Vehicle Motion Characteristics Research Articles

Analysis of mixed traffic flow characteristics based on cellular automata model under lane management measures

In the future, mixed traffic flow comprising Human-Driven Vehicles (HDVs), Connected and Autonomous Vehicles (CAVs), and CAV platoons will coexist for an extended period. Exploring the operational characteristics of mixed traffic flow under different lane management measures is essential for effective management and control. Initially, we analyze the motion characteristics of HDVs, CAVs, and CAV platoons and identify the car-following types within mixed traffic. Based on vehicle motion characteristics and the variance in maximum desired speeds among HDV drivers, we establish longitudinal motion rules for HDVs using a safety distance model. For CAVs and their platoons, we develop longitudinal motion rules by considering platoon merging and splitting behaviors, as well as speed and spacing error requirements for platoon driving. Subsequently, we formulate lateral lane-change rules based on the Symmetric Two-lane Cellular Automata (STCA) model, considering differences in reaction times and following distances between HDVs and CAVs. Finally, we conducted simulation experiments on a unidirectional three-lane highway using the multi-lane mixed traffic flow cellular automata model, analyzing the characteristics of mixed traffic under various lane management measures, such as mixed lane, CAV-dedicated lane, and CAV-priority lane. The results show that as the proportion of CAV (denoted as p) increases, the traffic flow capacity, optimal density, and jam density also increase. When p≤0.6, the k−q diagram exhibits a triangular shape, whereas for p>0.6, it assumes a trapezoidal shape. Implementing CAV-dedicated lane can reduce Cooperative Adaptive Cruise Control (CACC) degradation rates but only enhances traffic flow capacity when the CAV proportion reaches a certain threshold. Compared to the mixed lane scheme, when p≤0.3 and k≤0.3, the CAV-priority lane schemes not only meet traffic demands but also reduce CACC degradation rates. The vehicle speed in CAV-priority lane surpasses that in HDV lanes, facilitating improved traffic efficiency for CAVs. The distribution of maximum speeds among HDV drivers affects the fundamental diagram of mixed traffic flow and the performance of CAV-priority lane, with greater impacts observed as the standard deviation of the HDVs' maximum speed increases.

Research on anti-rollover control of three-axle rescue vehicle based on active suspension and differential braking

Abstract. Due to its intricate operational environment, substantial mass, and elevated center of mass, the three-axle emergency rescue vehicle is more prone to rollover. This study aims to enhance the operational stability and mitigate rollover risks by establishing a rollover dynamic model with 11 degrees of freedom. Using the coupling characteristics of vehicle dynamics and tire force, an anti-rollover control strategy of the three-axle vehicle based on the cooperative work of differential braking and active suspension is proposed. According to the vehicle motion characteristics, the active suspension controller is built by using a fuzzy PID algorithm, and the differential braking controller is built by using the improved adaptive model predictive control algorithm. The step and fishhook tests of differential braking, active suspension control and joint control are carried out respectively. The simulation results show that the joint control strategy can effectively reduce the rollover tendency of the vehicle, and further improve the anti-rollover ability of the vehicle compared with the single system control.

VMC-LIO: Incorporating Vehicle Motion Characteristics in LiDAR Inertial Odometry

VMC-LIO: Incorporating Vehicle Motion Characteristics in LiDAR Inertial Odometry

Research on the multiphase flow interference and motion characteristics of vehicles during an underwater salvo

A cavitation flow can greatly impact a vehicle's attitude and stability when leaving water. This paper adopts an improved delayed detached eddy turbulence model and Schnerr–Sauer cavitation model as well as the volume-of-fluid method and an overlapping grid technique to investigate this effect. The simulation method used for the cavitation model is validated. The interference effects of a transient multiphase flow, collapse loads, and the motion instability of vehicles during an underwater salvo are studied. The results show multiple obvious pressure peaks during the process of cavity collapse, which do not overlap significantly. Instead, they are sequentially arranged from the top to the end of the bubble, and the synchronous collapse pressure peak is much stronger than the other pressure peaks. The synchronous collapse pressure has a high peak and a short pulse width, and its action position is at the bottom of the shoulder cavity. The salvo time interval is zero, the launch depth is equal to the length of the vehicle, the initial cavitation number is 0.233, and the lateral launch spacing is varied from 2 times the diameter to 5 times the diameter. When the lateral spacing is in the range of 4 times the diameter to 5 times the diameter, the effect of flow interference on the underwater travel and water exit stages disappears.

A methodology of cooperative driving based on microscopic traffic prediction

We present a methodology of cooperative driving in vehicular traffic, in which for short-time traffic prediction rather than one of the statistical approaches of artificial intelligence (AI), we follow a qualitative different microscopic traffic prediction approach developed recently (Kerner and Klenov, 2022). In the microscopic traffic prediction approach used for the planning of the subject vehicle trajectory, no learning algorithms of AI are applied; instead, microscopic traffic modeling based on the physics of vehicle motion is used. The presented methodology of cooperative driving is devoted to application cases in which microscopic traffic prediction without cooperative driving cannot lead to a successful vehicle control and trajectory planning. For the understanding of the physical features of the methodology of cooperative driving, a traffic city scenario has been numerically studied, in which a subject vehicle, which requires cooperative driving, is an automated vehicle. Based on microscopic traffic prediction, in the methodology first a cooperating vehicle(s) is found; then, motion requirements for the cooperating vehicle(s) and characteristics of automated vehicle control are predicted and used for vehicle motion; to update predicted characteristics of vehicle motion, calculations of the predictions of motion requirements for the cooperating vehicle and automated vehicle control are repeated for each next time instant at which new measured data for current microscopic traffic situation are available. With the use of microscopic traffic simulations, the evaluation of the applicability of this methodology is illustrated for a simple case of unsignalized city intersection, when the automated vehicle wants to turn right from a secondary road onto the priority road.

Narrow Tilting Vehicle Drifting Robust Control

The narrow tilting vehicle receives extensive public attention because of traffic congestion and environmental pollution, and the active rolling motion control is a traffic safety precaution that reduces the rollover risk caused by the structure size of the narrow vehicle. The drifting motion control reflects the relatively updated attentive research of the regular-size vehicle, which can take full advantage of the vehicle’s dynamic performance and improve driving safety, especially when tires reach their limits. The narrow tilting vehicle drifting control is worthy of research to improve the driving safety of the narrow tilting vehicle, especially when tires reach the limit. The nonlinear narrow tilting vehicle dynamic model is established with the UniTire model to describe the vehicle motion characteristics and is simplified to reduce the computation of the drifting controller design. The narrow tilting vehicle drifting controller is designed based on the robust theory with uncertain external disturbances. The controller has a wide application, validity, and robustness and whose performance is verified by realizing different drifting motions with different initial driving motions. The narrow tilting vehicle drifting robust control has some practical and theoretical significance for more research.

Generation and Selection of Driver-Behavior-Based Transferable Motion Primitives

To integrate driver experience and heterogeneous vehicle platform characteristics in a motion-planning algorithm, based on the driver-behavior-based transferable motion primitives (MPs), a general motion-planning framework for offline generation and online selection of MPs is proposed. Optimal control theory is applied to solve the boundary value problems in the process of generating MPs, where the driver behaviors and the vehicle motion characteristics are integrated into the optimization in the form of constraints. Moreover, a layered, unequal-weighted MP selection framework is proposed that utilizes a combination of environmental constraints, nonholonomic vehicle constraints, trajectory smoothness, and collision risk as the single-step extension evaluation index. The library of MPs generated offline demonstrates that the proposed generation method realizes the effective expansion of MP types and achieves diverse generation of MPs with various velocity attributes and platform types. We also present how the MP selection algorithm utilizes a unique MP library to achieve online extension of MP sequences. The results show that the proposed motion-planning framework can not only improve the efficiency and rationality of the algorithm based on driving experience but can also transfer between heterogeneous vehicle platforms and highlight the unique motion characteristics of the platform.

Efficient Trajectory Matching Algorithm Based on Spatial Coordinate Rotation

Trajectory similarity estimation is the key to discover vehicle motion characteristics and trajectory classification, but the calculation of trajectory similarity is slow, and improving the speed of trajectory matching can help to carry out trajectory characteristics mining fast. Therefore, an efficient trajectory matching algorithm based on the rotation of spatial coordinate is proposed. Firstly, the trajectory curve is converted into the mean and variance curve which the number of points is equal to the number of rotations by multiple rotation of the spatial coordinate system. Then, Pearson correlation coefficient and Fréchet distance are used to measure the correlation of the mean and variance curves. Finally, the similarity between the original trajectories is determined indirectly according to the values of four parameters: Fréchet mean, Fréchet var, Pearson mean and Pearson var. Based on the taxi trajectory data of Hangzhou, under different trajectory sampling points and spatial coordinate rotation times, compared trajectory matching accuracy and speed with the traditional Hausdorff trajectory matching algorithm. The several experiments show that the algorithm can improve the trajectory matching speed by 85% on average while ensuring the accuracy of trajectory matching. By constructing a visual analysis system that includes three major modules: a map overview showing the results of trajectory matching, several visual interactive components that explore the differences in trajectory matching results, and a module for selecting trajectory matching parameters, explore the differences in the results of the four trajectory matching methods to help road network researchers to match real driving trajectories and find similar trajectory groups faster.

Video-based trajectory extraction with deep learning for High-Granularity Highway Simulation (HIGH-SIM)

High-granularity vehicle trajectory data can help researchers develop traffic simulation models, understand traffic flow characteristics, and thus propose insightful strategies for road traffic management. This paper proposes a novel vehicle trajectory extraction method that can extract high-granularity vehicle trajectories from aerial videos. The proposed method includes video calibration, vehicle detection and tracking, lane marking identification, and vehicle motion characteristics calculation. In particular, the authors propose a Monte-Carlo-based lane marking identification approach to identify each vehicle's lane. This is a challenging problem for vehicle trajectory extraction, especially when the aerial videos are taken from a high altitude. The authors applied the proposed method to extract vehicle trajectories from several high-resolution aerial videos recorded from helicopters. The extracted dataset is named by the High-Granularity Highway Simulation (HIGH-SIM) vehicle trajectory dataset. To demonstrate the effectiveness of the proposed method and understand the quality of the HIGH-SIM dataset, we compared the HIGH-SIM dataset with a well-known dataset, the NGSIM US-101 dataset, regarding the accuracy and consistency aspects. The comparison results showed that the HIGH-SIM dataset has more reasonable speed and acceleration distributions than the NGSIM US-101 dataset. Also, the internal and platoon consistencies of the HIGH-SIM dataset give lower errors compared to the NGSIM US-101 dataset. To benefit future research, the authors have published the HIGH-SIM dataset online for public use.

High-Accuracy Transient Fuel Consumption Model Based on Distance Correlation Analysis

Due to lack of a high-accuracy practical fuel consumption model, it is difficult to evaluate and control fuel consumption accurately. It is necessary to establish a more accurate and practical model according to transient motion characteristics of vehicles. To ensure accuracy of model, structural form of the steady-state basic + transient correction module is determined as overall structure of model. Then principal component analysis and cluster analysis are used to classify different driving conditions of vehicles reasonably. Then, based on distance correlation analysis, parameter combination which has strongest correlation with estimation error of steady-state is determined, and transient correction is established. After that, optimal transient correction structures are determined based on Bayesian criterion. Finally, model is tested and results show that mean absolute percentage error of fuel consumption estimation of new model is about 15%, which is lower than classical models. What’s more, new model is simple in structure and calculating speed is similar to VT-Micro model. Therefore, model not only has high accuracy, but also has high calculation speed.

Vehicle aerodynamic simulation analysis and research on bumpy road

PurposeIn order to certify the testing of vehicle Cd on the real road can be replaced by the much simplified steady-state simulation process inside wind tunnel.Design/methodology/approachThe paper opted innovative methods to verify the feasibility of using aerodynamic wind tunnel test for vehicle development purpose, including establishing the typical sinusoidal mapping function for road shapes during vehicle driving process to simplify the complexity of the road and choosing three different kinds of road spectrum amplitudes to establish the vehicle motion model, which can accurately predict the vehicle motion characteristics in the road driving process. The new approach simulates the vehicle aerodynamics on the road by taking into account the pitch angle changes during vehicle motion.FindingsBy comparing the transient drag coefficient on road with the steady-state simulated value in the conventional aerodynamic wind tunnel. This paper provides empirical insights about the flow field analysis around the vehicle on road that can verify the feasibility of using aerodynamic wind tunnel test for vehicle development purpose.Research limitations/implicationsBecause of establishing the typical sinusoidal mapping function for road shapes, the study results may lack generalizability. Therefore, researchers are encouraged to test the proposed propositions further.Practical implicationsThis paper includes implications for the flow field analysis around the vehicle on road that can verify the feasibility of using aerodynamic wind tunnel test for vehicle development purpose.Originality/valueThis paper fulfills an identified need to use aerodynamic wind tunnel test for vehicle development purpose.

A deep convolutional neural network based approach for vehicle classification using large-scale GPS trajectory data

Transportation agencies are starting to leverage increasingly-available GPS trajectory data to support their analyses and decision making. While this type of mobility data adds significant value to various analyses, one challenge that persists is lack of information about the types of vehicles that performed the recorded trips, which clearly limits the value of trajectory data in transportation system analysis. To overcome this limitation of trajectory data, a deep Convolutional Neural Network for Vehicle Classification (CNN-VC) is proposed to identify the vehicle’s class from its trajectory. This paper proposes a novel representation of GPS trajectories, which is not only compatible with deep learning models, but also captures both vehicle-motion characteristics and roadway features. To this end, an open source navigation system is also exploited to obtain more accurate information on travel time and distance between GPS coordinates. Before delving into training the CNN-VC model, an efficient programmatic strategy is also designed to label large-scale GPS trajectories by means of vehicle information obtained through Virtual Weigh Station records. Our experimental results reveal that the proposed CNN-VC model consistently outperforms both classical machine learning algorithms and other deep learning baseline methods. From a practical perspective, the CNN-VC model allows us to label raw GPS trajectories with vehicle classes, thereby enriching the data and enabling more comprehensive transportation studies such as derivation of vehicle class-specific origin-destination tables that can be used for planning.

Novel self-adaptive routing service algorithm for application in VANET

As a special MANET (mobile ad hoc network), VANET (vehicular ad-hoc network) has two important properties: the network topology changes frequently, and communication links are unreliable. Both properties are caused by vehicle mobility. To predict the reliability of links between vehicles effectively and design a reliable routing service protocol to meet various QoS application requirements, in this paper, details of the motion characteristics of vehicles and the reasons that cause links to go down are analyzed. Then a link duration model based on time duration is proposed. Link reliability is evaluated and used as a key parameter to design a new routing protocol. Quick changes in topology make it a huge challenge to find and maintain the end-to-end optimal path, but the heuristic Q-Learning algorithm can dynamically adjust the routing path through interaction with the surrounding environment. This paper proposes a reliable self-adaptive routing algorithm (RSAR) based on this heuristic service algorithm. By combining the reliability parameter and adjusting the heuristic function, RSAR achieves good performance with VANET. With the NS-2 simulator, RSAR performance is proved. The results show that RSAR is very useful for many VANET applications.

An experimental driving simulator study of unintentional lane departure

Driving characteristics of lane departure can provide design principles for lane departure warning system and lane keeping assist system. In this article, based on driving simulator experiments of unintentional lane departure, the operation performances of human drivers, the motion characteristics of vehicles, and the relative motion between the vehicle and lane line are synthetically studied. First, unintentional lane departure is classified into lane departure by fatigue and lane departure by secondary task. Subsequently, two simulator experiments of fatigue-based lane departure and secondary task–based are designed and performed to collect the synchronous driver–vehicle–road data. The data of steering angle, steering angle velocity, steering angle entropy, lateral acceleration, lateral velocity, and yaw velocity, under fatigue-based and secondary task–based lane departure are collected and compared with those gathered under normal lane changing. Results show that the characteristics of unintentional lane departures differ from that of normal lane departure changing. Furthermore, the characteristics of fatigue-based lane departures are shown some differences with that of secondary task–based ones.

Simulation and experimental research on trans-media vehicle water-entry motion characteristics at low speed.

The motion characteristics of trans-media vehicles during the water-entry process were explored in this study in an effort to obtain the optimal water-entry condition of the vehicle for developing a novel, single control strategy integrating underwater non-control and in-air control. A water-entry dynamics model is established by combining the water-entry motion characteristics of the vehicle in uncontrolled conditions at low speed with time-varying parameters (e.g. buoyancy, added mass). A water-entry experiment is designed to confirm the effectiveness of the established model. After that, by comparing the experimental results with the simulated results, the model is further modified to more accurately reflect water-entry motion. The change laws of the vehicle’s attitude and position during the water-entry process are also obtained by analyzing the simulation of the modified model under different velocity, angle, and angle of attack conditions. The results presented here have guiding significance for the future realization of reaching the stable underwater navigation state of the vehicle after water-entry process.

Vehicle Statistics and Retrograde Detection Based on Characteristic Analysis

Vehicle detection in the traffic monitoring system has been widely studied in recent years. This paper presents a vehicle detection method based on different object motion characteristics to track the moving vehicles. Firstly, we use the frame different method to detect the moving objects. Secondly, we do binarization and filtering processing with the adaptive threshold segmentation technique to extract the moving vehicles in the traffic video. Then we determine centroid displacement to recognize the vehicle motion characteristics for retrograde judgment. Finally, we draw the vehicle trajectory and make the vehicle motion statistics. Experiment results show that the method can real-time track the moving vehicles and accurately get the vehicle retrograde detection.

Detecting Research for Vehicles Track at Roundabout Based on Dual Data Source

Extracting vehicles track at roundabout is the basis research for motion characteristics of vehicles. At present, most of the vehicle trajectory was obtained by using single data source. However, the precision of the method needs to be improved. In this paper, two cameras are set at two different directions shooting the same scene image to obtain image data. And then a new method to extracted vehicles track more accurately by integrate the dual data source organically was proposed. In the case analysis, through the contrastive analysis of 4 different curves, the result shows that the error of dual data detection is decreased by 2.7% than single data detection. The method to extract vehicles track by dual data source can provide the theoretical support for the analysis of traffic conditions at roundabout.

Examination of Vehicle Motion Characteristics of a Maglev Train Set Using a Reduced-scale Model Experiment Apparatus

This paper focuses on the vehicle dynamics of superconducting magnetically levitated (Maglev) vehicles as a train set, and reports results of computer simulation as well as results of experiments using a reduced-scale Maglev model vehicle dynamics experiment apparatus (MAGMOX). The previously constructed MAGMOX consisting of a single car was expanded to form a train set comprising several cars. The computer simulations and MAGMOX experiments produced results that demonstrated their effectiveness in reproducing testing conditions of the full-scale Maglev system that are difficult to achieve on an actual full-scale vehicle due to vehicle structure constraints and rigging space.

Phase Plane Analysis for Vehicle Handling and Stability

Nonlinear stability analysis of phase plane is one of the most classic and effective methods. Based on established two degrees of freedom (2 DOF) vehicle model, combined with magic formula tire mode, phase plane analysis is used to study vehicle motion characteristics in various initial conditions. In addition, equilibrium point and phase plane trajectories changes are analyzed under the condition of traveling in a straight line, circular motion and sine wave input. The results denote that vehicle steady state can be well determined according to analysis changes of phase plane trajectory and state variables. Namely, phase plane analysis can be well used to analyze and evaluate the trend of variables and vehicle motion characteristics.

Design Method of Electric Power Steering Control based on Vehicle Motion Characteristics

In this paper, we propose a new Electric Power Steering (EPS) control and its design method. This control enables to transmit a vehicle motion information to a driver through a steering wheel. First, kinetic equations including characteristics of both EPS system and vehicle lateral motion are shown. Control-design policies are then derived from the frequency responses and the step responses of the vehicle equipped with EPS. In particular we regard the vehicle motion which is caused by a disorder of a lateral force balance between front and rear wheels as an important information that should be transmitted to a driver. A generalized plant is designed based on those equations and policies, and H-infinity controller is calculated from it. Finally, its effectivenesses are shown by vehicle experimental results.