between accuracy and computational complexity. In this work, we propose a structural Informer method, which can achieve accurate and efficient long-term trajectory prediction of the target vehicle. Specifically, the proposed method considers not only the temporal and spatial features of the interaction vehicle trajectory, but also the impact of vehicle state changes on the trajectory. To reduce computational redundancy and complexity while improving memory usage and prediction accuracy, theProbSparseself-attention mechanisms and attention distillation operations are employed. The method is validated and evaluated using the NGSIM dataset, and the results demonstrate that the proposed structural Informer achieves satisfactory accuracy and time cost in long-term prediction of the TV compared with state-of-the art methodsNote to Practitioners—The motivation of this research is to address the impact of future trajectories of surrounding vehicles on the motion planning of autonomous vehicles. The method proposed applies advanced deep learning methods, and its strongpoint is that the proposed network can achieve higher efficiency and accuracy of trajectory prediction compared with state-of-the art methods. The specific implementation method is to use structured embedding methods and networks to extract more valuable features of the target vehicle, such as spatiotemporal features and vehicle state features. The novel attention mechanism is designed to solve the problem of exponential growth in computational complexity of traditional attention mechanisms in long-term prediction. The advancement of the method proposed confirmed by verification on naturalistic driving dataset.

Given the complex nature of interaction under ambiguous right-of-way scenarios, the interactions between Autonomous Vehicles (AVs) and Human-driven Vehicles (HVs) present considerable challenges to the safety and efficiency of the traffic system. Existing AVs struggle to comprehend and apply common HV social norms, especially the proactive behavior exhibited by adept human drivers in ambiguous right-of-way scenarios. In this study, we propose a novel framework to leverage expert priors for proactive-aware decision-making in ambiguous right-of-way, merging Reinforcement Learning (RL) with parameterized modeling. Building upon unprotected-turning interactions from real-world driving datasets, we select typical cases under ambiguous right-of-way as human-expert priors, which are utilized to guide the learning of the RL agent. Then, a Hidden Markov Model (HMM), which is governed by interpretable parameters derived from expert priors, introduces human decision updating mechanism into AV strategy. Experimenting with typical driving tasks, our approach achieves balanced safety and efficiency in tackling ambiguities of right-of-way, with superior decision-making performance via the guidance of expert priors when compared with established baselines. Furthermore, the results indicate that the proposed method enables AVs to accelerate the convergence during the interaction by consistent probing and decision updates.

The development of autonomous vehicles has shown great potential to enhance the efficiency and safety of transportation systems. However, the decision-making issue in complex human-machine mixed traffic scenarios, such as unsignalized intersections, remains a challenge for autonomous vehicles. While reinforcement learning (RL) has been used to solve complex decision-making problems, existing RL methods still have limitations in dealing with cooperative decision-making of multiple connected autonomous vehicles (CAVs), ensuring safety during exploration, and simulating realistic human driver behaviors. In this paper, a novel and efficient algorithm, Multi-Agent Game-prior Attention Deep Deterministic Policy Gradient (MA-GA-DDPG), is proposed to address these limitations. Our proposed algorithm formulates the decision-making problem of CAVs at unsignalized intersections as a decentralized multi-agent reinforcement learning problem and incorporates an attention mechanism to capture interaction dependencies between ego CAV and other agents. The attention weights between the ego vehicle and other agents are then used to screen interaction objects and obtain prior hierarchical game relations, based on which a safety inspector module is designed to improve the traffic safety. Furthermore, both simulation and hardware-in-the-loop experiments were conducted, demonstrating that our method outperforms other baseline approaches in terms of driving safety, efficiency, and comfort.

Interacting with the surrounding road users is crucial for autonomous vehicles (AV). However, the inherent multimodality and uncertainties associated with traffic participants (TP) pose challenges in AVs’ prediction and decision-making (PnD). A primary challenge is adapting predictors trained on static offline datasets to the dynamic, diverse data streams encountered in reality. Secondly, utilizing one single forecast trajectory with the highest probability for decision-making contains potential risks as it neglects that even a small probability represents a subset of TP behaviors. Based on the existing prediction backbone, we propose an online learning approach incorporating pseudo-labels inferred from partial feedback as compensation for conventional methodologies, considering both the commonsense and personalization facets of driving. Drawing inspiration from the second law of thermodynamics, we propose to minimize microscopic traffic entropy as an additional objective in decision-making. This objective aims to reduce the chaos of traffic scenes, thus achieving more predictable future interactions and, conversely, making future decisions easier. Through real-time human-in-the-loop experiments, we quantifiably and comparably reveal that adopting one single trajectory without online learning in PnD is risky. However, this reliability is verified to be significantly improved by our proposed techniques, and the efficacy is further analyzed in a subsequent qualitative study. A static experiment transferring the prediction algorithm trained exclusively on Argoverse 2 to datasets including NGSIM, HighD, RounD, and NuScenes is also conducted, demonstrating that the proposed correction can effectively mitigate the gap between the datasets and real-world scenarios.

In this paper, a human-like driving system is designed for autonomous vehicles (AVs), which aims to make AVs better integrate into the human transportation systems and mitigate misunderstanding and conflicts when interacting with human-driven vehicles. Based on the analysis of the real world INTERACTION dataset, a driving aggressiveness estimation model is established with the fuzzy inference approach. In the human-like lane-change decision-making algorithm, the cost function is designed comprehensively considering driving safety and travel efficiency. Based on the cost function with multi-constraint, a dynamic game algorithm is developed to model the interactions and decision making between AV and human-driven vehicles. Additionally, to guarantee the safety during lane-change of AVs, an artificial potential field model is built for collision risk assessment. Further, a human-like driving model is designed, which integrates the brain emotional learning circuit model (BELCM) with a two-point preview model. Finally, the proposed algorithm is evaluated through human-in-the-loop experiments, and the results demonstrated the feasibility and effectiveness of the proposed method.

Using drones to carry out commercial parcel delivery can significantly promote the transformation and upgrading of the logistics industry thanks to the saving of human labor source, which is becoming a new component of intelligent transportation systems. However, the flight distance of drones is often constrained due to the limited battery capacity. To address this challenge, this paper designs a multi-drones-assisted commercial parcel delivery system, which supports long-distance delivery by a generalized service network (GSN). Each node of the GSN is equipped with charging piles to provide a charging service for drones. Given the limited number of charging piles at each node and the limited battery capacity of a drone, to ensure the efficient operation of the system, the flight planning problem of drones is converted into a large-scale optimization problem by a priority-based encoding mechanism. To solve this problem, an enhanced backtracking search algorithm (EBSA) is reported, which is inspired by the characteristics of the considered flight planning problem and the weak ability of the backtracking search algorithm to escape from a local optimum. The core components of EBSA are the designed comprehensive learning mechanism and local escape operator. Experimental results prove the validity of the improved strategies and the excellent performance of EBSA on the considered flight planning problem.

To address the coordination issue of connected automated vehicles (CAVs) at urban scenarios, a game-theoretic decision-making framework is proposed that can advance social benefits, including the traffic system efficiency and safety, as well as the benefits of individual users. Under the proposed decision-making framework, in this work, a representative urban driving scenario, i.e. the unsignalized intersection, is investigated. Once the vehicle enters the focused zone, it will interact with other CAVs and make collaborative decisions. To evaluate the safety risk of surrounding vehicles and reduce the complexity of the decision-making algorithm, the driving risk assessment algorithm is designed with a Gaussian potential field approach. The decision-making cost function is constructed by considering the driving safety and passing efficiency of CAVs. Additionally, decision-making constraints are designed and include safety, comfort, efficiency, control and stability. Based on the cost function and constraints, the fuzzy coalitional game approach is applied to the decision-making issue of CAVs at unsignalized intersections. Two types of fuzzy coalitions are constructed that reflect both individual and social benefits. The benefit allocation in the two types of fuzzy coalitions is associated with the driving aggressiveness of CAVs. Finally, the effectiveness and feasibility of the proposed decision-making framework are verified with three test cases.

To address the safety and efficiency issues of vehicles at multi-lane merging zones, a cooperative decision-making framework is designed for connected automated vehicles (CAVs) using a coalitional game approach. Firstly, a motion prediction module is established based on the simplified single-track vehicle model for enhancing the accuracy and reliability of the decision-making algorithm. Then, the cost function and constraints of the decision making are designed considering multiple performance indexes, i.e. the safety, comfort and efficiency. Besides, in order to realize human-like and personalized smart mobility, different driving characteristics are considered and embedded in the modeling process. Furthermore, four typical coalition models are defined for CAVS at the scenario of a multi-lane merging zone. Then, the coalitional game approach is formulated with model predictive control (MPC) to deal with decision making of CAVs at the defined scenario. Finally, testings are carried out in two cases considering different driving characteristics to evaluate the performance of the developed approach. The testing results show that the proposed coalitional game based method is able to make reasonable decisions and adapt to different driving characteristics for CAVs at the multi-lane merging zone. It guarantees the safety and efficiency of CAVs at the complex dynamic traffic condition, and simultaneously accommodates the objectives of individual vehicles, demonstrating the feasibility and effectiveness of the proposed approach.

This paper investigates a human-machine cooperative trajectory planning and tracking control approach for automated vehicles. The proposed method is developed based on a novel algorithm of cooperative human-machine rapidly-exploring random (HM-RRT) for path planning, together with the risk assessment of driver behavior. First, the driver’s behaviour is assessed according to the information of the predicted vehicle trajectory, the identified safe driving area and the driving risks evaluated in both lateral and longitudinal directions. Based on the driver’s expected driving task, when driving risks are identified by real-time assessment, then the human-machine cooperation is activated during trajectory planning. By HM-RRT, the newly developed safety assurance mechanism for path planning, the cooperative trajectory is then generated, which incorporates the driver’s desire and actions and automation’s corrective actions, to ensure the safety, stability and smoothness of the human-vehicle system. The simulation and experimental results show that the proposed HM-RRT algorithm can effectively improve the convergence rate and reduce the computation load, comparing to the conventional method. Beyond this, the proposed human-machine cooperation approach is able to simultaneously ensure the safety, stability and smoothness of the vehicle and largely reduce human-machine conflicts in real-time applications, demonstrating its feasibility and effectiveness.

Considering that human-driven vehicles and autonomous vehicles (AVs) will coexist on roads in the future for a long time, how to merge AVs into human drivers' traffic ecology and minimize the effect of AVs and their misfit with human drivers, are issues worthy of consideration. Moreover, different passengers have different needs for AVs, thus, how to provide personalized choices for different passengers is another issue for AVs. Therefore, a human-like decision making framework is designed for AVs in this paper. Different driving styles and social interaction characteristics are formulated for AVs regarding driving safety, ride comfort and travel efficiency, which are considered in the modeling process of decision making. Then, Nash equilibrium and Stackelberg game theory are applied to the noncooperative decision making. In addition, potential field method and model predictive control (MPC) are combined to deal with the motion prediction and planning for AVs, which provides predicted motion information for the decision-making module. Finally, two typical testing scenarios of lane change, i.e., merging and overtaking, are carried out to evaluate the feasibility and effectiveness of the proposed decision-making framework considering different human-like behaviors. Testing results indicate that both the two game theoretic approaches can provide reasonable human-like decision making for AVs. Compared with the Nash equilibrium approach, under the normal driving style, the cost value of decision making using the Stackelberg game theoretic approach is reduced by over 20%.

Connected automated driving technologies have shown tremendous improvement in recent years. However, it is still not clear how driving behaviors and energy consumption correlate with each other and to what extent these factors related to connected vehicles can influence the motion prediction performance. The precise recognition of driving behaviors and prediction of the vehicle motion is critical to the driving safety for connected automated vehicles (CAVs). Hence, in this study, an energy-aware driving pattern analysis and motion prediction system are proposed for CAVs using a deep learning-based time-series modeling approach. First, energy-aware longitudinal acceleration and deceleration behaviors and lateral lane-change behaviors are statistically analyzed. Then, a sliding standard deviation (SSD) test is applied to evaluate the smoothness of the trajectory and velocity signals considering different energy consumption levels. An energy-aware personalized joint time-series modeling (PJTSM) approach based on a deep recurrent neural network (RNN) and long short-term memory (LSTM) cell are proposed for accurate motion (trajectory and velocity) prediction of the leading vehicle. Finally, the differences in the prediction performance regarding different energy consumption levels are compared and discussed. It is shown that due to the higher randomness of the driving behaviors, the prediction accuracy for heavy energy users is the lowest among the three categories, which means it is harder to anticipate the driving behaviors of cars exhibiting heavy energy consumption. The personalized estimation of driving behaviors of CAVs will contribute to safer automated driving and transportation systems.

In this paper, a novel reference-free shared control system is designed for obstacle avoidance for automated vehicles. Rather than using a reference path to guide the driver, the proposed framework constrains the vehicle's status to guarantee the safety without scarifying the driver's freedom. The constrained Delaunay triangle method is introduced to identify the vehicle's position constraints and the constraints of obstacle avoidance, vehicle stability and physical limitations are investigated and unified. A nonlinear predictive control problem, which is constructed accounting nonlinear vehicle dynamics and given driver actions, is designed to optimize the steering and braking actions needed to keep the vehicle safe. The automation is supposed to correct the driver's steering or braking actions to prevent constraint violation and losing the control of vehicle. The simulation results show that the automation can assist the driver to avoid obstacles and guarantee the vehicle's stability with minimal control intervention.