Vehicle Speed Research Articles

Enhancing stability and flexibility of platoon control with an adaptive spacing policy

Connected and Automated Vehicles (CAVs) forming platoons have shown significant potential in enhancing traffic flow. This paper proposes a cooperative platoon control model using MPC integrated with the CTG spacing policy to improve both platoon stability and overall traffic performance. This integration offers greater flexibility and practicality compared to the CS-based model. Additionally, the control model includes a safety constraint with high flexibility to prevent collisions. We numerically analyse the asymptotic stability of the proposed control model to address its mathematical complexity. A wide range of parameter configurations and weight assignment schemes were tested to provide rigorous proof of the model's asymptotic stability. Numerical experiments show that the CTG-based model offers better stability than the CS-based model, though it leads to larger spacings at higher vehicle speeds. Moreover, traffic simulation results demonstrate that the CTG-based model outperforms the CS-based model in mitigating traffic oscillations and reducing congestion.

Effects of Track Structure Nonlinearity on Running Safety of Simply Supported Girder Bridges During Earthquakes

The nonlinearity of the ballastless track-simply supported girder bridge primarily manifests in the bearings and track structure during earthquakes. While existing research has elucidated the effects of bearing nonlinearity on running safety during earthquakes, the effects of track structure nonlinearity remain unclear. Therefore, this study further explores the effects of track structure nonlinearity on running safety during earthquakes. First, a dynamic analysis of the train–track–bridge coupled system (TTBCS) incorporating track structure nonlinearity, was performed using the ANSYS-TRBF (train–rail–bridge-foundation coupled system dynamic analysis software, TRBF) co-simulation approach to assess running safety during earthquakes. Then, by comparing results from the TTBCS models both with and without track structure nonlinearity, this study clarified the effects of track structure nonlinearity on dynamic response and running safety assessment (RSA) indices. It was found that neglecting track structure nonlinearity during earthquakes can result in an overestimation of the Nadal, leading to an inaccurate RSA. Moreover, this effect intensifies as the seismic intensity increases. By explaining these results, this study elucidates the mechanism by which track structure nonlinearity affects running safety. Finally, this study specified the necessary seismic intensities and vehicle speed ranges needed to consider track structure nonlinearity, and established a comprehensive influence domain. This study reveals the effects of track structure nonlinearity on running safety during earthquakes, contributing to a more accurate RSA during earthquakes.

An Adaptive Cruise Control Algorithm Based on DDPG Algorithm Based on Deep Reinforcement Learning Under Variable Acceleration Conditions

Adaptive cruise control (ACC) dynamically regulates a vehicle's speed to preserve a secure gap from the preceding vehicle, enhancing road safety. In this study, ACC is examined through the lens of deep reinforcement learning, with a focus on the Deep Deterministic Policy Gradient (DDPG) technique. The reward function takes into account the speed error, and two modesspeed control and distance controlare implemented. The proposed ACC strategy is trained and validated through simulations on the MATLAB/Simulink platform. The experimental results indicate that the reward function converges rapidly, confirming the suitability of the DDPG algorithm for automotive ACC research.

Optimal Speed Ranges for Different Vehicle Types for Exhaust Emission Control

Controlling vehicle speed is crucial for reducing exhaust emissions and ensuring the sustainable development of road transportation. Currently, speed limits on expressways are primarily set from a safety perspective, with limited research addressing speed limits from an environmental protection standpoint. In this study, based on real-world vehicle experiments and a vehicle flow exhaust emission model, we investigated the exhaust emission characteristics of light passenger vehicles (categorized as M1) and freight vehicles (categorized as N, including N1-minivans, N2-light heavy-duty vehicles, N3-medium heavy-duty vehicles, and N4-large heavy-duty vehicles) both individually and in traffic flows at varying speeds. We take carbon monoxide (CO), nitrogen oxides (NOx), particular matter (PM), and hydrocarbons (HCs) as representative emission components. The emission rate ranking of typical exhaust factors differs between M1-light passenger vehicles and N-freight vehicles. For M1-light passenger vehicles, the order is CO > HC > NOx > PM2.5, while for N-freight vehicles, it is NOx > CO > PM2.5 > HC. Conversely, for freight vehicles, higher speeds correlate with increased exhaust emissions in general, although carbon emissions specifically decrease as the speed increases. The results indicate the following speed limits conducive to sustainable road transportation development and low exhaust and carbon emissions: 90–110 km/h for light passenger vehicles and 80–100 km/h for freight vehicles.

Analysis of Black Site and Black Spot at Jl. Daeng Manambon-Raden Kusno-Gusti Sulung Lelanang Section, Mempawah

Transportation plays a vital role in society by facilitating the movement of people and goods. However, it also brings challenges like traffic congestion and accidents. With urbanization and increased vehicles, road safety has become crucial, as accidents have significant economic and social impacts. Analyzing accident-prone areas helps identify necessary preventive measures. This research was conducted on the 10.1 km Jl. Daeng Manambon-Raden Kusno-Gusti Sulung Lelanang section in Mempawah. Primary data from field surveys includes road conditions, fittings, geometry, and vehicle speeds. Secondary data is traffic accident data (2018-2023) from the Mempawah City Police, analyzed using numerical methods.The Jalan Daeng Manambon-Raden Kusno-Gusti Sulung Lelanang corridor analysis reveals several accident-prone areas (Black Spots) with damaged road features and poor safety facilities. Key findings include deteriorating zebra crossings, speed traps, and traffic signs. High-risk accident locations were identified using Z-Score and Cusum methods. Recommendations include repairing roads, repainting markings, installing better signage, and maintaining clear sightlines. Future studies should focus on more specific interventions and monitoring to improve road safety, following Department of Settlements and Regional Infrastructure guidelines for effective accident reduction

Study on Propeller Positioning and Trajectory Control of Underwater Robots Based on Bayesian Inference and Multi-Objective Optimization

In ocean exploration, the positioning and trajectory optimization of underwater vehicles are critical research areas that directly impact the success of ocean resource exploration, environmental monitoring, and search and rescue missions. However, the complex and uncertain marine environment poses significant challenges. This study first determines the three-dimensional spatial position of the underwater vehicle, calculates the vehicle's speed in the marine environment through mechanistic analysis, traversing the initial position, velocity, acceleration, angular velocity and angular acceleration of the submersible, fits the motion trajectory using a random walk, and uses Bayesian inference to estimate maximum probability positions of submersibles and select the optimal initial deployment point. To better cope with real-world scenarios involving ocean currents and the diverse motion modes of underwater vehicles, this study further used an improved genetic algorithm to divide the motion trajectory of the submersible into a series of discrete path points, and constructed a multi-objective optimization model with the shortest search and rescue time and the maximum probability of successful search and rescue as the objective function to realize the precise positioning of the lost underwater vehicle.

A novel stochastic second-order macroscopic continuum traffic flow model for traffic instability

Dispersion of vehicle speed often causes traffic instability, which affects traffic efficiency and safety. Previous studies have demonstrated that fluctuations near the steady-state equilibrium of traffic flow can be attributed to the stochasticity of traffic system leading by large vehicle speed dispersion. However, the intrinsic mechanisms between fluctuations of traffic flow and vehicle speed dispersion need further discussion. Therefore, a novel stochastic extended speed gradient model based on the fast-and-low speed vehicles is proposed to describe their relationships in this paper. Firstly, a speed-dependent stochastic process is characterized for the stochastic deviation of velocity dispersion, and a stochastic high-order macroscopic traffic flow model is constructed for fast and low speed vehicles. Secondly, using Lyapunov stability theorem and its derivatives, the linear stability criterion of the proposed model is derived. The theoretical results also indicate that the steady-state equilibrium of traffic flow is profoundly affected by the traffic flow initial density, penetration rate of fast and low speed vehicles, and noise intensity. Hence, simulations are implemented from the above three parameters. It can be found that the critical values of initial density affecting traffic flow stability are pursued, and some detailed tendencies of traffic flow are explored for different parameters. Finally, the model is also calibrated and validated through real data, and the impact of fast-and-low-speed vehicles on traffic instability at ramp-merging area is analyzed. Numerical experiments show that the stochastic traffic flow model proposed in this paper well reproduces the traffic oscillations occurring in real traffic.

Analysing driver behaviour and crash frequency at railway level crossings using connected vehicle and GIS data

Railway level crossings (RLCs) pose unique safety challenges as crucial intersections between vehicular traffic and railways. This study examines the effects of driver behavior on the safety performance of RLCs (within a 150-meter radius) in New South Wales, Australia. Historical databases on crashes, train operations, and inventory for RLCs were integrated. Also, vehicle movement raw data, including acceleration, deceleration, G-force, and speed, were extracted using the connected vehicle data. Then, the driver’s harsh braking and stiff steering events were identified. A random effect Hurdle Poisson model was adopted to account for the excessive zeros and unobserved heterogeneity. We identified risk factors affecting the likelihood and number of crashes at RLCs across two severity levels. Results of the non-injury crashes suggested that street, active control types, harsh braking, and stiff steering were associated with a higher likelihood of zero crash observations, while train frequency showed the opposite effect. It was also revealed that multiple tracks contributed to the increase in the number of non-injury crashes. Interestingly, after crossing the hurdle, harsh braking at RLC is associated with more non-injury crashes. As for the injury crashes, active control and stiff steering were associated with a higher likelihood of observing zeros. Nevertheless, once crossed the hurdle, intersecting with a highway, having multiple rail tracks and harsh braking events show increasing effects on the frequency of injury crashes. The findings of this study emphasize the need for targeted driver education programs and a larger-scale implementation of active control measures. It is hoped that these actionable insights can assist policymakers in prioritizing safety interventions at RLCs.

A velocity adaptive steering control strategy of autonomous vehicle based on double deep Q-learning network with varied agents

Autonomous vehicle steering control is sensitive to the vehicle driving speed and traditional model-based approaches are limited by the accuracy of the control model in various driving speed scenarios. To address these challenges, this study proposes a model-free control strategy based on deep reinforcement learning (DRL). In this strategy, the improved double deep Q-learning network (DDQN) with varied agents is employed for steering control to minimize the tracking errors across varying speeds. According to the kinematic characteristics of the vehicle, a dynamic action space is applied to enhance the tracking capability at high speeds. Furthermore, to ensure the output of the agent is more stable, a velocity adaptive reward function is designed by incorporating an action penalty factor. The performance of the proposed strategy is evaluated through simulation and experimental comparisons with other existing algorithms at a double-lane change maneuver. The results demonstrate that the DDQN-based strategy can effectively adapt to various vehicle speeds and perform the tracking task more accurately and stably. Finally, the feasibility of this strategy is verified using an actual prototype vehicle.

Three-point adaptive preview steady-state compensation driver model based on articulated heavy vehicle

This article proposes a three-point adaptive preview steady-state compensation driver model based on articulated heavy vehicles (AHVs). Although AHVs have extremely high road transportation efficiency, they have poor driving stability and high accident risk during high-speed transportation due to their large weight, high center of mass and multi-body characteristics. However, there are few studies on intelligent driving control of AHVs. In order to improve the active safety performance of AHVs, a three-point adaptive preview driver model is designed in this article. Based on the driver model, a sliding mode steady-state compensation controller is added. The three-point adaptive preview driver is different from other driver models. Its characteristic is that the preview distance can be automatically adjusted according to the vehicle speed and road curvature. A driver model with three-point preview weighted deviation, preview time, current vehicle speed and vehicle state as input and front wheel angle as output is established. Considering the trailer state, the front wheel angle is compensated and corrected by sliding mode steady-state compensation controller. The simulation results show that the model can reasonably judge the relationship between the target path and the vehicle position, adapt to the dynamic adjustment mechanism of the preview distance, and has good tracking accuracy. The front wheel compensation angle provided by the sliding mode controller under high speed and extreme conditions can ensure that the AHVs can track the target trajectory while maintaining driving stability.

Adaptive Second-Order Sliding Mode Wheel Slip Control for Electric Vehicles with In-Wheel Motors

The influence of the external environment can reduce the braking performance of the electric vehicle (EV) with in-wheel motors (IWM). In this paper, an adaptive sliding mode wheel slip control method with a vehicle speed observer consideration is proposed, which enables the EV to accurately track the optimal slip ratio in various environments and improve braking performance. First, the braking system dynamics model is established by taking the EV with IWM as the study object. Second, a super-twisting sliding mode observer is used to estimate the vehicle speed, and a new adaptive second-order sliding mode controller is constructed to control the braking torque. Finally, co-simulation experiments are performed under different conditions based on Carsim and MATLAB/Simulink, and the proposed scheme is validated by comparison with three control methods. The experimental results show that the proposed scheme has better control performance, and both the safety and control quality of the EV is improved.

Dynamic Obstacle Avoidance Strategy for High-Speed Vehicles Via Constrained Model Predictive Control and Improved Artificial Potential Field

Abstract The paper proposes an automatic planning and control strategy to keep the automatic driving high-speed vehicles collision-free based on the improved artificial potential field (APF) and constrained model predictive control (MPC). Firstly, the road potential field satisfying the constraints of vehicle safe driving is constructed according to the driving characteristics of vehicles and road boundary conditions. Focusing on the influence of the speed and direction of the obstacle vehicle on the potential field, the dynamic obstacle vehicle potential field is established. Secondly, the road potential field and the dynamic obstacle vehicle potential field are incorporated into the objective function of the path planning module to establish a real-time path planner. Thirdly, the yaw stability constraints of the vehicle are established, which are added to the QP solver and updated in real time according to the current vehicle states, so as to establish the constrained MPC controller. In the end, the safety of this planning and control strategy for obstacle avoidance overtaking and the effectiveness of constrained MPC in improving vehicle stability are verified by comparative simulation analysis in multi-obstacle vehicles scenarios.

Performance-Oriented and Deformation-Constrained Dual-topology Metamaterial with High-Stress Uniformity and Extraordinary Plastic Property.

The study of classical mechanical metamaterials has overwhelmingly remained at the elastic stage, while the increase in extreme speeds of vehicles and aircraft has created an urgent need and demanding requirements for excellent plasticity performance. Although some plastically deformable metamaterials exist, high initial peak stresses, short plastic strokes, and low plastic stresses limit their applications considerably. Here, an ideal malleable large-deformation metamaterial featuring high-stress levels and stability is reported. A performance-oriented multidimensional performance expansion strategy is adopted to obtain the bionic triangular corrugation-based plate lattice (TCPL) metamaterial. Then, the deformation constraint strategy that TCPL is innovatively used as the main topology with lateral expansion and buckling inhibited by the inserted enhancing topology is proposed, thus obtaining the built-in dual-topology enhanced TCPL (ETCPL). The ETCPL is again substantially strengthened in stress uniformity with almost no gradient and mechanical properties with strain energy improved by 51.56%. They are much more robust than typical multicellular materials, with the largest performance enhancement reaching 18 667.19%. In addition, the strength-density performances of both metamaterials significantly exceed the predictions of Gibson-Ashby model up to 75.2% maximum. The unprecedented performance confirms that multidimensional performance expansion strategy and deformation constraint strategy have created new design guidelines for ideal high-performance plastic metamaterials.

Multi-parameter optimization based on a surrogate model for improving vehicle dynamics and reducing wheel wear in high-speed EMUs

With the continuous development of China's high-speed railways, the problems of vehicle dynamics and wheel wear in high-speed electric multiple units (EMUs) are becoming increasingly prominent, and the suspension parameters of these vehicles have a significant effect on improving vehicle dynamics performance and reducing wheel wear. This paper first establishes a vehicle dynamics model using the Ultra-Latin Hypercube sampling method to select suspension parameters, and targets the improvement of vehicle dynamics performance and the reduction of wheel wear, and finally a Kriging Surrogate Model-Compression Particle Swarm Optimization (KSM-CPSO) model used to optimise the suspension parameters. The vehicle dynamics and wheel wear of the before and after optimisation are analysed. The results showed that the use of optimised suspension parameters effectively increased the vehicle's critical speed. The optimised parameters further improved the vehicle's ride index, safety index, and reduced the lateral force of the wheel axle. In addition, the optimised parameters suppressed the lateral vibration of the vehicle. When the wear mileage reached 200,000 km, the wheel wear depths before and after optimisation were 1.086 and 0.9806 mm, respectively. Therefore, the optimisation of the suspension parameters can effectively improve the vehicle dynamics performance and subsequently reduce wheel wear.

Multi-population coevolutionary algorithm for a green multi-objective flexible job shop scheduling problem with automated guided vehicles and variable processing speed constraints

This study focuses on addressing a multi-objective Flexible Job Shop Scheduling Problem with Automated Guided Vehicles (FJSP-AGVs) and variable processing speed constraints. First, a position-based mixed integer linear programming model (MILP) is proposed to optimize simultaneously the maximum completion time and the total energy consumption. Then, we decompose FJSP-AGVs into four interrelated subproblems and design a Multi-Population Coevolutionary Algorithm (MCEA) to solve them. In MCEA, (1) The effective encoding and decoding methods are used to accurately reflect the characteristics of the problem, and generate feasible scheduling solutions. (2) A multi-rule-based heuristic is proposed to enrich the diversity of four populations. (3) A disjunctive graph is constructed to depict and obtain the critical path(s). On this basis, (4) two cooperative evolution strategies based on critical paths are proposed to facilitate collaborative evolution between different populations and improve the global search capability of the algorithm. Furthermore, (5) a consumption reduction strategy is proposed by reducing the processing speed of operations on non-critical paths while ensuring that it does not affect the makespan. Finally, we validate the effectiveness of MCEA by GD, and IGD, and set coverage metrics on the four typical benchmark datasets. Based on the average GD (IGD) metric across 65 instances, MCEA shows reductions of 77.63% (93.60%), 95.30% (97.27%), and 96.17%(97.89%) relative to EHA, EMOEA, and mop-BRKGA, respectively. The set coverage metric, MCEA outperforms EHA, EMOEA, and mop-BRKGA in 59, 64, and 64 instances, respectively. These results clearly indicate that MCEA can solve the FJSP-AGVs with variable processing speed constraints.

Dynamic analysis of orthotropic bridge deck under moving vehicular loads by using Hencky bar-net model

This study extends the Hencky Bar-Net model (HBM) to dynamic analysis of vehicle-orthotropic bridge deck interaction system. The HBM was used to investigate the dynamic behaviour of an orthotropic bridge deck under moving vehicular loads and random road irregularities. The results indicate that the lower bending rigidity in width direction change the deformations of orthotropic deck from a global mode into a localized one, and dynamic magnification factors do not necessarily increase with higher vehicle speeds. However, dynamic magnification factors demonstrate an obvious increase in relation to higher vehicle speeds when the bending rigidity ratio is high. In addition, the HBM can easily handle damage of the bridge deck because of its discrete characteristic. Hence, the common longitudinal cracks that keep open are modelled by reducing the spring stiffness in the HBM. The results demonstrate longitudinal cracks show a complex influence on dynamic magnification factors of the bridge deck depending on the crack depth and location.

Analysis of the Application of Underpass at the Cibiru Roundabout Using PTV VISSIM

Traffic congestion is a persistent global issue, particularly in densely populated cities like Bandung, Indonesia. The construction of an underpass offers a potential solution to reduce congestion at high-density intersections, such as the Cibiru Roundabout, which connects JL. Soekarno-Hatta, JL. Cibiru, and JL. A.H Nasution. This research analyzes traffic performance and assesses the feasibility of implementing an underpass at this location. A quantitative descriptive research method was used, with data collected through field measurements of vehicle volume and speed. The analysis followed the Indonesian Road Capacity Guidelines (PKJI) 2023 to evaluate capacity, degree of saturation, and service level. Using PTV VISSIM software, simulations were conducted to project traffic conditions over the next ten years, considering a 5% annual growth rate. Results indicate significant congestion, with service levels reaching "F" across various segments. Simulation outcomes suggest that an underpass would improve traffic flow by increasing capacity, reducing delays, and shortening queues. This study provides a valuable reference for urban planners seeking effective interventions in other cities facing similar traffic challenges.

Advancing autonomous vehicle safety assessment: A novel methodology for moving from functional to concrete scenarios using kinetic 3D-LiDAR and SHAP

The commercialization of autonomous driving systems is gaining momentum, yet ensuring safety remains an ongoing challenge. To address these safety concerns, autonomous vehicle safety assessment employs a scenario-based approach, which can be categorized into knowledge-based and data-driven methods. In this study, we propose a framework that extends existing data-driven approaches by addressing three critical limitations: data, method, and scenario development. Using actual driving data, including 3D LiDAR Point Cloud Data (3D-LiDAR PCD), we extracted kinetic properties such as vehicle speed, acceleration, and detected critical accident triggered vehicle (ATV). Subsequently, we employed SHAP (SHapley Additive exPlanations) to assess the importance of kinetic properties and set criteria for selecting the configuration value into scenario. Specifically, we used SHAP value to determine the optimal configuration values for each variable in concrete scenario. This study presents a comprehensive scenario development framework that not only overcomes data limitations but also provides a methodological foundation for developing scenarios that accurately reflect real world. It offers an innovative approach to addressing safety concerns in the commercialization of autonomous driving systems.

Quantitative Estimation and Analysis of Spatiotemporal Delay Effects in Expressway Traffic Accidents

Expressway traffic accidents often result in severe congestion, with their unpredictable nature complicating timely and effective response measures. This paper presents a comprehensive method for accurately estimating and analyzing the spatiotemporal delay effects of expressway accidents through the integration of multi-source geographic data. The innovation lies in utilizing real-world vehicle trajectory data, combined with a Traffic Performance Index (TPI), to quantitatively assess delay impacts. By applying spatial clustering and hotspot detection techniques, we investigate the distribution patterns of delays and further employ a Spatial Error Model (SEM) to examine the relationships between accident characteristics and associated delay effects. Using expressway accident data and vehicle trajectory records from Hunan Province, the results demonstrate that the TPI-based approach effectively captures the duration, extent, and severity of traffic delays. Moreover, significant correlations are identified between delay impacts and specific accident characteristics, such as accident type, road type, road environment, pre-accident vehicle speed, and secondary accidents. This approach provides traffic management authorities with actionable insights into the overall roadway impact, facilitating targeted emergency response strategies and informing road usage policies tailored to the characteristics of accident impacts, thus helping to mitigate future risks.

Dynamic multimedia transmission scheduling by unmanned aerial vehicle–mounted base stations for a vehicular ad hoc network

On highways, vehicle speed and topology changes are likely to cause transmission delays. In this study, a multimedia transmission system for highways is proposed; this system combines a next-generation cloud radio access network, which has a centralised architecture, with unmanned aerial vehicle (UAV)-mounted base stations (UAV-BSs), cellular network base stations, and roadside units. The deployment characteristics of the UAV-BSs are examined to improve the service of vehicular ad hoc networks and the quality of experience of users. A UAV location assignment algorithm and a transmission resource allocation algorithm for multiple bit rates, communication channels, and relay stations are proposed to reduce multimedia playback lag. We implemented a simulation programme to evaluate how the proposed algorithms outperformed other relevant methods. The simulation results indicate that the proposed algorithms achieve lower lag time than do existing demand- and transmission-based methods. The findings of this study have implications for the practical application of multimedia transmission decision support on highways.