Rear Of Vehicle Research Articles

Comparison of gap-based and flow-based control strategies using a new controlled stochastic cellular automaton model for traffic flow

ABSTRACT Autonomous vehicles are essential to future transportation systems, potentially reducing traffic congestion. This study examines the impact of different vehicle control strategies on traffic flow through simulations. We propose a novel stochastic cellular automaton model, the controlled stochastic optimal velocity (CSOV) model, which incorporates vehicle control effects. Within the CSOV model, two control strategies are implemented: gap-based control (GC), which adjusts vehicle velocity to balance the gaps between adjacent vehicles, and flow-based control (FC), which aims to maintain a consistent local flow between the front and rear vehicles. Results show that both control strategies improve traffic flow. However, under weaker control, the GC sometimes resulted in lower flow compared to no control. In contrast, the FC consistently enhanced flow across control strengths, yielding more robust outcomes. Furthermore, when both strategies achieved comparable flow rates, the FC provided a more stable velocity distribution under varying traffic densities than the GC.

A following model considering multiple vehicles from the driver's front and rear perspectives

In the current traffic environment dominated by manual driving, existing models of drivers' rear-view perceptions are inadequate. Existing car-following models that incorporate rear-view information are primarily focused on the Internet of Vehicles (IoV) and automated driving environments. However, they fail to realistically reflect the visual processing mechanisms of human drivers, limiting their effectiveness in realistic traffic scenarios. Therefore, we propose a new following model, the multi-vehicle influence from front and rear perspectives (MVFR), that considers the influence of multiple vehicles. The MVFR model combines information from both front and rear vehicles, integrating views from the front, side front and rear. It provides an in-depth analysis of the effects of relative state differences between a vehicle and its surrounding vehicles on speed, including the effects of perspectives in both the lateral and longitudinal directions. Linear stability analysis and numerical simulation demonstrate that considering the perspectives of rear-following vehicles and lateral offset angles can improve traffic flow stability to a certain extent. Furthermore, properly considering the lateral offset distance and the number of vehicles ahead also positively affects traffic flow stability. This study reveals that observing following vehicles and considering information from multiple front vehicles enhances system stability, especially when there is no or minimal lateral offset. In contrast, focusing on fewer front vehicles is more effective for traffic flow stability when there is a large lateral offset. Experimental results using the CKQ4up dataset show that the MVFR model achieves higher accuracy than the conventional FVD model, the front-view-only improved FVD model (MFVD-RV), and the MFRHVAD-RV and MFRHVAD-AV models. Compared with models relying solely on front-view or non-visual perception, the MVFR model demonstrates a better fit, validating the advantages of this full-view perception model in manual driving environments. This innovation addresses the shortcomings of existing research, thereby enhancing the reliability of models under manual driving conditions on highways.

Steering Mechanism and Capability of Forced Steering Device for Low-Floor Light Rail Train

For 70% low-floor trains on the light rail lines in Changchun, a multi-linkage forced steering device (FSD) is developed aiming at relieving the independently rotating wheelset (IRW) excessive flange wear and improving IRW bogie system running safety on curve line. This FSD employs guiding mode of steering four independently rotating wheels separately in a two-axle bogie, that is, four FSDs are employed to work together to realize the steering of a two-axle bogie. One side of an FSD is connected to the axle of an independently rotating wheel while the other side is attached to the front or rear vehicle body. When train passes through curve, the yaw angle between the IRW bogie and the front or rear carbody drives the linkage system to rotate to guide the independently rotating wheel towards its radial position. The coupled vehicle-track dynamics model for a 6-module single-type low-floor light rail train is developed and simulations of the train passing through tangent and curved tracks with and without FSD proposed are carried out. The results indicate that the FSD can improve the IRW restoring capability on tangent track and curve negotiation performance on whether sharp, medium, or large curved track. For example, passing through a curve with a radius of 200[Formula: see text]m, the IRW friction work has the biggest drop by 62.07% and the wheelset attack angle is second with a reduction rate of 55.02%. Adopting of larger steering stiffness, smaller steering clearance, and lever ratio greater than the ideal value can guarantee the higher steering ability of FSD. Besides, lower longitudinal stiffness of the trailer primary suspension is recommended because it can also enhance the steering ability of the FSD.

Multi-objective optimization for connected and automated truck platoon control with improved CACC model

Connected and Automated Truck Platoon (CATP) refers to a group of trucks traveling closely together with minimal spacing to improve fuel economy and safety. However, challenges arise from instability due to internal platoon factors and external traffic disturbances. This research presents an improved Cooperative Adaptive Cruise Control (CACC) model tailored for CATP to address these challenges. The model is designed to enhance safety, fuel efficiency, and traffic efficacy. The improvements of the proposed model are in two aspects: the optimizing of the time headway strategy and the dynamic parameter adjustments of controller based on multi-objectives. The Dynamic Safety Requirement Time Headway (DSRTH) strategy facilitates the timely detection of the accelerations of the leading vehicles within the platoon, enabling quick driving responses. Additionally, Model Predictive Control (MPC) enables dynamic calibration of Proportional-Derivative (PD) control parameters and issuance of velocity commands. Meanwhile, the integration of a second-order time-delay response model has been implemented to adapt to dynamic changes in commands. A transfer function has been established, and stability has been proven. To evaluate the model performance, simulation analysis was performed using real vehicle trajectories as the CATP following vehicles. The results indicate that the DSRTH strategy outperforms both the Constant Time Headway (CTH) and Variable Time Headway (VTH) strategies, allowing rear vehicles to reach the speed trough earlier, with response speeds improved by 3.1 % and 1.5 %, respectively. Compared to the Intelligent Driver Model (IDM) and CACC models, the improved CACC model achieves a steady state of constant acceleration sooner, with recovery times reduced by 17.7 % and 3.2 %. Additionally, compared to the IDM model, the improved CACC model can save 3.23 % in fuel consumption. Furthermore, sensitivity analysis indicates that as the CATP proportion and platoon size increase, there is a positive impact on traffic flow. However, when the platoon size exceeds 5 vehicles, it shows a negative impact on the stability of other vehicles in the traffic flow besides those in the CATP.

Train Trajectory-Following Control Method Using Virtual Sensors.

Trajectory-following control is the basis for the practical application of an articulated virtual rail train transportation system. In this paper, a planar nonlinear dynamics model of an articulated vehicle is derived using the Euler-Lagrange method. A trajectory-following control strategy based on the first following point is proposed, and a feedback linearization control algorithm is designed based on the vehicle dynamics model to achieve the trajectory following of the rear vehicle. Based on the target trajectory formed by the first following point and measured by virtual sensors, a vector analysis method grounded in geometric relationships is proposed to solve in real time for the desired position, velocity, and acceleration of the vehicle. Finally, a MATLAB/SIMPACK dynamics virtual prototype is established to test the vehicle's trajectory-following effectiveness and dynamics performance under lane change and circular curve routes. The results indicate that the control algorithm can achieve trajectory following while maintaining good vehicle dynamics performance. It is robust to variations in vehicle mass, vehicle speed, tire cornering stiffness, and road friction coefficient.

Real-time rear-end conflict prediction on congested highways sections using trajectory data

Predicting rear-end conflicts in advance can avoid potential crashes and significantly improve road safety, especially in congested road sections. Many existing studies adopt macroscopic aggregated traffic flow state features and or environment features for rear-end conflicts prediction, which seems to overlook the impact of the temporal trends of various features during the conflict process on the outcomes. Thus, this paper uses microscopic trajectory data of front and rear vehicles for conflict prediction and explored the impact of trajectory changes trend on conflicts formation. A Gated Recurrent Unit (GRU) is employed to learn and encode conflict and non-conflict trajectory data and perform binary classification. The model has a 93 % recall and a 1.41 % false alarm rate. The Local Interpretable Model-agnostic Explanations (LIME) tool also explains the relationships between predicted conflict probability and input microscopic trajectory data. From the time analysis of the input trajectory using LIME, the following conclusions can be drawn. In congested road segments, when the speed of the leading vehicle is below 3 m/s and the speed of the following vehicle is above 4 m/s, it has a significant positive effect on the occurrence of conflicts. And some aggressive acceleration behaviors of drivers have the positive effect also. In addition, the reasons for conflicts among most vehicles are identical Because their feature distributions are similar. These findings can provide targeted insights for the management of ATM in congested road segments.

Drive-By Identification of Joint Bridge Damage and Surface Roughness Based on Sensitivity Analysis of Residual Contact Point Deflections

Drive-by inspection of bridge damage using a passing vehicle’s dynamic response has great potential in bridge damage identification. Among the current drive-by methodologies, the methods based on bridge modeling have been proposed for the quantitative identification of bridge damage and bridge surface roughness. However, the coupling of bridge damage and unknown surface roughness increases the difficulty of the identification problem and most previous studies conduct the identification task of bridge damage or bridge surface roughness separately. In this paper, a novel method is proposed for the identification of joint bridge damage and bridge surface roughness based on the residual bridge deflections from the front and rear wheels contact points of an instrumented passing vehicle. The vehicle–bridge interaction is analyzed using a half-car model of a four degrees of freedom (4-DOF) with front and rear vehicle wheels. First, the vehicle–bridge unknown forces and displacement of the vehicle axles are identified by the generalized Kalman filter under unknown input (GKF-UI) proposed by the authors. The sensors can be installed conveniently on the vehicle body due to GKF-UI. Then, the residual bridge deflections from the front and rear vehicle wheels contact points are utilized to eliminate the effect of road surface roughness. Afterward, bridge structural damage is identified based on the sensitivity analysis of residual bridge deflections from the front and rear contact points with [Formula: see text]1-norm regularization. Finally, bridge road surface roughness is estimated based on the identified unknown forces and updated bridge model with damage. The results of numerical identification examples demonstrate the effectiveness of the proposed method for the identification of joint bridge damage and surface roughness.

Optimization of wheel brow structures of a van considering rainwater pollutants based on discrete phase model

Four optimization models were proposed to reduce the impact of pollutants from vans on themselves and surrounding vehicles. The adsorption of pollutants on the body of the van in rainy conditions and the diffusion of pollutants into the external environment were numerically investigated using the Discrete Phase Model (DPM) for vans having different wheel brow structures. The pressure distribution on the surface of the van, the surrounding flow field, and the raindrop distributions of various external positions on and around the van were analyzed and compared. The results indicated that the van with externally covered wheel brow model was most effective in inhibiting the diffusion of rainwater pollutants from the van under rainy conditions. Compared with the original model, the reduction in the polluted area was 11.19%, the reduction in the influence of the visual field on the rear vehicle was 25.4%, and the reduction in the diffusion of pollutants on the bodies of the side vehicle was 20.7%.

Evaluation of Vehicle Lateral and Longitudinal Dynamic Behavior of the New Package-Saving Multi-Link Torsion Axle (MLTA) for BEVs

To increase the package space for the battery pack in the rear of battery electric vehicles (BEVs), and thus extend their driving range, a novel rear axle concept called the multi-link torsion axle (MLTA) has been developed. In this work, the kinematic design was extended with an elastokinematic concept, and the MLTA was designed in CAD and realized as a prototype. It was then integrated into a B-class series-production vehicle by adding masses in different locations of the vehicle to replicate the mass distribution of a BEV. Both objective and subjective vehicle dynamic evaluations were conducted, which included kinematic and compliance tests, constant-radius cornering, straight-line braking, and a frequency response test, as well as subjective evaluations by both expert and normal drivers. These test results were analyzed and compared to a production vehicle. It can be concluded that the vehicle dynamic performance of the MLTA-equipped vehicle is, overall, 0.67 grades lower than that of the comparable production vehicle on a 10-grade scale. According to OEM experts, this deficit can be eliminated by tuning the different components of the MLTA and meeting the tolerance requirements of series production vehicles.

Finite element analysis and experimental study of strain for a vehicle rear suspension

In this paper, aspects of the study of the durability of rear suspension components of a vehicle are discussed. These aspects are addressed in two ways, namely the study by CAD modelling and finite element method simulation, and secondly by experimental analysis on a durability test bench. In order to develop these research problems, an experimental stand is virtually modelled and studied by simulation using ADAMS software. Results are obtained that characterize the operation of the test bench system kinematically and dynamically. The studies are then completed by finite element analysis of the dependent rear axle and helical springs in the suspension structure. These results are corroborated with those obtained experimentally, i.e. stresses and strains recorded on the stand. The usefulness of these studies is that they allow the validation of different components of the rear suspension in terms of fatigue strength and different types of rear axles from cars can be tested, as the stand has a modular construction and the test parameters can be adjusted.

Autonomous-Vehicle Intersection Control Method Based on an Interlocking Block

Non-signalized intersections have only ever been suitable for low traffic flow; however, with the development of autonomous driving technology and new control methods, the operation efficiency of this kind of intersection may be improved. In view of the shortcomings of existing non-signalized intersection control methods in multilane situations and inspired by railway trains, an interlocking-block intersection control model is proposed. In this study, vehicles between parallel lanes are combined into a few combos, and the combo shape can be determined according to a pairing model and the interlocking angle range, and the gaps between the front and rear vehicles are simulated as blocks in a railway system, which are added into the intersection control model as virtual blocked cars (VBCs) for optimization. In setting the optimization objectives, the connotation and realization of fairness are discussed. Experimental results show that compared with signalized intersections, roundabouts, and non-signalized intersections without control, the interlocking-block intersection control model greatly reduces vehicle delay. Compared with an existing model, the calculation speed in a multilane situation has been greatly improved, while the vehicle delay is similar.

IRS-assisted vehicular visible light communications systems: channel modeling and performance analysis.

Visible light communications (VLC) is a promising solution as an alternative for the fully occupied radio frequency bands in the near future. The rear (tail) and front of vehicles have lamps that can be used for vehicular visible light communications (VVLC) systems. However, one of the main challenges of VLC systems is the line-of-sight (LoS) blockage issue. In this paper, we propose the installation of intelligent reflecting surfaces (IRSs) (i.e.,smart mirrors) on the back of vehicles to overcome the issue in VVLC systems. We assume three different patterns of angular distribution for the radiation intensity: a commercially available LED with an asymmetrical pattern (Philips Luxeon Rebel), a symmetrical Lambertian pattern, and an asymmetrical Gaussian pattern. In the first section of this paper, we obtain the channel model for the IRS-assisted VVLC systems, then we investigate the path loss results versus link distance under different conditions such as weather type (clear, rainy, moderate fog, and thick fog) and radiation patterns. Moreover, the impact of system parameters such as the aperture size of the photodetector (PD), side-to-side and front-to-front distances, the number of IRS elements, and the IRS area are studied. In the second part, we derive a closed-form expression for the maximum achievable link distance versus the probability of error for the IRS-assisted VVLC systems. In addition, in this section we analyze the impact of the parameters in a single-photon avalanche diode (SPAD), background noise, and the system parameters for the path loss.

Characteristics of Suspended Road Dust According to Vehicle Driving Patterns in an Urban Area and PM10 Content in Silt

Characterizing the influence factors of exhaust gas based on the suspended road dust on paved roads, according to the number of vehicles and their distance with regard to driving pattern, is important in order to provide a coefficient for driving patterns to find a model equation. This has been a limitation of previous studies, in which this was difficult to carry out in a large area reflecting various driving patterns because some sections were selected according to empirical measurement results, and only one vehicle measurement was used to find the level of road dust. This study measured the concentration of suspended road dust that could occur, depending on the vehicle’s driving patterns, on an experimental road in Yongin, South Korea, from May to July 2023. The study was conducted to determine the degree of the effect of exhaust gas, according to the concentration of suspended road dust generated, by determining the separation distance based on real-time measurements. This study attempted to determine the changes in suspended road dust based on driving patterns in urban areas and factor in the concentration of suspended road dust with regard to emission characteristics in terms of exhaust gas and particulate matter with a diameter of 10 microns or less (PM10). This was in accordance with conditions evaluated using mobile laboratories, based on suspended-PM10-concentration-measuring equipment. This study mainly focused on the following main topics: (1) increasing the level of suspended particulate matter at less than 10 m intervals produced by exhaust gas; (2) decreasing the level of suspended road dust with an increase in the number of vehicles, with the area measured at a distance of three cars in front showing the lowest level of suspended road dust in the air and a low level for the rear vehicle; (3) demonstrating that PM10 is effective in measuring the generation of suspended road dust; and (4) evaluating suspended road dust levels by road section. Based on the results, this research is necessary to more appropriately set the focus of analyses that aim to characterize suspended road dust according to exhaust gas and PM10 content in silt.

Hierarchical optimization control strategy for intelligent fuel cell hybrid electric vehicles platoon in complex operation conditions

Encountering complex traffic conditions such as the dynamic interference of preceding and rear vehicles and gradients, a control strategy that simultaneously considers inter-vehicle cooperative control and energy economy is one of the key technologies, that improves traffic efficiency and exploits the energy-saving potential of platoon vehicles. In this paper, a hierarchical optimization control strategy is proposed for the intelligent fuel cell hybrid electric vehicles (FCHEV) platoon in a network-connected environment. The hierarchical control framework consists of upper speed control and lower power distribution. In the upper layer, the improved particle swarm optimization (PSO) algorithm is applied to calculate the global optimal speed trajectory, and then the model predictive control (MPC) is adopted for global speed trajectory tracking and self-adaptation, which can ensure the ego vehicle tracks the pre-calculated speed trajectory, and can re-plan the vehicle speed under the condition of safety priority when sudden disturbances occur in the foreground. The lower layer utilizes Q-learning (QL) to achieve power distribution between hybrid power sources, reducing the number of fuel cell starts and stops, slowing battery degradation and improving vehicle economy. Simulation results based on complex road conditions show that the proposed controller has good energy economy and tracking performance. Under the condition of the slope, the total platoon cost of the proposed strategy is reduced by 3.99% compared with the constant speed cruise strategy, and under the interference condition, the total platoon consumption cost of the proposed strategy decreased by 6.79% compared with adaptive cruise control (ACC).

Model for Predicting Lateral Shifts and Driving Style of Motorized Two-Wheelers

The compact sizes, lack of available physical protection, and complex maneuvering patterns of motorized two-wheeler (MTW) riders make them more vulnerable to crash risks and accidents. Considering the increased vulnerability of MTWs in dense urban mixed traffic environments, a proper evaluation and modeling of their lateral movement decisions and driving style can enhance safety associated with the riders and augment the reliability of the existing microsimulation models. Utilizing trajectory data of a six-lane divided urban arterial, the current study attempts to investigate the lateral movement (lateral shift) tendency of MTWs, addressing different influential variables that may affect riders’ decisions and describing how this choice is affected by prevailing traffic conditions. Based on several driving style parameters, such as change in speed, change in angular position, and longitudinal gap maintained with rear vehicles, this study further contributes to the existing literature by proposing a new index for identifying the driving style of riders during the lateral shifting process. Modeling results of a multinomial logit model indicated the importance of considering longitudinal and lateral gaps, vehicle speeds, and lateral fluctuations made by the subject MTW in the past trajectories in modeling the lateral shift decisions of riders. Considering the number of lateral fluctuations as an indicator of MTWs’ driving style, a new “aggressiveness index” is defined and, accordingly, a modeling approach is proposed to classify aggressive and non-aggressive driving styles of the riders. The results suggest that the history of past trajectories of the subject MTW during lateral shifting should be considered, and consideration of a non-linear relationship among the parameters can result in a better classification of driving style of MTW riders.

Brake light detection of vehicles using differential evolution based neural architecture search

Recognition of brake light status in vehicles is crucial for anticipating speed changes and preventing rear-end collisions for autonomous driving systems. Existing literature presents two types of methods for brake light detection: hand-crafted feature-based methods and deep learning-based methods. However, hand-crafted methods often struggle to capture brake light characteristics accurately in real-life conditions. In contrast, deep learning-based systems can adapt to diverse brake light variations across different vehicle types and environments. Nevertheless, manually designing Deep Neural Network (DNN) models requires expertise and is prone to errors. To address this limitation, we propose a novel approach that leverages Neural Architecture Search (NAS) to automatically generate optimal DNN architectures for object detection tasks, specifically for brake light detection. In contrast to the existing NAS approaches that focus on classification models, our technique explores NAS for object detection tasks. We employ a modified Differential Evolution algorithm, incorporating evaluation correction-based selection for mutation and species protection-based selection to identify the optimal DNN backbone architecture with optimal training parameters. The proposed approach achieved mean accuracy of 89.73 %, and 88.90 % on four-wheeler datasets CaltechGraz and UC Merced Vehicle Rear Signal datasets, respectively, and it has achieved 97.97 % on the proposed two-wheeler NITW-MBS dataset. The proposed approach’s generalization capability and practical applicability are ascertained through cross-dataset evaluation and experiments on real-world traffic video.

O1C1-04 Study on Retroreflective Material Contour Affixing to the Rear of Large Vehicles

O1C1-04 Study on Retroreflective Material Contour Affixing to the Rear of Large Vehicles

Detection of Pedestrians in Reverse Camera Using Multimodal Convolutional Neural Networks.

In recent years, the application of artificial intelligence (AI) in the automotive industry has led to the development of intelligent systems focused on road safety, aiming to improve protection for drivers and pedestrians worldwide to reduce the number of accidents yearly. One of the most critical functions of these systems is pedestrian detection, as it is crucial for the safety of everyone involved in road traffic. However, pedestrian detection goes beyond the front of the vehicle; it is also essential to consider the vehicle's rear since pedestrian collisions occur when the car is in reverse drive. To contribute to the solution of this problem, this research proposes a model based on convolutional neural networks (CNN) using a proposed one-dimensional architecture and the Inception V3 architecture to fuse the information from the backup camera and the distance measured by the ultrasonic sensors, to detect pedestrians when the vehicle is reversing. In addition, specific data collection was performed to build a database for the research. The proposed model showed outstanding results with 99.85% accuracy and 99.86% correct classification performance, demonstrating that it is possible to achieve the goal of pedestrian detection using CNN by fusing two types of data.

Analyzing Driving Safety on Prairie Highways: A Study of Drivers’ Visual Search Behavior in Varying Traffic Environments

This study aimed to investigate disparities in drivers’ visual search behavior across various typical traffic conditions on prairie highways and analyze driving safety at the visual search level. The study captured eye movement data from drivers across six real-world traffic environments: free driving, vehicle-following, oncoming vehicles, rear vehicles overtaking cut-in, roadside risks, and driving through intersections, by carrying out a real vehicle test on a prairie highway. The drivers’ visual search area was divided into five areas using clustering principles. By integrating the Markov chain and information entropy theory, the information entropy of fixation distribution (IEFD) was constructed to quantify the complexity of drivers’ traffic information search. Additionally, the main area of visual search (MAVS) and the peak-to-average ratio of saccade velocity (PARSV) were introduced to measure visual search range and stability, respectively. The study culminated in the creation of a visual search load evaluation model that utilizes both VIKOR and improved CRITIC methodologies. The findings indicated that while drivers’ visual distribution and transfer modes vary across different prairie highway traffic environments, the current lane consistently remained their primary area of search for traffic information. Furthermore, it was found that each visual search indicator displayed significant statistical differences as traffic environments changed. Particularly when encountering roadside risks, drivers’ visual search load increased significantly, leading to a considerable decrease in driving safety.

Research on key technologies for connected vehicle autonomous driving based on 5G big data

Abstract In recent years, with the improvement of computers, automation, and communication technologies, autonomous driving has developed rapidly and has become a research hotspot in transportation. In order to optimize the existing autonomous driving scheme, this paper investigates the key technologies in 5G-based Telematics autonomous driving, mainly including the millimeter wave communication method and automatic obstacle avoidance strategy design, and tests and analyzes them through simulation experiments. In the simulation experiment, the synchronization rate of rear vehicle 1 of lane X 1 is 97.56%, that of rear vehicle X 2 is 98.43%, and that of rear vehicle X 3 is 97.82%, with an average synchronization rate of 97.94%. The synchronization rates of rear vehicle Y 1, Y 2 and Y 3 of lane 2 are 98.27%, 97.84%, and 96.89%, respectively, with an average synchronization rate of 97.67%. For the local observation latency in Telematics, the 5G Big Data-based scheme reduces 10.22% on average compared to the F-DDQL scheme and 9.76% on average compared to the IF-DDQL scheme. Regarding system latency, the 5G Big Data-based scheme reduces 8.67% and 9.21% on average compared to the other two schemes, respectively. The 5G Big Data-based Telematics autopilot can significantly improve the synchronization rate of vehicles and effectively reduce network latency. The research on the key technologies of 5G big data-based connected vehicle autonomous driving in this paper can overcome the shortcomings of traditional autonomous driving technology with unstable networking and help reduce the reliance on high-precision sensors, thus further improving autonomous driving performance.