ABSTRACT The best car-following model (Intelligent Driver Model) incorporates desired speed parameter, whereas the literature suggested to include such parameter in driving behavior of lane changing model. Previous researches, however, have overlooked few things that desired speed values of many vehicles are to be collected from big data, and these values may have a significant effect on discretionary lane changing action. This research proposes the desired speed values for lane changing drivers and target lane vehicle drivers from calibrated IDM using big data for on-ramp and off-ramp areas, and simulates this IDM using the proposed data for validation test. The calibration method uses a genetic algorithm against the real dataset. Further, finding results suggest overcoming conflicts in this dataset by controlling the used dynamic factors. High performance-based traffic simulation software in the future can use the further developed model to decrease traffic crashes, bottlenecks, and long signals in the intersection.

Jamming transition in intelligent driver model integrating fault-tolerant control to counteract cyber-attacks within cellular vehicle-to-everything environments

This study devised a fog recognition model and simulated traffic flow in low visibility. It initially built a cloud image recognition model based on convolutional neural network and support vector machine. Subsequently, a mixed traffic flow model was developed for low-visibility conditions. The results showed that the Gaussian kernel function achieved the highest fog image recognition accuracy, reaching 92.58%, while the polynomial kernel function had the lowest accuracy of 84.19%. When five experiments were conducted, the fog image recognition model in this study exhibited the highest accuracy (0.94), recall (0.875), and F1 score (F1) (0.9). In a vertical driving formation, vehicles ahead travelled faster, indicating that the convergence speed and stability of the full speed difference within the formation were improved. The enhanced intelligent driver model demonstrated minimal speed fluctuations, with all vehicles in an 8-car fleet reaching a stable driving speed within 40 s. This implies excellent stability of the improved intelligent driver model. In conclusion, the model developed in this research shows promising practical applications in fog recognition and traffic flow management under low visibility, and has positive significance for improving highway safety performance.

Congestion transition in intelligent driver model with fault-tolerant control to counter cyber-attacks during the lane-changing process under a connected autonomous vehicles platform

Analysis of secondary risks induced by defensive braking in autonomous vehicles: a study based on stochastic distribution of drivers.

Safety and efficiency-oriented adaptive strategy controls for connected and automated vehicles in unstable communication environment.

New perception error patterns, such as misdetection, emerge in Autonomous Vehicles (AV) and other autonomous systems due to the pervasive implementation of AI-driven algorithms. However, existing planning/control approaches in AVs have not yet adapted to these new error patterns because of their black-box or grey-box nature and high complexity. This lack of adaptation leads to increased collision risk and reduced comfort of passengers. In this paper, the negative effects of the misdetection arising in the AI-enabled perception system are first investigated, where the widely used Intelligent Driver Model (IDM) for the car-following task is selected as a case study. Simulation result shows that the presence of perception errors may lead to unsafe behavior of an IDM. A novel car-following control scheme, Safe IDM, is designed to adapt to misdetection and measurement noise. A state estimation module based on Perception Error Model (PEM) and Intermittent Kalman FIlter (IKF) is designed, and followed by a safety-boundary calculation module on the basis of classical IDM. It is shown that the proposed safe IDM is able to maintain stable following of the leading vehicle in the presence of varying detection rates and measurement noise. Simulation results confirm that, compared to the original IDM that does not consider misdetection errors in its design, the proposed safe IDM exhibits significant improvements in both comfort of passengers and safety. This paper shows that AI-induced perception errors could substantially degrade the performance of autonomous driving functions and increase their collision risk, while the perception-error-aware controller has great potential to reduce their negative effects.

Connected and automated vehicle (CAV) platooning, enabled by Vehicle-to-Vehicle (V2V) communication, promises significant improvements in traffic safety, throughput, and energy efficiency. However, communication constraints — such as range limitations and intermittent connectivity — disrupt information flow, destabilizing platoon dynamics. Existing models lack a unified framework to analyze how these constraints propagate through CAV interactions. To address this gap, the Platoon Intelligent Driver Model (PIDM) is proposed, a novel analytical framework that integrates dynamic communication topologies (predecessor-following, predecessor-leader-following, and 𝕜-predecessor-leader-following) with vehicle dynamics under V2V limitations. The PIDM enables systematic stability analysis and quantifies disturbance propagation mechanisms. Through numerical simulations, the study demonstrates that: (1) 𝕜-predecessor-leader-following topology reduces recovery time by 32% compared to conventional topologies; (2) smaller communication ranges (4–6 vehicles) optimize stability in urban roads, while larger ranges (8–10 vehicles) suit highways; (3) delay time tolerance thresholds depend on platoon size and topology complexity. These findings provide actionable guidelines for designing robust CAV platoon systems under real-world communication constraints.

Effective communication solutions are crucial in the dynamic transportation landscape. The rise of autonomous vehicles and sophisticated transportation systems has shaped urban mobility, underscoring the importance of safety considerations and data-driven decision making. This study examines the significance of rapid, low-latency communication in advanced intelligent transportation systems, focusing on the use of Visible Light Communication (VLC) in vehicle ad hoc networks (VANETs). This study introduces a tree-structured communication architecture utilizing hierarchical optical codewords to enhance data routing efficiency and establish a vehicle identification system. The proposed system employs dynamic attachment and reattachment protocols in conjunction with adaptive quality-of-service mechanisms to effectively mitigate variability in traffic dynamics, thus enhancing network stability and data aggregation. Simulation results contrasting the Intelligent Driver Model, Gipps, and Krauss mobility models indicate that, while more complex network trees may lead to increased delay and lower effective signal-to-noise ratios, models characterized by greater vehicular spacing generally result in reduced delay and enhanced SNR, though this improvement comes at the cost of connectivity. This document provides a detailed examination of mobility-aware performance and the incorporation of tree-structured VLC VANETs that employ hierarchical optical codewords for distinct node identification. The performance insights reveal significant improvements in scalability, latency, and throughput, which support the advancement of smart city infrastructures that are more sustainable, efficient, and secure.

Abstract The coupling of microscopic traffic simulation models with emission models offers a powerful tool for assessing and optimising traffic control strategies to reduce fuel consumption and vehicle emissions. Although many studies use traffic simulation for emission analysis and designing traffic control measures, most focus on calibrating a selected traffic model to replicate observed traffic flow. This raises a critical question: are the resulting optimal emission control strategies adequately designed to account for the sensitivity of traffic models in capturing vehicle dynamics and emissions? To address this issue, we compared three car-following models—the Krauss model, the Intelligent Driver Model (IDM), and the Wiedemann model—each rooted in distinct theoretical frameworks to understand traffic dynamics. We evaluated their performance in optimising road speed limits to minimise (PMx) emissions in a school case study. A school was selected as the case because children are highly vulnerable and particularly exposed to pollutants during their school commute, and their exposure can be mitigated through optimal traffic control. Our findings reveal that, even when tuned to achieve comparable levels of traffic flow, the models displayed significant differences in their objective functions for traffic control optimisation. These discrepancies stemmed from variations in fuel consumption and particulate matter (PMx) emission patterns resulting from the traffic dynamics captured by the selected traffic model. At a macroscopic level (e.g., average speed, flow, and density), the models exhibited minimal differences. However, at a microscopic level (e.g., acceleration, deceleration rates, and deviations from the mean), pronounced differences became evident. These results highlight that while certain traffic control strategies appeared less effective, revisiting and critically examining the limitations of the models is essential to ensure robust and tailored solutions for emission reduction.

Different micro-meteorological conditions can affect a driver's judgment of road conditions, leading to changes in following behavior. On rainy days, water films on the road reduce traction, increasing the likelihood of hydroplaning and traffic accidents. While there are existing following models under various weather conditions, research on the specific impact of micro-meteorological factors is insufficient. To achieve fine management in intelligent transportation and real-time monitoring of vehicle states, it's essential to study following behavior under different micro-meteorological conditions and establish corresponding models. This paper focuses on the Intelligent Driver Model (IDM) and the Wiedemann99 model, considering the impact of micro-meteorological conditions. By incorporating a driver's judgment factor, λ, the IDM and Wiedemann99 models are improved, leading to the development of new models: I-IDM and I-Wiedemann99. Simulation validation is used to choose speed and following distance as performance indicators for parameter calibration of the I-IDM and I-Wiedemann99 models, with the sum of Root Mean Square Percentage Error (RMSPE) as the goodness-of-fit function. Comparisons are made between the driving paths, speeds, and accelerations of following vehicles before and after calibration, verified through simulations. The conclusions are as follows: the average error and standard deviation of the improved I-IDM model are smaller than those of the I-Wiedemann99 model, with the maximum Root Mean Square Percentage Error (RMSPE) for I-IDM model parameter calibration being 0.4568 and the minimum being 0.1324. For the I-Wiedemann99 model, the maximum RMSPE is 0.4613 and the minimum is 0.1376. The parameter calibration results of the I-Wiedemann99 model are more dispersed compared to those of the I-IDM model, indicating that the I-IDM model simulates following behavior more effectively than the I-Wiedemann99 model. The findings of this study can provide a reference for further improving the theory of following behavior, and offer a theoretical basis and IoT technology support for refined traffic management under rainy conditions.

With the development of superhighways, research on mixed traffic flow has become increasingly important. This paper aims to investigate the car-following behavior of different vehicle types on superhighways, focusing on the impacts of connected autonomous vehicles (CAVs), human-driven vehicles, and degraded CAVs. By employing the intelligent driver model, the adaptive cruise control model (ACC), and the cooperative ACC model, a fundamental graph was constructed to analyze traffic capacity on superhighways. A decrease followed by an increase in freeway capacity was found to occur as the CAVs’ penetration rate increased. The study reveals that the penetration rate significantly affects traffic flow fluctuations, with higher penetration rates potentially increasing the risk of congestion. The findings provide a theoretical basis for traffic management and optimization on superhighways.

Abstract As a fundamental driving behavior, the accurate modeling of car‐following (CF) dynamics is essential for improving traffic flow and advancing autonomous driving technologies. Due to the stochastic nature of CF behaviors, the CF model parameters often exhibit heterogeneity (multimodal trends), distribution uncertainty, and parameter correlations. Most studies have examined correlations among CF model parameters, assuming deterministic marginal distributions, and investigated heterogeneity through driving behavior indicators. However, distribution uncertainty and multimodal trends in CF model parameter characteristics remain insufficiently explored. To address this challenge, this study proposes a driving style–based Bayesian model averaging Copula (DS‐BMAC) framework that simultaneously accounts for heterogeneity, distribution uncertainty, and parameter correlations in CF behavior modeling. Using the intelligent driver model (IDM) as a representative example, its parameters are calibrated using CF trajectory data extracted from the Waymo open motion data set. Based on these calibrated IDM parameters, a multivariate Gaussian mixture model is employed to categorize three distinct driving styles, capturing heterogeneity. Subsequently, a Bayesian model average Copula approach is applied to address distribution uncertainty and parameter correlations. Deterministic and multivehicle ring road simulations were conducted to assess the effectiveness of the proposed DS‐BMAC framework. The results demonstrate that the DS‐BMAC framework provides a precise characterization of CF model parameters and effectively reproduces microscopic CF behaviors compared to other approaches. Additionally, the DS‐BMAC framework offers a realistic representation of traffic flow dynamics. The research findings are valuable for understanding mixed traffic flow dynamics and for developing CF decision‐making models for autonomous vehicles and advanced driver‐assistance systems.

This paper presents a calibration technique for the Intelligent Driver Model (IDM), a car-following model that considers the physical interpretation of each parameter. Using an instrumented vehicle, trajectory data were gathered for a group of Portuguese drivers. The data included various basic scenarios, such as unrestricted acceleration and deceleration maneuvers, as well as following other cars in steady-state conditions. The calibration process involved two steps. In the first step, specific parameters that have clear physical interpretations were manually adjusted to accurately reproduce the speed patterns of basic driving scenarios. In the second step, the obtained results were used to establish the limits of values for a simultaneous calibration procedure. The results demonstrate that the calibration procedure enables precise replication of the actual trajectories. Nevertheless, the model validation results indicate that calibrating without limitations on the parameter search space produces estimates with greater explanatory capability, contradicting previous research and supporting the need for additional analyses.

Car-following safety modeling and risk assessment of autonomous vehicle in icy and snowy weather.

As connected and autonomous vehicle (CAV) technologies are rapidly integrated into modern transportation systems, understanding the dynamics of mixed traffic flow involving both human-driven vehicles (HVs) and CAVs is becoming increasingly important, particularly under uncertain conditions. In this paper, we propose a car-following model framework to investigate the combined effects of driver stochasticity and connectivity uncertainties of CAVs on mixed traffic flow. The proposed framework can capture the inherent stochastic variations in human driving behavior by extending the classic intelligent driver model (IDM) with a Langevin-type stochastic differential equation. A car-following model with multi-anticipation control is developed for CAVs, explicitly incorporating sensor noise, communication delays, and dynamic connectivity. Extensive numerical simulations demonstrate that higher CAV penetration leads to more stable traffic flows. Even with certain levels of connectivity uncertainty, CAVs can still effectively stabilize the traffic. However, driver stochasticity has a pronounced impact on traffic stability-greater variability in driver behavior tends to reduce overall stability. Furthermore, sensitivity analyses reveal that in pure CAV environments, sensor noise, communication delays and communication ranges can affect traffic stability and energy consumption. In contrast, in mixed traffic conditions, the inherent instability of HV behavior tends to dominate and diminish the relative influence of CAV connectivity-related uncertainties. These findings underscore the necessity of robust sensor fusion and error compensation strategies to fully realize the potential of CAV technology. In mixed traffic environments, measures should be taken to minimize the adverse effects of HVs on CAV performance.

The development of e-commerce logistics has driven the expansion of truck transportation. The application of cooperative adaptive cruise control (CACC) technology in truck platooning is considered as an effective way to improve safety and road capacity, as well as reduce fuel consumption and environmental pollution. However, the influence of surrounding vehicles on the safety and efficiency of truck platoons remains a challenge in mixed traffic. This study aims to evaluate the impact of surrounding vehicle behavior, such as car-following and cut-in, on the performance of autonomous truck platooning. Considering traffic flow stability and the impact of the actual road environment, the intelligent driver model is improved. The CACC system control algorithm is further designed. Meanwhile, human-driven vehicle behavior is described based on the full velocity difference model. A simulation platform integrating MATLAB/Simulink/PreScan is developed to replicate real-world vehicle interactions. The safety and efficiency of truck platooning are analyzed quantitatively considering multiple factors. The results show that two car-following events and two cut-in events reduce the fuel saving rate of the platoon by 0.52%–5.04% and 0.15%–2.00%, respectively. At high velocity, the collision risk reflected by inverse time-to-collision is higher for gaps in the front of the platoon as a result of car-following and cut-in. Shorter headway times can result in higher fuel consumption and lower safety. Four recommendations to reduce the impact of surrounding vehicles are presented based on the findings to support the successful deployment of truck platooning in mixed traffic.

In controlled environments like ports, CAV platoons (CAVPs), leveraging cooperative control and communication technologies, have emerged as leading innovators in enhancing traffic safety. The probabilistic characteristics of human drivers are central to traffic flow dynamics.. Unfortunately, this randomness is often overlooked in existing studies examining mixed traffic scenarios in mixed traffic flow. To bridge the gap in modeling interactions between automated vehicle platoons (CAVPs), human-driven cars (HDCs), and trucks (HDTs), this paper introduces a complex mixed traffic model that captures their hierarchical dynamics. To address the limitations of deterministic driver models, this paper employs the Stochastic Intelligent Driver Model (SIDM) to characterize human-driven vehicle (HDV) following behavior with uncertainty. The results reveal that greater platoon length. Under high traffic flow conditions, the presence of CAVPs may adversely affect safety. Furthermore, longer CAVPs, while enhancing coordination, are more susceptible to stochastic disturbances.

Vehicle trajectory data contain extensive spatiotemporal information and are of great significance for analyzing the operational patterns of urban traffic networks, optimizing traffic signal control and achieving refined traffic management. However, due to the low penetration rate of probe vehicles and the limited coverage of fixed sensors, it remains challenging to obtain comprehensive full-sample vehicle trajectory data. To address this issue, this paper proposes a multi-source data fusion-based vehicle trajectory reconstruction method, which comprises vehicle trajectory state estimation and a self-optimization algorithm. First, the trajectory states of undetected vehicles are categorized into four types based on the trajectory states of adjacent probe vehicles. Four corresponding trajectory estimation models are then established using an extended Intelligent Driver Model to reconstruct the initial trajectories of undetected vehicles. Second, a particle filter-based trajectory self-optimization algorithm is proposed, integrating upstream and downstream fixed sensor data to iteratively correct and optimize the initial trajectories by minimizing errors, thereby enhancing trajectory accuracy and smoothness. Case studies demonstrate that the proposed method achieves outstanding performance under low PV penetration rates and in complex traffic environments. Compared to baseline methods, MAE, MAPE, and RMSE are reduced by 14.31%, 22.87%, and 13.36%, respectively. Furthermore, the reconstruction errors of the proposed method gradually decrease as traffic density and PV penetration rates increase. Notably, PV penetration has a more significant impact on model accuracy. These findings confirm the robustness and effectiveness of the proposed method in complex traffic scenarios, providing critical technical support for refined urban traffic management and optimized decision-making.

ABSTRACT The variable time headway (VTH) strategy can sensitively adjust vehicle spacing and it is essential to investigate the role in maintaining platoon stability. The driving characteristics and transformation relationships of connected and automated vehicles (CAVs), connected vehicles (CVs), automated vehicles (AVs), and human-driven vehicles (HVs) within a novel heterogeneous platoon are analyzed, considering the differences in automated driving functionalities and the uncertainties in connected capabilities. An intelligent driver model (IDM) and adaptive cruise control model (ACC) incorporating the VTH strategy, which considers relative velocity (VRV-IDM and VRV-ACC), were developed to characterize HVs and AVs. The cooperative adaptive cruise control (CACC) and IDM under the VTH strategy, considering relative velocity and preceding vehicle acceleration (VPA-CACC and VPA-IDM), were constructed to represent CAVs and CVs. Using the Laplace transform method, the string stability incorporating the preceding vehicle’s acceleration is derived. The results indicate that VTH effectively enhances the string stability.