Search Results (132)

This study addresses critical safety challenges in sustainable urban mobility by developing a probabilistic framework for lane-change risk prediction on congested expressways. Utilizing unmanned aerial vehicle (UAV)-captured trajectory data from 784 validated lane-change events, we construct a Bayesian network model integrated with an I-CH scoring-enhanced MMHC algorithm. This approach quantifies risk probabilities while accounting for driver decision dynamics and input data uncertainties—key gaps in conventional methods like time-to-collision metrics. Validation via the Asia network paradigm demonstrates 80.5% reliability in forecasting high-risk maneuvers. Crucially, we identify two sustainability-oriented operational thresholds: (1) optimal lane-change success occurs when trailing-vehicle speeds in target lanes are maintained at 1.0–3.0 m/s (following-gap < 4.0 m) or 3.0–6.0 m/s (gap ≥ 4.0 m), and (2) insertion-angle change rates exceeding 3.0°/unit-time significantly elevate transition probability. These evidence-based parameters enable traffic management systems to proactively mitigate collision risks by 13.26% while optimizing flow continuity. By converting behavioral insights into adaptive control strategies, this research advances resilient transportation infrastructure and low-carbon mobility through congestion reduction. Full article

Legal requirements for minimum distances between vehicles are often not met for short periods of time, especially when changing lanes on multi-lane roads. These situations are typically non-hazardous, as human drivers anticipate surrounding traffic, allowing for shorter headways and improved traffic flow. Automated vehicles (AVs), however, are typically designed to maintain strict headway limits, potentially reducing traffic efficiency. Therefore, legal questions arise as to whether mandatory gap and headway limits for AVs may be violated during periods of non-compliance. While traffic flow simulation is a common method for analyzing AV impacts, previous studies have typically modeled AV behavior using driver models originally designed to replicate human driving. These models are not well suited for representing clearly defined, structured non-compliant maneuvers, as they cannot simulate intentional, rule-deviating strategies. This paper addresses this gap by introducing a concept for AV non-compliant behavior and implementing it as a module within a pre-existing AV driver model. Simulations were conducted on a three-lane highway with an on-ramp under varying traffic volumes and AV penetration rates. The results showed that, with an AV-penetration rate of more than 25%, road capacity at highway entrances could be increased and travel times reduced by over 20%, provided that AVs were allowed to merge with a legal gap of 0.9 s and a minimum non-compliant gap of 0.6 s lasting up to 3 s. This suggests that performance gains are achievable under adjusted legal requirements. In addition, the proposed framework can serve as a foundation for further development of AV driver models aiming at improving traffic efficiency while maintaining regulatory compliance. Full article

Path planning and collision avoidance are essential components of an autonomous driving system (ADS), ensuring safe navigation in complex environments shared with other road users. High-quality planning and reliable obstacle avoidance strategies are essential for advancing the SAE autonomy level of autonomous vehicles, which can largely reduce the risk of traffic accidents. In daily driving scenarios, lane changing is a common maneuver used to avoid unexpected obstacles such as parked vehicles or suddenly appearing pedestrians. Notably, lane-changing behavior is also widely regarded as a key evaluation criterion in driver license examinations, highlighting its practical importance in real-world driving. Motivated by this observation, this paper aims to develop an autonomous lane-changing system capable of dynamically avoiding obstacles in multi-lane traffic environments. To achieve this objective, we propose a hierarchical decision-making and control framework in which a Double Deep Q-Network (DDQN) agent operates as the high-level planner to select lane-level maneuvers, while a High-Order Control Lyapunov Function–High-Order Control Barrier Function–based Quadratic Program (HOCLF-HOCBF-QP) serves as the low-level controller to ensure safe and stable trajectory tracking under dynamic constraints. Simulation studies are used to evaluate the planning efficiency and overall collision avoidance performance of the proposed hierarchical control framework. The results demonstrate that the system is capable of autonomously executing appropriate lane-changing maneuvers to avoid multiple obstacles in complex multi-lane traffic environments. In computational cost tests, the low-level controller operates at 100 Hz with an average solve time of 0.66 ms per step, and the high-level policy operates at 5 Hz with an average solve time of 0.60 ms per step. The results demonstrate real-time capability in autonomous driving systems. Full article

With the rapid advancement of urbanization, urban transportation systems are facing increasingly severe congestion challenges, especially at traditional roundabouts. The rapid increase in vehicles has led to a sharp increase in pressure at roundabouts. In order to alleviate the traffic pressure in the roundabout, this paper changes the road design parameters of the roundabout, uses a CFD method combined with sensitivity analysis to study the influence of different inlet angles, lane numbers, and the outer radius on the pressure, and seeks the road design parameter scheme with the optimal mitigation effect. Firstly, the full factorial experimental design method is used to select the sample points in the design sample space, and the response values of each sample matrix are obtained by CFD. Secondly, the response surface model between the road design parameters of the roundabout and the pressure in the ring is constructed. The single-factor analysis method and the multi-factor analysis method are used to analyze the influence of the road parameters on the pressure of each feature point, and then the moment-independent sensitivity analysis method based on the response surface model is used to solve the sensitivity distribution characteristics of the road design parameters of the roundabout. Finally, the Kriging surrogate model is constructed, and the NSGA-II is used to solve the multi-objective optimization problem to obtain the optimal solution set of road parameters. The results show that there are significant differences in the mechanism of action of different road geometric parameters on the pressure of each feature point of the roundabout, and it shows obvious spatial heterogeneity of parameter sensitivity. The pressure changes in the two feature points at the entrance conflict area and the inner ring weaving area are significantly correlated with the lane number parameters. There is a strong coupling relationship between the pressure of the maximum pressure extreme point in the ring and the radius parameters of the outer ring. According to the optimal scheme of road parameters, that is, when the parameter set (inlet angle/°, number of lanes, outer radius/m) meets (35.4, 5, 65), the pressures of the feature points decrease by 34.1%, 38.3%, and 20.7%, respectively, which has a significant effect on alleviating the pressure in the intersection. This study optimizes the geometric parameters of roundabouts through multidisciplinary methods, provides a data-driven congestion reduction strategy for the urban sustainable development framework, and significantly improves road traffic efficiency, which is crucial for building an efficient traffic network and promoting urban sustainable development. Full article

This paper proposes an integrated control framework for improving the trajectory tracking performance of autonomous ground electric vehicles (AGEVs) under complex disturbances, including parameter uncertainties, and environmental changes. The framework integrates active disturbance rejection control (ADRC) for real-time disturbance estimation and compensation with a deep deterministic policy gradient (DDPG)-based deep reinforcement learning (DRL) algorithm for dynamic optimization of controller parameters to improve tracking accuracy and robustness. More specifically, it combines the Line of Sight (LOS) guidance rate with ADRC, proves the stability of LOS through the Lyapunov law, and designs a yaw angle controller, using the extended state observer to reduce the impact of disturbances on tracking accuracy. And the approach also addresses the nonlinear vehicle dynamic characteristics of AGEVs while mitigating internal and external disturbances by leveraging the inherent decoupling capability of ADRC and the data-driven parameter adaptation capability of DDPG. Simulations via CarSim/Simulink are carried out to validate the controller performance in serpentine and double-lane-change maneuvers. The simulation results show that the proposed framework outperforms traditional control strategies with significant improvements in lateral tracking accuracy, yaw stability, and sideslip angle suppression. 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

This study optimizes the deployment of autonomous taxis for safety at skewed intersections through a simulation-based approach, identifying an optimal penetration rate and control strategies. Here, we investigate the safety impacts of autonomous taxis (ATs) at such intersections using a simulation-based approach, leveraging the VISSIM traffic simulation tool and the Surrogate Safety Assessment Model (SSAM). Our study identifies an optimal AT penetration rate of approximately 0.5–0.7, as exceeding this range may lead to a decline in safety metrics such as TTC and PET. We find that connectivity among ATs does not linearly correlate with safety improvements, suggesting a nuanced approach to AT deployment is necessary. The “Normal” control strategy, which mimics human driving, shows a direct proportionality between AT penetration and TTC, indicating that not all levels of automation enhance safety. Our conflict analysis reveals distinct patterns for crossing, lane-change, and rear-end conflicts under various control strategies, underscoring the need for tailored approaches at skewed intersections. This research contributes to the discourse on AT safety and informs the development of traffic management strategies and policy frameworks that prioritize safety and efficiency in the context of skewed intersections. Full article

As human-driven vehicles (HDVs) and autonomous vehicles (AVs) coexist on the road, the asymmetry between their driving behaviors, decision-making processes, and responses to traffic scenarios introduces new safety challenges, especially in complex merging areas where frequent interactions occur. The existing traffic safety analysis of mixed traffic is mainly to analyze each safety index separately, lacking comprehensive evaluation. To investigate the safety risk more broadly, this study proposes a comprehensive safety evaluation framework for mixed traffic flows in merging areas from the perspective of traffic conflicts, emphasizing the asymmetry between HDVs and AVs. Firstly, an indicator of Emergency Lane Change Risk Frequency is introduced, considering the interaction characteristics of the merging area. A safety evaluation index system is established from lateral, longitudinal, temporal, and spatial dimensions. Then, indicator weights are determined using a modified game theory approach that combines the entropy weight method with the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method, ensuring a balanced integration of objective data and expert judgment. Subsequently, a cloud model enhanced with the fuzzy mean value method is then developed to evaluate comprehensive safety. Finally, a simulation experiment is designed to simulate traffic operation of different traffic scenarios under various traffic flow rates, AV penetration rates, and ramp flow ratios, and the traffic safety of each scenario is estimated. Moreover, the evaluation results are compared against those derived from the fuzzy comprehensive evaluation (FCE) method to verify the reliability of the comprehensive evaluation model. The findings indicate that safety levels deteriorate with increasing total flow rates and ramp flow ratios. Notably, as AV penetration rises from 20% to 100%, safety conditions improve significantly, especially under high-flow scenarios. However, at AV penetration rates below 20%, an increase of the AV penetration rate may worsen safety. Overall, the proposed integrated approach provides a more robust and accurate assessment of safety risks than single-factor evaluations, providing deeper insights into the asymmetries in traffic interactions and offering valuable insights for traffic management and AV deployment strategies. Full article

With the advancement of autonomous driving technology, transportation systems are inevitably confronted with mixed traffic flows consisting of connected and automated vehicles (CAVs) and human-driven vehicles (HDVs). Current research has predominantly focused on implementing homogeneous control strategies for ramp merging vehicles in such scenarios, which, however, may result in the oversight of specific requirements in fine-grained traffic scenarios. Therefore, a classified cooperative merging strategy is proposed to address the challenges of microscopic decision-making in hybrid traffic environments where HDVs and CAVs coexist. The optimal cooperating vehicle on the mainline is first selected for the target ramp vehicle based on the principle of minimizing time differences. Three merging strategies—joint coordinated control, partial cooperation, and speed limit optimization—are then established according to the pairing type between the cooperating and ramp vehicles. Optimal deceleration and lane-changing decisions are implemented using the average speed change rate within the control area to achieve cooperative merging. Validation via a SUMO-based simulation platform demonstrates that the proposed strategy reduces fuel consumption by 6.32%, NO_x emissions by 9.42%, CO₂ emissions by 9.37%, and total delay by 32.15% compared to uncontrolled merging. These results confirm the effectiveness of the proposed strategy in mitigating energy consumption, emissions, and vehicle delays. Full article

Managing emissions and congestion in urban transportation systems is a growing challenge, particularly when traffic dynamics are influenced by real-time conditions and infrastructure constraints. This study addresses this issue by proposing a cellular automata-based model to analyze traffic emissions and flow dynamics in two-route traffic systems under one-directional flow conditions, incorporating various real-time information feedback strategies. Unlike previous studies, the proposed model integrates key components of urban infrastructure, such as lane-changing dynamics, traffic signalization, and vehicle-type heterogeneity, along with operational factors including entry rates, exit probabilities, and the number of waiting vehicles. The model aims to fill a gap in existing emission studies by capturing the dynamics of heterogeneous, multi-lane systems with integrated feedback mechanisms. These considerations provide valuable insights into traffic management and emission mitigation strategies. The analysis reveals that prioritizing information feedback from the system entrance, rather than relying on feedback from the entire system, more effectively reduces traffic emissions. Additionally, the Vehicle Number Feedback Strategy (VNFS) proved to be the most effective, reducing the number of waiting vehicles and consequently lowering CO₂ emissions. Furthermore, simulation results indicate that for entry rate values below approximately 0.4, asymmetrical lane-changing generates higher emissions, whereas symmetrical lane-changing yields elevated emissions when entry rate surpasses this threshold. Overall, this research contributes to advancing the understanding of traffic management strategies and offers actionable insights for emissions mitigation in two-route systems, with potential applications in intelligent transportation infrastructure. Full article

This work aims to investigate the effectiveness of road maintenance interventions by analyzing changes in the International Roughness Index (IRI) by means of crowdsourced connected vehicle data. For this purpose, 136 pavement maintenance interventions on a single lane were considered over a period between 2021 and 2024. A multiple linear regression model (R² = 0.780) has been employed as statistical tool to assess the relationship between pre/post-intervention IRI scores and various factors. Despite the fact that results showed a general improvement in pavement condition, the effectiveness of the interventions was found to be influenced by several factors. In particular, intervention on the middle lane appears to be the most effective for enhancing section characteristics, and the effectiveness of maintenance on the overall condition of the section tends to be reduced as the number of lanes increases. Additionally, maintenance appears to be less effective in improving post-maintenance performance as the initial IRI value increases; this suggests that severely deteriorated sections may require more extensive rehabilitation strategies. The ultimate aim of study is to provide evidence to support the inclusion of crowdsource vehicle data in Pavement Management Systems (PMSs) to optimize maintenance planning and resource allocation. Full article

To address the challenge of optimizing system adaptability, disturbance rejection, control precision, and convergence speed simultaneously in four-wheel steering (4WS) stability control, a 4WS controller with a variable steering ratio (VSR) strategy and fast adaptive super-twisting (FAST) sliding mode control is proposed to control and output the steering angles of four wheels. The ideal VSR strategy is designed based on the constant yaw rate gain, and a cubic quasi-uniform B-spline curve fitting method is innovatively used to optimize the VSR curve, effectively mitigating steering fluctuations and obtaining precise reference front wheel angles. A controller based on FAST is designed for active rear wheel steering control using a symmetric 4WS vehicle model. Under double-lane change conditions with varying speeds, the simulations show that, compared with the constant steering ratio, the proposed VSR strategy enhances low-speed sensitivity and high-speed stability, improving the system’s adaptability to different operating conditions. Compared with conventional sliding mode control methods, the proposed FAST algorithm reduces chattering while increasing convergence speed and control precision. The VSR-FAST controller achieves optimization levels of more than 7.3% in sideslip angle and over 41% in yaw rate across different speeds, achieving an overall improvement in the stability control performance of the 4WS system. Full article

This study quantified the effects of seven proposed traffic management strategies (MS) to leverage the synergy between Active Traffic Management (ATM) and connected and automated vehicles (CAV) to mitigate congestion, reduce queue lengths, and improve travel time after incident occurrence. First, three proposed MS are discussed: (a) controlling speed limit but not restricting lane changes, (b) directing CAV to change lanes earlier, and (c) restricting CAV in open lanes from lane changes near incidents. Then, combinations of these strategies are presented. At 10% CAV MP, MS1 that focuses on longitudinal control reduced travel time by 11.6% compared to 1.9% with no MS. Similarly, MS2, which directs CAV to change lanes earlier, were most effective when applied at 1-mile upstream of the incident site, achieving a notable 6.0% travel time reduction compared to 1.9% with no MS. The beneficial impact of MS3, which restricts CAV in open lanes from making lane changes near incident sites, became more pronounced with increasing CAV MP. Among the combined strategies (MS4 to MS7), some strategies proved more effective than others. Findings from Vissim simulation runs showed the importance of combining CAV and MS. 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

Bus lanes with intermittent priority (BLIP) are designed to optimize road resource allocation. The advent of connected and automated vehicles (CAVs) promotes the implementation of BLIP. However, it is crucial to find an effective method to intermittently grant right-of-way to CAVs. In this paper, we introduce a BLIP method with spatio-temporal clear distance (BLIP-ST) and a CAV control method in an intelligent transportation environment. When CAVs access BLIP-ST, the constraints of the moving gap between buses are considered. When CAVs leave BLIP-ST, coordination with the nearest CAV platoon on the adjacent lane is considered to cope with situations where CAVs cannot find the appropriate space. Then, the proposed method was simulated by an open boundary cellular automaton model. The results showed that with the same inflow, a CAV-sharing bus lane could significantly improve road traffic efficiency, and it is the most significant when the CAV penetration rate is medium, with the average road speed increasing from 6.67 km/h to 30.53 km/h. Meanwhile, when the CAV penetration rate is medium, BLIP-ST operates with the best efficiency at different strategies. This was due to the fact that when the penetration rate is too high, BLIP-ST is excessively occupied, which affects public transportation priority. When the penetration rate is too low, BLIP-ST cannot be fully utilized. In addition, regardless of the penetration rate of CAV, CAV platoon collaborative lane changing is better than single CAV collaborative lane changing in terms of improving road traffic efficiency and can increase the average road speed by 8–19%. Full article