Varying Vehicle Speed Research Articles

Researching The Dynamic Response Law of High and Steep Subgrade Slopes Using ABAQUS

The study aimed to investigate the dynamic response of vehicles traveling on expressways across high and steep subgrade slopes under vehicle loads. This was done by creating a finite element model of high and steep subgrade slopes through ABAQUS software and its secondary program development. Specific engineering examples were used to analyze the dynamic response states and laws of high and steep subgrade slopes under varying vehicle speeds and load conditions. The study's results indicate that: (1) When the vehicle is travelling at 100 km/h, its vibration frequency is close to the natural frequency of the subgrade in the section, leading to strong dynamic responses. (2) As the number of axles increases, the pressure on the contact surface between the wheel and the roadbed rises, and dynamic responses become stronger. The four-axle vehicle shows the most significant dynamic response. (3) The maximum vertical stress fluctuates around an intermediate value over time. (4) The highest maximum vertical displacement value is observed in the penultimate analysis step, with a noticeable peak value. At this point, the vehicle load has the most prominent impact on the roadbed slope.

Collaboration and resource sharing in the multidepot time-dependent vehicle routing problem with time windows

Concerns about energy conservation and emission reduction have highlighted the importance of environmentally sound logistics networks in urban areas. These networks are deeply intertwined with urban traffic systems, where variations in transit speeds can significantly increase the energy consumption and carbon emissions of delivery vehicles, compromising the environmental sustainability of urban deliveries. To address this, we propose a multidepot time-dependent vehicle routing problem with time windows, enhancing route planning flexibility and resource configuration. Our approach begins with a route spatiotemporal decomposition method to estimate vehicle travel times and emissions based on varying vehicle speeds. We then develop a multiobjective mixed integer linear programming model that aims to minimize total operating costs, the number of vehicles, and carbon dioxide emissions. A hybrid heuristic algorithm combining spectral clustering, multiobjective ant colony optimization, and variable neighborhood search is proposed to solve the model. This algorithm incorporates collaboration and resource sharing strategies, a pheromone initialization mechanism, a novel heuristic operator that accounts for time dependency, and a self-adaptive update mechanism, enhancing both solution quality and algorithm convergence. We compare the performance of our algorithm with that of the CPLEX solver, multiobjective ant colony optimization, non-dominated sorting genetic algorithm-Ⅲ, and multiobjective particle swarm optimization. The results demonstrate the superior convergence, uniformity, and spread of our proposed algorithm. Furthermore, we apply our model and algorithm to a real-world case in Chongqing, China, analyzing optimized results for different time intervals and vehicle speeds. This study offers robust methodologies for theoretically and practically addressing the multidepot time-dependent vehicle routing problem with time windows, contributing to the development of economical, efficient, collaborative, and sustainable urban logistics networks.

Pedestrian Behavior in Static and Dynamic Virtual Road Crossing Experiments

Virtual studies involving pedestrians have gained relevance due to the advantage of not exposing them to actual risk, and simulation setups have benefitted from rapid technical advancements, becoming increasingly complex and immersive. However, it remains unclear whether complex setups affecting participants’ freedom of movement impact their decision-making. This research evaluated the effects of a more realistic approach to studying pedestrian crossing behavior by comparing a perception-action task requiring participants to walk effectively along a semi-virtual crosswalk with a similar experiment using static crossing conditions. Using a CAVE system, two real-world streets were modeled in two different virtual scenarios, varying vehicle speed patterns and distance from the crosswalk. Visual stimuli were presented to two groups of 30 participants, with auditory stimuli adapted accordingly. The impact of various factors on participants’ crossing decisions was evaluated by examining the percentage of crossings, crossing start time, and time-to-passage. Overall, the experimental approach did not significantly affect participants’ crossing decisions.

Space-time graph path planner for unsignalized intersection management with a V2V agent coordination architecture

Reducing traffic congestion and increasing passenger safety are important objectives for emerging automated transportation systems. Autonomous intersection management systems (AIMS) enable large scale optimization of vehicle trajectories with connected and autonomous vehicles (CAVs). We propose a novel approach for computing the fastest waypoint trajectory in intersections using graph search in a discretized space-time graph that produces collision-free paths with variable vehicle speeds that comply with traffic rules and vehicle dynamical constraints. To assist our planner algorithm in decentralized scenarios, we also propose a multi-agent protocol architecture for vehicle coordination for trajectory planning using a vehicle-to-vehicle (V2V) network. The trajectories generated allow a much higher evacuation rate and congestion threshold, with lower O(N) algorithm runtime compared to the state of the art conflict detection graph platoon path planning method, even for large scenarios with vehicle arrival rate of 1/s and thousands of vehicles.

Analysis of wheel-rail contact under oil lubrication conditions

Abstract This investigation centers on the characteristics of the contact between the wheel and rail when lubricated with oil, with a consideration of the impact of surface roughness. Confronting significant challenges related to low adhesion, a thorough examination is undertaken to analyze the changes in film thickness under oil lubrication, investigating trends at varying vehicle speeds and axle loads. Employing detailed modeling based on micro-elastohydrodynamic theory, the distribution of oil film thickness under diverse operational conditions is determined. The findings indicate that, under oil lubrication, a film thickness in the range of several tens of micrometers is established, effectively insulating the surfaces of the wheel and rail, thereby reducing adhesion. With increasing vehicle speed, the thickness of the oil film also rises, whereas an elevation in axle load results in a marginal decrease in film thickness. This research contributes to a comprehensive comprehension of the wheel-rail contact mechanism in oil lubrication scenarios, providing theoretical backing to address concerns related to low adhesion. The implications are substantial for improving the safety and economic efficiency of efficient railway transportation.

Integrating vehicle trajectory planning and arterial traffic management to facilitate eco-approach and departure deployment

Eco-approach and departure (EAD) enable continuous vehicle motion in urban signalized corridors. Since such a motion can extend to the EAD vehicles’ followers, it makes EAD a promising technology to benefit the traffic flow where automated vehicles and conventional vehicles coexist. Most existing EAD studies envision an ideal setting that neglects real-world operational conditions such as lane changes, multi-movement intersection configuration, partially automated fleet, and/or limited traffic state awareness. This study aims to fill the gap by designing an EAD algorithm considering real-world traffic operation constraints. The proposed algorithm uses a model predictive controller to minimize vehicle speed reduction and variation based on the real-time traffic signal control plan and measured queues at the intersection. The required inputs are readily available at many modern intersections. We observed that the proposed controller’s performance might degrade because of lane-changing maneuvers and lead-left turn traffic signals. These observations motivated our development of a lane change management strategy and a signal control implementation strategy to facilitate the EAD implementation. The lane change management strategies separate the EAD operations and lane-changing maneuvers in time and space. The signal control implementation strategy applies lag-left turn signals to enable EAD operation for both the through and left-turn vehicles. Compared to the non-EAD case, our EAD approach produces 2.5% to 7.8% energy savings while keeping similar intersection mobility. Notably, this approach brings about 2.5% to 3.6% energy savings in a 2% CAV case. This result demonstrates the feasibility of deploying EAD at low connected automated vehicle penetration rates.

Quantitative Detection of Vertical Track Irregularities under Non-Stationary Conditions with Variable Vehicle Speed.

Track irregularities directly affect the quality and safety of railway vehicle operations. Quantitative detection and real-time monitoring of track irregularities are of great importance. However, due to the frequent variable vehicle speed, vehicle operation is a typical non-stationary process. The traditional signal analysis methods are unsuitable for non-stationary processes, making the quantitative detection of the wavelength and amplitude of track irregularities difficult. To solve the above problems, this paper proposes a quantitative detection method of track irregularities under non-stationary conditions with variable vehicle speed by order tracking analysis for the first time. Firstly, a simplified wheel-rail dynamic model is established to derive the quantitative relationship between the axle-box vertical vibration and the track vertical irregularities. Secondly, the Simpson double integration method is proposed to calculate the axle-box vertical displacement based on the axle-box vertical acceleration, and the process error is optimized. Thirdly, based on the order tracking analysis theory, the angular domain resampling is performed on the axle-box vertical displacement time-domain signal in combination with the wheel rotation speed signals, and the quantitative detection of the track irregularities is achieved. Finally, the proposed method is validated based on simulation and field test analysis cases. We provide theoretical support and method reference for the quantitative detection method of track irregularities.

Machine vision-based autonomous road hazard avoidance system for self-driving vehicles

The resolution of traffic congestion and personal safety issues holds paramount importance for human’s life. The ability of an autonomous driving system to navigate complex road conditions is crucial. Deep learning has greatly facilitated machine vision perception in autonomous driving. Aiming at the problem of small target detection in traditional YOLOv5s, this paper proposes an optimized target detection algorithm. The C3 module on the algorithm’s backbone is upgraded to the CBAMC3 module, introducing a novel GELU activation function and EfficiCIoU loss function, which accelerate convergence on position loss lbox, confidence loss lobj, and classification loss lcls, enhance image learning capabilities and address the issue of inaccurate detection of small targets by improving the algorithm. Testing with a vehicle-mounted camera on a predefined route effectively identifies road vehicles and analyzes depth position information. The avoidance model, combined with Pure Pursuit and MPC control algorithms, exhibits more stable variations in vehicle speed, front-wheel steering angle, lateral acceleration, etc., compared to the non-optimized version. The robustness of the driving system's visual avoidance functionality is enhanced, further ameliorating congestion issues and ensuring personal safety.

Dynamic Load Analysis of Mine-Truck Using Multibody Dynamic Analysis Method

Abstract: Vehicle load distributed on front & rear axle assembly during various working conditions is one of the most important factors influencing working performance and service life of drive axle assembly. The dynamic load on axle assembly is higher compared to the rated load because of variations in vehicle speed and road surface conditions. Early failure of structural components is caused because of higher dynamic reaction forces due to uneven road profiles. The dynamic load calculation of axle assembly is presented in this paper for a Mine-truck with various road conditions and vehicle speeds. The road load conditions consist of grade, bump & path hole. Mine-truck with payload capacities of 63 Ton is considered for the study. The behaviour of vertical force is calculated for different vehicle speeds (3 - 12 km/hr.) of the machine on the front & rear axle assembly during running conditions. Multi-body dynamic Analysis tool ADAMS is used for this study. ADAMS helps to study the dynamics of moving parts and load distribution throughout mechanical systems during motion. The Mine-truck3D model was created with the help of computer-aided design (CAD) software. Major units of Mine trucks are considered to prepare 3D models like Cabin-engine mass, Bucket, front axle assembly, rear Axle assembly & tires. Multi-body vehicle models are prepared in simulation software ADAMS by importing 3D models prepared by computer-aided design (CAD) software. This provides guidelines for selecting dynamic load factors while designing structural parts of the vehicle.

Detection of distracted driving through the analysis of real-time driver, vehicle, and roadway volatilities

Distracted driving adversely impacts drivers’ decision-making and leads to safety-critical events (SCEs). Early detection of driver distraction is critical to prevent traffic crashes by providing warning messages to drivers and the surrounding vehicles. This study harnesses real-time multidimensional data collected through sensors that examine the variations in driver biometrics, vehicle kinematics, and roadway surroundings in different driving scenarios conducted on a Multimodal Virtual Reality Simulator. The driving behaviors of the study participants were examined under various visual detection response tasks of increasing complexity. The study classifies driving behaviors on a 5-level ordinal scale by estimating a Panel Ordered Logit Model, Random Forest, and Artificial Neural Network, using real-time volatilities in driver biometric signals, vehicle speed and acceleration, and roadway surroundings. The study results reveal that the driver gaze and the coefficients of variation in vehicle speed, driver eye movements, vehicular distances from the lane centerline, and the following vehicle significantly impact distracted driving. The study’s findings align with the principles of the safe systems approach by emphasizing the development of proactive safety measures in the form of feedback and warning the driver and surrounding vehicles of a potential distracted driving event, helping to foster safer user behavior and vehicles.

Dynamics modeling of urban rail vehicle axle-box bearings with roller defect considering time-varying impact

Accurately simulating the vibration response of the axle-box bearing with a localized defect on the roller can guide the proposal of several health condition estimation methods. However, there is still a lack of discussion and analysis on the detailed motion process of the roller defect passing through the inner/outer race. For filling this gap, a roller defect model considering time-varying impact is proposed and applied to the vehicle–track coupling system considering axle-box bearings. In addition to the Hertz contact force due to the relative displacement between the roller and race, time-varying impact excitation is designed according to the law of the conservation of energy. At first, an onsite test and an experimental test are implemented to validate the accuracy of the roller defect model and the vehicle–track coupling model, respectively. Then, the effect of the roller defect on the vibration characteristics of the components in the vehicle system is investigated. Finally, the influence of the defect size and vehicle speed on the vibration response of the axle-box bearing is studied, and the change rule of the time-varying impact is also researched with the defect size. Besides, the axle-box vibration response is simulated under variable vehicle speeds, and order tracking technique is applied to analyze the simulated result.

Intelligent identification of moving forces based on visual perception

The task of identifying moving forces presents a challenging inverse problem for bridge health monitoring and condition assessment. Existing methods for identifying moving loads rely on complex structural response measurement equipment, resulting in inefficiency and elevated costs. To address these challenges, this study introduces an innovative intelligent identification method for moving forces on bridges using visual perception techniques. The proposed method utilizes cameras to capture displacement changes at specific points on the bridge surface to reconstruct moving forces in the time domain. Initially, numerical simulations were conducted to ascertain the feasibility and robustness of identifying moving forces based solely on the structural displacement response. This phase also investigates the influence of different measurement point combinations on identification accuracy. Subsequently, the accuracy of the proposed method in measuring the dynamic displacement response of the structure was carefully evaluated by an outdoor shaking table test. Furthermore, a meticulously designed simply supported beam model was fabricated, followed by the execution of precise vehicle-bridge coupling dynamic experiments. This phase rigorously validates the effectiveness and accuracy of the proposed moving force identification method at varying vehicle speeds. The obtained results substantiate the proposed approach's capability not only to comprehensively capture bridge response data for full-area monitoring but also to consistently and reliably identify information on moving vehicle forces. This study underscores a pioneering application of intelligent visual perception technology in the field of identifying moving forces. The introduced noncontact intelligent identification method is an effective solution for monitoring moving forces on bridges, which has a wide range of applications in the future.

TCV-D: An Approach for Path Selection in Vehicular Task Offloading

The vehicular task offloading technology enhances the transportation system by offloading tasks of task-requesting vehicles (TRV) to either vehicular fog nodes (VFN) or road-side units (RSU) using single-hop or multi-hop communication. Due to the limited availability of VFN/RSU within a single-hop of TRV, a multi-hop path is employed for offloading tasks from TRV to VFN/RSU. However, the multi-hop path selection approaches have several issues, such as frequent re-connections due to varying vehicle speed, lower successful offloading when task deadline is missed, increased outage time when no VFN/RSU is within communication range of TRV, and inefficient vehicle selection to offload task leading to longer path lifetime. To address these issues, we have proposed a novel approach called Time-Computation-Variance based deadline sensitive path selection (TCV-D), which considers contextual information from k-hop neighbors. The approach offers four offloading modes: Direct mode, RSU mode, VFN mode, and Search mode, depending on the availability of RSUs/VFNs in single and multi-hop scenarios. To ensure tasks are delivered within deadlines, the proposed approach executes tasks by task forwarding vehicle or neighbor of task forwarding vehicle instead of designated VFN/RSU if delivering the task directly to the destination would exceed the deadline constraint. Extensive result analysis demonstrates substantial improvements compared to the existing k-hop-limited offloading time-based path selection (k-hop-limited-OTS) approach, including a 60% reduction in re-connections, a 35% decrease in path life time, a 30% decrease in outage time, and an 84% increase in successful offloading ratio, approximately.

Link stability based multipath routing and effective mobility prediction in cognitive radio enabled vehicular ad hoc network

Vehicular ad hoc networks (VANETs) provide a robust infrastructure for intelligent transportation system (ITS) applications. VANET communication involves vehicle-to-vehicle and vehicle-to-infrastructure connections, primarily with roadside units (RSUs). Analyzing cognitive radio (CR)-VANET studies revealed two key performance issues: high energy consumption and latency. To address these challenges, we propose a novel approach: link stability and mobility prediction-based clustered CR-VANETs, known as LMCCR-VANET. LMCCR-VANET consists of four main components: CR-VANET construction, clustering model, speed-based mobility prediction, and link-based multipath routing. Initially, we establish cluster-based CR-VANETs to analyze and mitigate spectrum scarcity and power utilization problems in VANETs. Mobility prediction evaluates vehicle speed variations and predictions. Finally, employing link stability-based multipath routing (LSMR) in conjunction with the fuzzy interference model and ad hoc on-demand multipath distance vector (AOMDV) routing protocol ensures stable and efficient routing. Experimental results showcase the superiority of LMCCR-VANET. It exhibits enhanced energy efficiency, delivery rates, reduced energy consumption, end-to-end latency, and routing overhead when compared to recent works such as SCCR-VANET, CFCR-VANET, and MMCR-VANET.

Path tracking control of automated vehicles based on adaptive MPC in variable scenarios

AbstractFor complex and dynamic high‐speed driving scenarios, an adaptive model predictive control (MPC) controller is designed to ensure effective path tracking for automated vehicles. Firstly, in order to prevent model mismatch in the MPC controller, a tire cornering stiffness estimation algorithm is designed and a soft constraint on slip angle is added to further enhance the controller's precision in tracking trajectories and the vehicle's driving stability. Secondly, the improved particle swarm optimization (IPSO) method with dynamic weights and penalty functions is suggested to address the issue of insufficient accuracy in solving quadratic programming. Additionally, the standard particle swarm optimization (PSO) algorithm is used to seek the most suitable time horizon parameters offline to obtain the best time horizon data set under different vehicle speeds and adhesion coefficients, and then it is optimized online by an adaptive network‐based fuzzy inference system (ANFIS) to enhance the model predictive controller's adaptability in different operating conditions. Finally, simulation experiments are conducted under three different operating conditions: docked roads, split roads, and variable vehicle speeds. The results indicate that the designed adaptive MPC controller can accurately and stably track the reference trajectory in various scenarios.

Adaptive suspension state estimation based on IMMAKF on variable vehicle speed, road roughness grade and sprung mass condition

Vehicle speed, road roughness grade and sprung mass are the three main factors to influence suspension control and state estimation. Aiming at the problem that fixed state observer cannot guarantee the estimation accuracy of suspension with driving scenario changes, a suspension state observer based on interactive multiple model adaptive Kalman filter (IMMAKF) is established. Firstly, an adaptive control suspension is proposed based on LQR algorithm and multi-objective optimization algorithm, which can automatically adjust the controller parameters according to the vehicle speed, road roughness grade and sprung acceleration parameters, so as to keep the optimal control effect of the suspension. Secondly, the theoretical model of IMMAKF is derived, and two kinds of IMMAKF suspension state observers and controllers are established. Finally, a simulation condition with the vehicle speed, road roughness grade and sprung mass changing simultaneously is established. The simulation results shows that: compared with ordinary IMMKF, AKF and KF observers, the estimation accuracy of IMMAKF5 is improved. Except for state observation, IMMAKF can be used to identify the road roughness grade and estimate the suspension sprung mass.

The impact of vehicle parameters on road PM10 vehicle resuspended emissions: A case in South African low-income settlement

About 70% of the roads in low-income settlements are unpaved and close (≤15m) to residents, thus a major source of ambientand indoor PM10 concentrations. International studies have suggested that decreasing vehicle speed and managing vehicle type onpaved or unpaved roads can reduce vehicle dust emissions. These mitigation strategies should be examined before being adoptedand gazetted into South African air quality management plans. This study aimed to characterise roads and traffic, emphasisingdetermining the impact of vehicle type on PM10 emissions. GIS was used to determine the proportion of paved and unpaved roads. Atraffic counter was used to monitor vehicles and to determine traffic composition, diurnal cycles, and average speed per road type.Field campaigns (summer; 6 days; 15 hrs per day) were carried out in Bokamoso to monitor PM10 concentration usinga TSI DustTrak DRX® real-time optical aerosol counter. The Box Model method quantifies vehicle PM10 emission factors for heavy-duty, medium-duty, and motor vehicles. About 0.88 km of road within Bokamoso is paved with a daily traffic volume of >2000, and 3.6 km is unpaved with >250 daily traffic volume. Paved road heavy-duty vehicle non-exhaust PM10 emissions reported a positive medium to strong coefficient of determination to vehicle speed increase with a coefficient of determination of 0.59. Even though there is a positive coefficient of determination between heavy-duty vehicle non-exhaust PM10 emission factors and speed on unpaved roads, it is weak (0.2) due to low and less variable vehicle speed. Motor vehicle paved and unpaved road non-exhaust emission factors showed no significant coefficient of determination with increased vehicle speed. Paved road non-exhaust emission factors were not significantly variable with vehicle type. They ranged between an average of 0.15-0.18 g/km/h, with motor vehicles reporting an average of 0.18 g/km/h while heavy-duty vehicles reported an average of 0.15 g/km/h. On the contrary, unpaved road non-exhaust traffic PM10 emissions ranged between 0.16-0.3 g/km/h. The emission factors presented may be used to model vehicle traffic emissions, improve on-road vehicle dust emissions impact assessment and as a guide when deciding on local applicable road dust mitigation strategies

Adaptive path planning and tracking system based on model predictive control for autonomous vehicle local obstacle avoidance

AbstractPath planning and tracking control are the key technologies of autonomous vehicle. The planned path and tracking results affect driving stability and safety directly. In this paper, an improved local path planning method based on model predictive control is proposed to match the variation of vehicle speed and road adhesion coefficient. A two‐layer model predictive control (MPC) path planning and tracking system is further designed to validate the method and the simulation results show that the proposed solution solves the problem of excessive avoidance and reduces the lateral deviation with the reference path.

Methodology to Obtain the State Feedback Gains for Lateral Control of the Autonomous Vehicles

This paper introduces a methodology for deriving state feedback gains aimed at enhancing the lateral control of Autonomous Vehicles (AV). Autonomous Vehicles represent a groundbreaking technological advancement falling under the category of intelligent transportation systems. In developed nations, AVs are regarded as a potential solution to mitigate injuries resulting from road accidents. The AV system encompasses three distinct modules: Perception, Reference Generation, and Control. Among these, the Control module plays a pivotal role in ensuring the autonomy of the vehicle. Within the realm of existing literature, various lateral control systems have been documented. However, those relying on state feedback mechanisms have failed to provide a well-defined and systematic methodology for acquiring the requisite control gains. Furthermore, these approaches have not addressed the issue of varying vehicle speeds in the context of gain determination. Hence, the primary objective of this article is to present a comprehensive methodology for the systematic derivation of appropriate control gains for the AV lateral control system. The proposed methodology commences by positioning the system’s poles in accordance with the desired eigenvalues. Subsequently, the system is subjected to different sets of poles. Finally, an in-depth analysis of input and output data is conducted, with the aim of identifying the poles that most effectively minimize system errors.

Ultra-Reliable Deep-Reinforcement-Learning-Based Intelligent Downlink Scheduling for 5G New Radio-Vehicle to Infrastructure Scenarios.

Higher standards for reliability and efficiency apply to the connection between vehicle terminals and infrastructure by the fifth-generation mobile communication technology (5G). A vehicle-to-infrastructure system uses a communication system called NR-V2I (New Radio-Vehicle to Infrastructure), which uses Link Adaptation (LA) technology to communicate in constantly changing V2I to increase the efficacy and reliability of V2I information transmission. This paper proposes a Double Deep Q-learning (DDQL) LA scheduling algorithm for optimizing the modulation and coding scheme (MCS) of autonomous driving vehicles in V2I communication. The problem with the Doppler shift and complex fast time-varying channels reducing the reliability of information transmission in V2I scenarios is that they make it less likely that the information will be transmitted accurately. Schedules for autonomous vehicles using Space Division Multiplexing (SDM) and MCS are used in V2I communications. To address the issue of Deep Q-learning (DQL) overestimation in the Q-Network learning process, the approach integrates Deep Neural Network (DNN) and Double Q-Network (DDQN). The findings of this study demonstrate that the suggested algorithm can adapt to complex channel environments with varying vehicle speeds in V2I scenarios and by choosing the best scheduling scheme for V2I road information transmission using a combination of MCS. SDM not only increases the accuracy of the transmission of road safety information but also helps to foster cooperation and communication between vehicle terminals to realize cooperative driving.