Search Results (143)

With the rapid development of intelligent transportation, accurately predicting vehicle trajectories is crucial for ensuring road safety and enhancing traffic efficiency. This paper proposes a trajectory prediction model that integrates a diffusion model framework with trajectory features of target and neighboring vehicles, as well as driving intentions. The model uses historical trajectories of the target and adjacent vehicles as input, employs Long Short-Term Memory (LSTM) networks to extract temporal features, and dynamically captures the interaction between the target and neighboring vehicles through a multi-head attention mechanism. An intention module regulates lateral offsets, and the diffusion framework selects the most probable trajectory from various possible predictions, thereby improving the model’s ability to handle complex scenarios. Experiments conducted on real traffic data demonstrate that the proposed method outperforms several representative models in terms of Average Displacement Error (ADE) and Final Displacement Error (FDE), without sacrificing efficiency. Notably, it exhibits higher robustness and predictive accuracy in high-interaction and uncertain scenarios, such as lane changes and overtaking. To the best of our knowledge, this is the first application of the diffusion framework in vehicle trajectory prediction. This study provides an efficient solution for vehicle trajectory prediction tasks. The average ADE within 1 to 5 s reached 0.199 m, while the average FDE within 1 to 5 s reached 0.437 m. Full article

This study proposes a Tube-based Robust Model Predictive Control (Tube-RMPC) strategy for autonomous vehicle control to address model parameter uncertainties and variations in road–tire adhesion coefficients in complex road scenarios. More specifically, the proposed approach improves the representation of vehicle dynamic behavior by introducing a unified vehicle–tire modeling framework. To facilitate computational tractability and algorithmic implementation, the model is systematically linearized and discretized. Furthermore, the Tube-based Robust Model Predictive Control strategy is developed to improve adaptability to uncertainty in the road surface adhesion coefficient. The Tube-based Robust Model Predictive controller ensures robustness by establishing a robust invariant tube around the nominal trajectory, effectively mitigating road surface variations and enhancing stability. Finally, a co-simulation platform integrating CarSim and Simulink is employed to validate the proposed method’s effectiveness. The experimental results demonstrate that Tube-RMPC improves the path-tracking performance, reducing the maximum tracking error by up to 9.17% on an S-curve and 2.25% in a double lane change, while significantly lowering RMSE and enhancing yaw stability compared to MPC and PID. Full article

In an ever-changing driving environment where vehicles are becoming smarter, more autonomous, and more connected, a paradigmatic change in signals for drivers might be required. This need is correlated with road safety (social sustainability). There are several factors affecting road safety, and one of these, especially important on rural roads, is speed. One way to actively influence drivers’ speed is to intervene with regard to speed limit signs by providing credible and effective limits. This goal can be pursued by working on variable speed limits that align with the boundary conditions of the installation site. In this research, an analysis was conducted on the rural road network within the Metropolitan City of Bari (Italy) that involved collecting the speeds on each of the investigated two-way, two-lane rural roads of the network. In addition to the speeds, all the most relevant geometric details of the roads were considered, together with environmental factors like rainfall. A generalized linear model was developed to correlate the operating speed limits and other variables together with information about rainfall, which degrades tire–pavement friction and thus, road safety. After the development of this model, safety performance functions, depending on the amount of rain or number of days of rain, were calculated with the intent of predicting crash frequency, starting with the operative speed and rain conditions. Operative speed, speed limit, percentage of non-compliant drivers, traffic level, and site length were found to be associated with all typologies and locations of crashes investigated. Full article

This investigation analyses driving behaviors that lead to accidents on overly sharp mountain road curves in Nanjing Province, China. We collected information through field observations and driving simulations while analyzing key indicators like the mean speed of vehicles and spacing between vehicles. The FP-Growth algorithm was used to identify frequent behavioral patterns and measure their relationship with traffic conflicts. The findings showed that unsafe driver behavior on sharp turns was common, while the combination of “speeding–tailgating–frequent lane changing” behavior increased conflict risk by 3.7 times. A predictive LSTM neural network model was developed with driver, vehicle, road, and environmental factors. Testing on 4795 samples achieved 83.7% accuracy in foreseeing conflict risk levels. The model, which distinguishes between safety conditions and three severity levels of potential conflict, can provide the most fundamental level of safety needed. The research provides quantitative tools for improved road safety management aimed at supporting real evidence-based “safe roads” approaches. Full article

With the prosperity of the economy and the continuous expansion of the port area, container trucks have become the main means of transportation on port roads. Traditional traffic flow research mainly focuses on passenger cars. In view of the unique characteristics of container truck traffic flow and the lack of research on conflict-influencing factors for this traffic flow, this paper is committed to filling this research gap. This paper uses drones and YOLOv8 technology to construct a vehicle trajectory dataset in the container truck traffic flow scenario and extracts relevant features of container truck traffic flow from vehicle trajectory data from a macro perspective. For the trajectory data after denoising, the time to collision (TTC) indicator is used to identify conflict events, and then the synthetic minority oversampling technique (SMOTE) is used to obtain four datasets. Machine learning and related classification models are selected for conflict prediction. It is worth noting that the XGBoost model performs better than other models on the four datasets, with an accuracy of 0.86 and an AUC value of 0.933. The Shapley additive explanation (SHAP) theory is used to explain and analyze the model results and compare them with existing studies. The results show that in container truck traffic flow, traffic density is the most important factor affecting conflicts, and conflicts occur more frequently when the traffic density is between 50 and 70 vehicles/km, followed by lane change rate. In contrast, for general traffic flows, studies have shown that speed is the main factor affecting conflicts. Full article

Cooperative driving systems can coordinate individual vehicles on the road in a platoon, holding significant promise for enhancing traffic efficiency and lowering the energy consumption of vehicle movements. For an extended period, vehicles on the road will consist of a mix of traditional gasoline and electric vehicles. To explore the economic driving strategies for diverse vehicles on the road, this paper introduces a collaborative eco-driving system that takes into account the energy consumption traits of vehicles. Unlike prior research, this paper puts forward a lane change decision-making approach that integrates energy modeling and speed prediction. This method can effectively capture the speed variations in the vehicle ahead and facilitate lane changes with energy efficiency in mind. The system encompasses three vital functions: vehicle cooperative architecture, ecological trajectory planning, and power system control. Specifically, eco-speed planning is carried out in two stages: the initial stage is executed globally, with cooperative speed optimization performed based on the energy consumption characteristics of different vehicles to determine the economical speed for vehicle platoon driving. The subsequent stage involves local speed adaptation, where the vehicle platoon dynamically adjusts its speed and makes lane change decisions according to local driving conditions. Ultimately, the generated control information is fed into the powertrain control system to regulate the vehicle. To assess the proposed collaborative eco-driving system, the algorithms were tested on highways, and the results substantiated the system’s efficacy in reducing the energy consumption of vehicle driving. Full article

In the context of the rapid advancement of reconstruction and expansion projects, there has been a significant increase in the demand for the inspection and evaluation of existing asphalt pavements. In order to enhance the efficiency and effectiveness of joint detection using 3D ground-penetrating radar and falling weight deflectometers, this study investigates non-destructive testing methods for existing asphalt pavements based on geostatistical correlation principles. The relationship between crack rate and deflection is analyzed using group average values. The characteristic sections division method based on the crack rate guideline was realized. Research on the prediction method for deflection using Kriging interpolation has been conducted. Research has revealed that there is a positive correlation between the crack rate and the deflection index. The principle of the singularity index can be employed to divide characteristic sections. The falling weight deflectometer is capable of conducting targeted testing in accordance with characteristic sections. Furthermore, the superior performance of Kriging interpolation in predicting deflection compared with linear interpolation has been demonstrated. According to the Kriging interpolation principle, the detection interval of slow lane deflection should not be more than 100 m. Kriging interpolation on one way lane of matrix data has the best effect, and it can predict deflection using a limited amount of slow lane and hard shoulder data. This facilitates analysis of the changing trend of the deflection index in cases where detection conditions are constrained. This method is of great significance for grasping the true performance status of the existing asphalt pavement structure. Full article

To enhance road safety and optimize intelligent driving systems, this study introduces the concept of “driving style tendency” to characterize short-term driver behavior, particularly lane-changing patterns. A multidimensional framework is established to analyze driving roles and behaviors, utilizing a Hidden Semi-Markov Model and Hierarchical Dirichlet Process for the unsupervised segmentation of driving trajectory data into behavioral primitives. By systematically analyzing driver behaviors in leading and following scenarios, characteristic thresholds are derived through distribution fitting, enabling the development of a non-parametric Bayesian-based scoring method for driving style tendency. The K-means clustering algorithm is employed to transform primitive segments into quantifiable semantic information, facilitating the interpretation of driver behavior preferences. This research contributes to improved collision risk prediction in complex traffic environments, supports the design of personalized driving assistance systems, and provides valuable insights for autonomous driving technology development. Full article

To meet the active avoidance requirements of intelligent vehicles, this paper proposes an efficient hierarchical control system. The upper layer generates a safe avoidance trajectory through an optimized path planning algorithm, while the lower layer precisely controls the vehicle to follow the planned path. In the upper layer design, an improved quintic polynomial method is employed to generate the baseline trajectory. By dynamically adjusting lane change duration and utilizing an improved dual-quintic algorithm, collisions with preceding vehicles are effectively avoided. Additionally, a genetic algorithm is applied to automatically optimize parameters, ensuring both driving comfort and planning efficiency. The lower layer control is based on a three-degree-of-freedom monorail vehicle model and the Magic Formula tire model, employing a model predictive control (MPC) approach to continuously correct trajectory deviations in real time, thereby ensuring stable path tracking. To validate the proposed system, a co-simulation environment integrating CarSim, PreScan, and MATLAB was established. The system was tested under various vehicle speeds and road conditions, including wet and dry surfaces. Experimental results demonstrate that the proposed system achieves a path tracking error of less than 0.002 m, effectively reducing accident risks while enhancing the smoothness of the avoidance process. This hierarchical design decomposes the complex avoidance task into planning and control, simplifying system development while balancing safety and real-time performance. The proposed method provides a practical solution for active collision avoidance in intelligent vehicles. Full article

Lane changing is a crucial scenario in traffic environments, and accurately recognizing and predicting lane-changing behavior is essential for ensuring the safety of both autonomous vehicles and drivers. Through considering the multi-vehicle information interaction characteristics in lane-changing behavior for vehicles and the impact of driver experience needs on lane-changing decisions, this paper proposes a lane-changing model for vehicles to achieve safe and comfortable driving. Firstly, a lane-changing intention recognition model incorporating interaction effects was established to obtain the initial lane-changing intention probability of the vehicles. Secondly, by accounting for individual driving styles, a lane-changing behavior decision model was constructed based on a Gaussian mixture hidden Markov model (GMM-HMM) along with a parameter estimation method. The initial lane-changing intention probability serves as the input for the decision model, and the final lane-changing decision is made by comparing the probabilities of lane-changing and non-lane-changing scenarios. Finally, the model was validated using real-world data from the Next Generation Simulation (NGSIM) dataset, with empirical results demonstrating its high accuracy in recognizing and predicting lane-changing behavior. This study provides a robust framework for enhancing lane-changing decision making in complex traffic environments. Full article

Autonomous vehicles are increasingly becoming a part of our daily lives, with active chassis control systems playing a pivotal role and drawing significant attention from both academia and industry. Current research on vehicle-to-vehicle overtaking behavior predominantly focuses on low-to-moderate speeds, with insufficient studies addressing high-speed lane-changing maneuvers. Under high-speed conditions, the variability and complexity of road environments significantly increase tracking errors, posing challenges for control algorithms that perform well at lower speeds but may suffer from reduced accuracy or instability at higher speeds. A hybrid control strategy based on vehicle dynamics for high-speed overtaking path tracking is developed to ensure vehicle stability and maneuverability. By integrating Model Predictive Control (MPC) with Four-Wheel Steering (4WS) controllers and employing a two-degree-of-freedom ideal model as the path-tracking response model, we have achieved effective control and path tracking for autonomous vehicles equipped with four-wheel steering. The effectiveness of the proposed control strategy was validated on the Carsim–Simulink integrated simulation platform. Experimental results demonstrate that this strategy offers higher path-tracking accuracy than single-controller approaches under high-speed conditions while also meeting vehicle stability requirements. The model provides robust support for enhancing the path-tracking performance of autonomous four-wheel steering vehicles at medium-to-high speeds, thereby advancing the reliability and safety of autonomous driving technology in practical applications. Full article

This study investigated and compared driver behaviors at signalized and non-signalized roundabouts in Qatar, focusing on turn signal usage, lane change behavior, and correct lane usage. The primary objectives were to determine the frequency of turn signal usage, assess correct lane usage, analyze lane change behavior, and compare these behaviors between the two types of roundabouts. Data were collected through a field study at selected roundabouts, where driver behaviors were observed and analyzed. The results revealed significant differences between signalized and non-signalized roundabouts. Turn signal compliance was higher in signalized roundabouts (up to 45%) compared to non-signalized roundabouts (20%). The rate of lane change in signalized roundabouts was observed to be 31%, whereas it was 14% in non-signalized roundabouts, and correct lane usage compliance was higher in signalized roundabouts (60%) compared to non-signalized roundabouts (35%). These findings suggest that traffic signals contribute to safer and more predictable driver behavior, although congestion and long waiting times in signalized roundabouts present challenges. The study recommends improving signage visibility, optimizing signal timings, enhancing road markings, and enforcing traffic regulations to address these issues. The findings can inform traffic engineers and policymakers in enhancing the safety and efficiency of roundabouts. Full article

In order to quickly and accurately identify the lane changing intention of vehicles, and to deeply consider the time series characteristics of vehicle driving processes and the interactive effects between vehicles, a lane changing intention recognition model, namely, Model_TA, was constructed by combining the time series feature extraction ability of the encoder in the Transformer model, the bidirectional gating mechanism of the bidirectional gated recurrent unit, and the additive attention mechanism. The performance of the Model_TA model was trained and validated on the I-80 dataset in NGSIM. The experimental results showed that the accuracy of model intent recognition was 97.01%, which was 20.3%, 4.73%, and 1.73% higher than that of SVM, LSTM, and Transformer models, respectively; the prediction accuracy at 2.0 s, 2.5 s, and 3.0 s is 90.15%, 84.58%, and 83.13%, respectively, which is better than similar models. It is proved that the model can better predict the lane changing intention of vehicles. Full article

Connected autonomous vehicles (CAVs) face constraints from multiple traffic elements, such as the vehicle, road, and environmental factors. Accurately quantifying the vehicle’s operational status and driving risk level in complex traffic scenarios is crucial for enhancing the efficiency and safety of connected autonomous driving. To continuously and dynamically quantify the driving risks faced by CAVs in the road environment—arising from the front, rear, and lateral directions—this study focused s on the self-driving particle characteristics that enable CAVs to perceive their surrounding environment and make driving decisions. The vehicle-to-vehicle interaction behavior was analogized to the inter-molecular interaction relationship, and a molecular Morse potential model was applied, coupled with the vehicle dynamics theory. This approach considers the safety margin and the specificity of driving styles. A multi-layer decoder–encoder long short-term memory (LSTM) network was employed to predict vehicle trajectories and establish a risk quantification model for vehicle-to-vehicle interaction behavior. Using SUMO software (win64-1.11.0), three typical driving behavior scenarios—car-following, lane-changing, and yielding—were modeled. A comparative analysis was conducted between the risk field quantification method and existing risk quantification indicators such as post-encroachment time (PET), deceleration rate to avoid crash (DRAC), modified time to collision (MTTC), and safety potential fields (SPFs). The evaluation results demonstrate that the risk field quantification method has the advantage of continuously quantifying risk, addressing the limitations of traditional risk indicators, which may yield discontinuous results when conflict points disappear. Furthermore, when the half-life parameter is reasonably set, the method exhibits more stable evaluation performance. This research provides a theoretical basis for the dynamic equilibrium control of driving risks in connected autonomous vehicle fleets within mixed-traffic environments, offering insights and references for collision avoidance design. Full article

In complex real-world traffic environments, the task of automatic lane changing becomes extremely challenging for vehicle control systems. Traditional control methods often lack the flexibility and intelligence to accurately capture and respond to dynamic changes in traffic flow. Therefore, developing intelligent control strategies that can accurately predict the behavior of surrounding vehicles and make corresponding adjustments is crucial. This paper presents an intelligent driving control scheme for autonomous vehicles (AVs) based on a responsibility-sensitive safety (RSS) tracking control mechanism within a Bayesian network intelligent learning framework. Initially, the Bayesian evidence construction method for vehicle lane changing scenarios is studied. Using this method, prior probability tables for lane-hanging vehicles are constructed, and the Bayesian formula is applied to predict the lane changing probabilities of surrounding vehicles. Subsequently, an optimal control method is employed to integrate Bayesian lane changing probabilities into the design of performance indices and auxiliary systems, transforming tracking and safety avoidance tasks into an optimization control problem. Additionally, a critic learning optimal control algorithm is developed to determine the control law. Finally, the proposed tracking control scheme is validated through simulations, demonstrating its reliability and effectiveness. Full article