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Accurate vehicle acceleration prediction is useful for developing reliable Advanced Driving Assistance Systems (ADAS) and improving road safety. The existence of driver heterogeneity magnifies the variations in acceleration data, leading to consequential impacts on the precision of vehicle acceleration prediction. However, few studies have fully considered the driver heterogeneity when predicting vehicle acceleration. To model the characteristics of individual drivers, this study first identifies the driving behavior semantics which is defined as the underlying patterns of driving behaviors. The analysis results from the coupled hidden Markov model (CHMM) are used to evaluate the driving behavior differences between different drivers by Wasserstein distance. Then the convolutional neural network (CNN) and long short-term memory (LSTM) network are applied to predict vehicle acceleration. To validate the accuracy of the proposed prediction framework, vehicle acceleration data in car-following conditions is extracted from the safety pilot model deployment (SPMD) dataset. The segmentation results indicate that the CHMM possesses a robust capacity for modeling driving behavior. The prediction results demonstrate that the proposed framework, which incorporates driver clustering before prediction, significantly improves the accuracy of predictions. And the CNN-LSTM outperforms the LSTM in predicting vehicle acceleration during car-following scenarios. The findings from this study can enhance the development of personalized functionalities within ADAS to promote its deployment, thereby improving its acceptance and safety. [ABSTRACT FROM AUTHOR]

Research on evaluating highway bridge performance through vehicle-bridge interaction (VBI) analysis has made significant advancements. However, when assessing driving comfort, using vehicle acceleration instead of human acceleration poses challenges in accurately representing comfort. First, the paper proposes a finite element analysis method for human-vehicle-bridge spatial interactions (HVBSIs). Then, the importance of wheel path roughness difference is explored when assessing driving comfort. Furthermore, a new method for evaluating driving comfort that includes human and vehicle vibration responses has been proposed, and a simulation example of the steel-concrete composite beam bridge (SCCBB) is used to verify the effectiveness of the proposed method. The results demonstrate that the HVBSI analysis method effectively simulates the interconnected vibrations of the human body, the spatial vehicle model, and the three-dimensional (3D) bridge model. Differences in wheel path roughness significantly impact the roll vehicle vibration responses, which are crucial in driving comfort analysis. The driver's body vibration response is essential for evaluating driving comfort, and its inclusion leads to increased comfort indices values. In comparison to traditional methods, the overall vibration total value (OVTV) increases by a maximum of 109.04%, and the level of weighted vibration ( L e q ) increases by a maximum of 6.74%. This leads to an upgrade from grade IV to grade V in terms of comfort level, indicating a reduced comfort. [ABSTRACT FROM AUTHOR]

Vehicle antilock systems play a very important role in the stability and reliability during vehicle braking. Due to the complexity of the braking process, antilock braking system (ABS) usually face the problems such as nonlinearity, time-varying, and uncertain parameter modeling. Thus, aiming at the parameter model uncertainty problem of ABS, an adaptive neural network sliding mode controller (ADRBF-SMC) is designed in this paper. On this basis, establishing the quarter-vehicle model and the seven-degree-of-freedom vehicle model, and treating the difference between the two models as a kind of disturbance, carrying out vehicle braking performance simulation experiments to analyze the variation of braking performance parameters such as vehicle and wheel speeds, slip ratio, braking distance, braking torque, under the three cases of adaptive neural network sliding mode controller, traditional sliding mode controller, and no control. Simulation results show that the adaptive neural network sliding mode controller (ADRBF-SMC) proposed in this paper can play an effective control role in both vehicle dynamics models. In addition, the control method proposed in this paper has stronger anti-interference capability and higher robustness compared with the sliding mode controller (SMC). [ABSTRACT FROM AUTHOR]

To describe the microtrajectory change behavior of two-wheeled vehicles when encountering nonrigid obstacles, this paper proposes an obstacle avoidance model of two-wheeled vehicles for nonrigid obstacles based on the improved social force method. In the model, the obstacle avoidance characteristics of two-wheeled vehicles for nonrigid obstacles and the safety operation requirements of two-wheeled vehicles are considered. The calculation methods of the minimum boundary distance of avoidance and the boundary avoidance force range edge function are proposed based on the ultimate turning angle and boundary avoidance characteristics, respectively. The speed control and direction control behavior of the rider to the vehicle is abstracted as the form of social force, and the two-wheeled vehicle/rider individual is subjected to the avoidance force and correction force perpendicular to the direction of the longitudinal axis of the vehicle body. The centrifugal trajectory obstacle avoidance motion obeying the Newtonian mechanics is studied based on the characteristics of the vehicle body turning angle change. Different traffic environments are constructed by MATLAB software and simulated according to the model running logic, and sensitivity analysis is performed based on the actual data collected in the survey. The simulation results show that, under the same conditions, compared with μ i = 50N, when μ i = 200N, the X-axis displacement of the simulated vehicle in the avoidance process is shortened by 26.31%, and the body deflection angle increases by 50.75% at the end moment of the avoidance process. Compared with electric motorcycles, the displacement of the X-axis in the avoidance process of the electric bicycle is shortened by 16.92%, and the body deflection angle at the end moment of the avoidance process is increased by 13.68%. The simulation results show that the model can well describe the trajectory change behavior characteristics of two-wheeled vehicles/riders encountering nonrigid obstacles in real situations, and the obstacle avoidance model of two-wheeled vehicles proposed in this paper is considered to be traffic justified. [ABSTRACT FROM AUTHOR]

Vehicle and crew scheduling is vital in public transit planning. Conventionally, the issues are handled sequentially as the vehicle scheduling problem (VSP) and crew scheduling problem (CSP). However, integrating these planning steps offers additional flexibility, resulting in improved efficiency compared with sequential planning. Given the ever-growing market share of electric buses, this paper introduces a new model for integrated electric VSP and CSP, called EVCSPM. This model employs the minimum cost flow formulations for electric VSP, set partitioning for CSP, and linking constraints. Due to the nonlinear integer property of EVCSPM, we propose a method that hybrids a matching-based heuristic and integer linear programming solver, GUROBI. The numerical results demonstrate the efficiency of our methodology, and the integrated model outperforms the sequential model in real-life scenarios. [ABSTRACT FROM AUTHOR]

Vehicle load estimation and health monitoring of bridges are of great importance for the health monitoring of bridge structure under vehicle loads. Traditional methods for the estimation of vehicle load require the positions of the vehicles. The vehicle position tracking is generally conducted in offline manner and requires the installation of additional sensors. To resolve these problems, we developed a Bayesian probabilistic approach for the online estimation of vehicle loads, vehicle positions, and structural parameters for bridges. The crux is to model the vehicle load vector as a modulated filtered Gaussian white noise due to the fact that the vehicle-bridge interaction forces are in essence the responses of the vehicle-bridge coupled system under the excitation of the road roughness described by Gaussian random field and the constant vehicle weights. Furthermore, the vehicle speed vector is introduced to track the unknown positions of vehicles. There are three appealing features in this approach. First, it allows the simultaneous estimation of vehicle loads, vehicle positions, and structural parameters in an online manner. Second, this method allows for time-varying vehicle speed tracking. Third, the proposed method is applicable to the case with multiple vehicles. Examples for the case where single/multiple vehicles pass across bridges with uniform speeds/variable speeds are presented to demonstrate the feasibility of the proposed method for vehicle load estimation, vehicle position tracking, and bridge structural identification using only strain measurements. [ABSTRACT FROM AUTHOR]

Federated learning is an enabling technology for the services in Internet of vehicles because it can effectively alleviate privacy issues in data circulation and diversified intelligent applications. However, existing federated learning methods still confront the problem of low computational efficiency when applied to the scenario of high-dynamic vehicles. In order to address this problem, we present a multistep federated learning architecture including task release, vehicle model training, identity authentication, quality assessment, and weighted aggregation processes, where a novel agile aggregation model is established. Based on the agile aggregation model, an efficient federated learning (EFL) algorithm is proposed, which is specifically applied to the scenario of high-dynamic vehicles during the training model. We theoretically analyze its advantages over the existing traditional federated learning algorithms in terms of efficiency, training accuracy, and security. Simulations are conducted using the MNIST dataset to evaluate the performance of the proposed EFL algorithm. The results show that EFL improves the efficiency of the federated training process with effective accuracy and privacy preservation. [ABSTRACT FROM AUTHOR]

Scanning the bridge's frequencies from the passing vehicle's vibration data has been frequently investigated recently. However, in previous studies, vehicles were typically simplified to quarter- or half-car models, and apparent disparity could be observed between the models and real vehicles. To make the vehicle model more practical, in this study, a 3D vehicle model is built to extract the bridge's frequencies from vehicle vibrations. For the first time, equations for calculating the contact-point (CP) response of the 3D vehicle model are derived with tire damping. Furthermore, residual CP responses between front and rear wheels are utilized to eliminate the inverse effects of road roughness, making the bridge frequencies outstanding in the frequency domain. The robustness of the proposed method is tested under different influence factors, and two possible measurement errors are as follows: the sensor position and axle distance when applying the proposed method in engineering. Results show that the proposed method performs stably under the influence of different road roughness classes and tire damping. Bridge frequencies can be identified when the vehicle is travelling at a highway speed (108 km/h in this study). Environmental noises can submerge the bridge's high-order frequencies but have little influence on the low-frequency range. High bridge damping will restrain the transmission of bridge vibration to the vehicle, making high-order bridge frequencies less visible. In addition, the errors introduced by a vehicle body sensor position can be eliminated when calculating the CP responses for tires, thus will not influence bridge frequency identification. To avoid possible errors induced by manual measurement of the axle distance, a novel cross-correlation function-based method is employed, which is verified effective and practical for calculating residual CP responses. [ABSTRACT FROM AUTHOR]

This study proposes a two-level optimization model system for vehicle control and signal timing at isolated signal intersections under the mixed traffic flow environment composed of intelligent connected autonomous vehicles (CAVs) and connected human-driven vehicles (CHVs), to minimize the energy consumption and vehicle delay at intersections. The proposed two-layer optimization model is composed of a two-layer vehicle trajectory control model and a fuzzy control signal timing optimization model. The two-layer vehicle trajectory control model includes a signal-oriented vehicle trajectory control model and a car-following oriented vehicle trajectory control model. The former calculates expected acceleration and speed commands at each time step according to the coming signal information, to help vehicle pass the closest signal intersection without stopping during the green light interval; the latter uses the variable headway (VTH) strategy to follow the preceding vehicle by maintaining a safe distance. A microscopic simulator based on SUMO is developed to test the performance of the proposed optimization algorithm. In the simulation experiment, with the driving characteristics of CHV drivers considered, the results show that our model performs well under a CAV penetration rate of 30%–60% and under small or moderate levels of traffic flow. The average waiting time of vehicles is reduced by about 25% compared with the uncontrolled scheme. Under the condition of penetration rate of 60%, the average energy consumption of vehicles in the proposed model is 17.56% lower than that of the uncontrolled scheme. In addition, the proposed model reduces by 21.94% compared with the scheme of only controlling vehicles. When the traffic flow is at a low or medium level, the average energy consumption and waiting time of vehicles are reduced by nearly 35% with the proposed model. [ABSTRACT FROM AUTHOR]

Automated assessment of car damage is a major challenge in the auto repair and damage assessment industries. The domain has several application areas, ranging from car assessment companies, such as car rentals and body shops, to accidental damage assessment for car insurance companies. In vehicle assessment, the damage can take many forms, from scratches, minor dents, and major dents to missing parts. Often, the assessment area has a significant level of noise, such as dirt, grease, oil, or rush, which makes accurate identification challenging. Moreover, in the repair industry, identifying a particular part is the first step in obtaining an accurate labor and part assessment, where the presence of different car models, shapes, and sizes makes the task even more challenging for a machine-learning model to perform well. To address these challenges, this study explores and applies various instance segmentation methodologies to determine the best-performing models. This study focuses on two genres of real-time instance segmentation models, namely, SipMask and YOLACT, owing to their industrial significance. These methodologies were evaluated against a previously reported car parts dataset (DSMLR) as well as an internally curated dataset extracted from local car repair workshops. The YOLACT-based part localization and segmentation method outperformed other real-time instance mechanisms with an mAP of 66.5. For the workshop repair dataset, SipMask++ reported better accuracy for object detection with a mAP of 57.0, with outcomes for A P I o U =. 50 and A P I o U =. 75 reporting 72.0 and 67.0, respectively, whereas YOLACT was observed to be a better performer for A P s with 44.0 and 2.6 for object detection and segmentation categories, respectively. [ABSTRACT FROM AUTHOR]

To explore the impact of autonomous vehicles (AVs) on human-driven vehicles (HDVs), a solution for AV to coexist harmoniously with HDV during the car following period when AVs are in low market penetration rate (MPR) was provided. An extension car following framework with two possible soft optimization targets was proposed in this article to improve the experience of HDV followers with different following strategies by deep deterministic policy gradient (DDPG) algorithm. The pretreated Next Generation Simulation (NGSIM) dataset was used for the experiments. 1027 car following events with being redefined were extracted from it, in which 600 of the events were used for training and 427 of the events were used for testing. The different driving strategies obtained from the classical car following models were embedded into virtual environment built by OpenAI gym. The reward function combined safety, efficiency, jerk, and stability was used to encourage the agent with DDPG algorithm to maximize it. The final result reveals that disturbance of HDV followers decreases by 2.362% (strategy a), 8.184% (strategy b), and 13.904% (strategy c), respectively. The disturbance of HDV follower decreases by 14.961% (strategy a), 12.020% (strategy b), and 13.425% (strategy c), respectively. HDV followers with different strategies get less jerk in both soft optimizations. AV passengers get a loss on jerk and efficiency, but safety is enhanced. Also, AV car following performs better than HDV car following in both soft and brutal optimizations. Moreover, two possible solutions for harmonious coexistence of HDVs and AVs when AVs are in low MPR are proposed. [ABSTRACT FROM AUTHOR]

The aim of this paper is to present a novel sliding mode control scheme for the supercavitating vehicle trajectory tracking problem that subjects to external disturbances and actuator saturation with two symmetric elevators and a cavitator as actuators by analytical methods and computer simulations. Firstly, the nonlinear and highly coupled dynamic and kinematic models of a supercavitating vehicle are presented in a comprehensive way by taking the cavity memory effect and time-variant planing force into consideration. The PSO algorithm is employed to optimize the control parameters for achieving better and more practical tracking performance by minimizing the objective function. A second-order extended state observer (ESO) is utilized to estimate the unknown external and state-dependent disturbances and compensates for control inputs. In addition, an antiwindup compensator is adopted to cope with actuator saturation. Finally, the proposed control scheme is employed for complex trajectory tracking of a supercavitating vehicle under various conditions by conducting comparative numerical simulations. Rigorous theoretical analysis and simulation results indicate that the proposed control scheme can achieve satisfactory tracking performance and have a good capability of robustness. [ABSTRACT FROM AUTHOR]

Numerical simulations of heterogeneous structures like battery modules of electric vehicles are challenging due to the various length scales involved in it. Even with the latest computing technology, it is impossible to simulate the crash scene of the full vehicle resolving all length scales. Such hurdles have prevented manufacturers to understand the mechanical response of battery packs in vehicle crash scenarios. In this work, the problem of multiple length scales was solved using the RVE technique based on homogenization theory. An appropriate representative volume element was identified, and a 3D FE model was developed. Classical first-order boundary conditions were used in this research work. The RVE was subjected to several macroscopic deformations, and its response was obtained. The homogenized material properties were computed from the obtained responses, and a material model available in LS-Dyna's material library was selected and calibrated to describe the nonlinear multiaxial behavior of the homogenized battery module at the macroscale. For validation, the USABC Crush and Drop Tests were simulated for the detailed and homogenized battery modules. The main output of this research is a robust and computationally efficient tool enabling satisfactory integration of a battery pack model to the vehicle for crash simulations, eliminating the need to simulate micro details at macro length scales. This approach significantly reduced the computational cost. For example, for a Drop Test simulation, the homogenized model reduced the simulation time from 40 hours (detailed model) to about 3 minutes, while maintaining a high precision ( R 2 = 0.9871) in predicting the load-displacement response. The system level modeling will enable the stakeholders to perform efficient optimization and safety evaluation for full-scale crashworthiness of electric vehicles. [ABSTRACT FROM AUTHOR]

To investigate and compare the lane changing behavior of passenger cars and heavy vehicles during the implementation period (defined as the interval from the start time to the end time of a lane change maneuver), this study applies the gradient boosting decision tree (GBDT) method to model the lane changing behavior of heavy vehicles and passenger cars, respectively. Results show that the lane change models vary with the vehicle types and lane change directions. Different factors are considered by the drivers of passenger cars and heavy vehicles when implementing lane changes to different directions. Partial dependence plots of GBDT models reveal that the influence of independent variables on lane changing behavior is nonlinear and complicated, which means that the same variable leads to various effects on the lane change decision across different vehicle types and lane change directions. In contrast with other state-of-the-art methods, the proposed method can obtain more accurate results. The findings indicate that it is necessary to build specific lane change models based on vehicle types and lane change directions for microscopic traffic simulators and autonomous vehicles. [ABSTRACT FROM AUTHOR]

To improve the quality of track maintenance work, it is a desire to estimate vehicle dynamic behavior from track geometry irregularities. This paper proposes a deep learning model to predict vehicle responses (e.g., vertical wheel-rail forces, wheel unloading rate, and car body vertical acceleration) using deep learning techniques. In the proposed CA-CNN-MUSE model, convolutional neural networks (CNNs) are used to learn features of track irregularities, and multiscale self-attention mechanisms (MUSE) are employed to capture the long-term and short-term trends of sequences. Coordinate attention (CA) is introduced into CNN to focus on important interchannel relationships and important spatial mileage points. The experiments were performed on a multibody simulation model of the vehicle system and the measured data of the actual high-speed line. The results show that the CA-CNN-MUSE has high prediction accuracy for vertical vehicle responses and fast computation speed. The predicted time-domain waveforms and power spectral densities (PSDs) agree well with the actual vehicle responses. The main features of the lateral vehicle responses can also be captured by the proposed method, yet the results are not as good as the vertical ones. [ABSTRACT FROM AUTHOR]

For traditional predictor-corrector guidance algorithm for reentry glide vehicle, it cost a lot of time to obtain predicted flight range with a slow speed to iterate. In this paper, according to residual network (ResNet)'s block and dynamic model of vehicle, through analyzing the characteristics of predicted flight range with constraints, the flight range prediction block and flight range prediction neural network are designed, which can obtain the predicted range accurately and quickly; then aiming at the separation between guidance logic and no-fly zone avoidance logic, which may lead to guidance failure and increasing of the sign variation number of the bank angle, the no-fly zone crossrange and the no-fly zone mapping crossrange are proposed in this paper. According the repulsion force of artificial potential field, an adaptive crossrange corridor combining guidance logic and no-fly zone avoidance logic is proposed, and the convergence of the corridor is analyzed theoretically. Through simulation, the block number of flight range prediction network is determined firstly. By this method, the efficiency of lateral guidance can be improved. Then, through the simulation with the different no-fly zones under different disturbed conditions, the stability and validity of the guidance method are verified. Finally, compared with other predictor-corrector algorithms, the proposed method can realize guidance with less sign variation number of bank angle and better avoidance for no-fly zones. [ABSTRACT FROM AUTHOR]

In order to calculate the vehicle load model of Baijianhe Bridge, based on the vehicle load data of the health monitoring system, the vehicle type, vehicle weight, wheelbase, and other information were counted and the data were processed and diagraphed to obtain the probability density distribution. At the same time, the automobile load model parameters relative to the national current code and finite element method are calculated. The results show that 2-axle and 6-axle vehicles are the main vehicle types, accounting for about 48%. The number of upstream and downstream vehicles is the same, but the number of vehicles in the lane is much higher than in the overtaking lane. The carriageway is dominated by 6-axle vehicles, while the overtaking lane is dominated by 2-axle vehicles. The probability density distribution of vehicle weight in overtaking lane obeys mixed Gaussian distribution and that in the carriageway obeys Weibull distribution. According to the measured vehicle load data, the vehicle load suitable for Baijianhe Bridge is 1.1 times the highway-I vehicle load of the current Chinese standard "General Code for Design of Highway Bridges and Culvers" (JTG D60-2015). [ABSTRACT FROM AUTHOR]

The high-speed steering control of tracked vehicles has been studied thoroughly, but the high-speed steering stability boundary of tracked vehicles has rarely been investigated. The establishment of a dynamics model with a high computational accuracy is the premise of studying the high-speed steering stability boundary of tracked vehicles. In this work, the track forces of tracked vehicles during steering were determined based on a shear stress model, and the steering dynamics model of tracked vehicles was established. A zero-differential-steering tracked vehicle was used as the object of study. The calculation results of the steering kinematics model and dynamics model of tracked vehicles under different steering conditions were studied in detail on sandy road surface environment, as well as the change laws of the steering trajectory, steering radius, vehicle centroid velocity, and steering slip angle. The steering trajectories of tracked vehicles on five kinds of roads under the same steering conditions were determined by simulations. By setting the critical condition for unstable steering, the corresponding relationship between the maximum circumferential velocity difference of the sprocket Δ u max , the minimum steering radius R min , the maximum sideslip angle β max , and the theoretical centroid speed v t h was obtained when the vehicle turned on five kinds of roads, and it was used to determine the high-speed steering stability boundary of tracked vehicles. The simulation results can provide a reference for the design of tracked vehicle steering mechanisms and high-speed steering control. [ABSTRACT FROM AUTHOR]

Crowdsensing systems are developed in order to use the computational and communication capabilities of registered users to monitor specific variables and phenomena in an opportunistic manner. As such, the Quality of Experience is not easily attained since these systems heavily rely on the user's behavior and willingness to cooperate whenever an event with certain interest needs to be monitored. In this work, we analyze the data acquisition phase, where pedestrians opportunistically transmit to vehicles to further disseminate it in the city according to their trajectory. This highly dynamic environment (sensors and data sinks are mobile, and the number of users varies according to the region and time) poses many challenges for properly operating a crowdsensing system. We first study the statistical properties of vehicular traffic in different regions of Luxembourg City where pedestrians share their computational resources and send data to passing cars. Then we propose an Erlang distribution to model the vehicles' dwelling times and develop a Markov chain accordingly. We model the system using two different queues: we use a single server queue to model the vehicle traffic, while we use an infinite server queue system to model the pedestrian traffic. [ABSTRACT FROM AUTHOR]

Intrusion detection is an important defensive measure for automotive communications security. Accurate frame detection models assist vehicles to avoid malicious attacks. Uncertainty and diversity regarding attack methods make this task challenging. However, the existing works have the limitation of only considering local features or the weak feature mapping of multifeatures. To address these limitations, we present a novel model for automotive intrusion detection by spatial–temporal correlation (STC) features of in‐vehicle communication traffic (intrusion detection system [IDS]). Specifically, the proposed model exploits an encoding‐detection architecture. In the encoder part, spatial and temporal relations are encoded simultaneously. To strengthen the relationship between features, the attention‐based convolutional network still captures spatial and channel features to increase the receptive field, while attention‐long short‐term memory builds meaningful relationships from previous time series or crucial bytes. The encoded information is then passed to detector for generating forceful spatial–temporal attention features and enabling anomaly classification. In particular, single‐frame and multiframe models are constructed to present different advantages, respectively. Under automatic hyperparameter selection based on Bayesian optimization, the model is trained to attain the best performance. Extensive empirical studies based on a real‐world vehicle attack data set demonstrate that STC‐IDS has outperformed baseline methods and obtains fewer false‐alarm rates while maintaining efficiency. [ABSTRACT FROM AUTHOR]

Highway traffic load, speed, and volume have been increasing continuously over the years. Because of its special structural form, the fatigue problem of a long-span concrete-filled steel tube arch bridge becomes more and more serious. To research the vehicle load spectrum and fatigue vehicle model of a long-span concrete-filled steel tubular arch bridge, the traffic data of the arch bridge were collected using the weight-in-motion system. The vehicle type and vehicle load in the actual traffic flow have strong stochastic characteristics, which cannot be directly applied. Therefore, according to the measured data, 10 representative models are proposed to facilitate the classification and screening of vehicle data. The wheelbase, mass, axle load, and overload data of the representative vehicle types were analysed, and the axle load distribution characteristics of vehicles in different lanes were studied. It is found that the vehicle load is not uniformly distributed in different lanes but concentrated in one lane. Moreover, a vehicle load spectrum for the fatigue assessment of the long-span concrete-filled steel tubular arch bridge is proposed. Based on the fatigue damage equivalence principle, a fatigue vehicle model and a simplified fatigue vehicle model of bridge heavy-duty vehicles are proposed. Compared with the model in the AASHTO specification, it is found that the weight of the local fatigue vehicle load model is 15.1 t heavier than the vehicle model given in the specification. This study could be further referenced in bridge-fatigue life prediction, management and maintenance, etc. [ABSTRACT FROM AUTHOR]

In this paper, the tracking control of the air-breathing hypersonic vehicle with model parameter uncertainties and actuator faults is studied. Firstly, a high-order linearization model is used to build an adaptive terminal sliding mode that eliminates chattering and provides increased robustness for unknown disturbances in the system. Second, a fault-tolerant control method mixing the radial basis function neural network with adaptive sliding mode control is suggested, with the addition of a hyperbolic tangent function to avoid controller input saturation. Finally, the stability of the controller is proved strictly by the Lyapunov theory, and the robustness and effectiveness of the controller are further verified by numerical simulations of the longitudinal model of the hypersonic vehicle. [ABSTRACT FROM AUTHOR]

Earthquakes often cause bridges to vibrate to different degrees; moreover, in the case of poor road conditions, the vibration amplitude of bridges caused by vehicles often exceeds a reasonable range, thus causing different degrees of damage to bridges. Therefore, this article studies the vibration reduction effect of the pounding tuned mass dampers (PTMD) on long-span continuous bridges under earthquake and vehicle loads. The PTMD used in this study can reduce the vibration in both lateral and vertical directions. The PTMD provides a stronger vibration dampening effect compared to the TMD since it increases collision to use energy. The LS-DYNA software is used for numerical modeling to optimize the parameters of the PTMD and to determine the size and installation position of the PTMD. Then, the bridge is subjected to two recorded ground motions, and the bridge response with and without the PTMD is compared and analyzed, which shows how PTMD might lessen bridge vibration in its transverse direction. To assess how efficiently the PTMD dampens vibrations induced by vehicle loads on bridges, a vehicle/bridge/PTMD system is simulated using a refined vehicle model. The findings demonstrate that the PTMD may significantly lessen the bridge's lateral and vertical vibration while enhancing driving comfort. Thus, this research study is of great significance for vertical and lateral vibration control of long-span continuous girder bridges. [ABSTRACT FROM AUTHOR]

Although automated vehicles could release drivers from the driving task, there are still passengers sitting in the vehicle. It is required that the driving comfort of passengers should be guaranteed. Cooperative adaptive cruise control (CACC) vehicle is of the one important type of automated vehicles using vehicle-to-vehicle (V2V) communications with various communication links. Different V2V communication links might have different driving comfort. Then, this paper focuses on exploring which link type for CACC vehicles is better from the perspective of improving driving comfort. To deal with this, car-following models of manual-driven vehicles (MDV) and CACC vehicles were first described. Then, simulations were performed using these car-following models, in which various CACC feedback link types, CACC penetration rates, and flow scenarios were taken into consideration. Simulations outputted microcosmic trajectory data of vehicles, based on which the driving comfort was evaluated using the comfort index described by the International Organization for Standardization (ISO) 2631-1. From the driving comfort perspective, simulation results suggest that CACC should monitor the immediately preceding vehicle and the third vehicle ahead when CACC penetration rates are less than approximately 50%. Additionally, if CACC penetration rates exceed 50%, the better choice is that CACC receives feedback links from two immediately successive vehicles ahead. [ABSTRACT FROM AUTHOR]

In order to improve the steering characteristics, roll characteristics, and lateral characteristics of heavy commercial vehicles, a multiobjective handling stability comprehensive score optimization model (MHSCS optimization model) was proposed in this study. In this study, the main factors affecting the steering characteristics, roll characteristics, and lateral characteristics are determined by combining the road test method and ADAMS software simulation analysis method. Based on the above road tests and ADAMS software simulation analysis, the MHSCS optimization model was proposed with "understeering degree," "vehicle roll angle", and "rearward amplification (RWA)" as evaluation indexes, and the response surface method combined with genetic algorithm was used to carry out multiobjective optimization of heavy commercial vehicle. ADAMS simulation results show that the comprehensive improvement degree of steering characteristics, roll characteristics, and lateral characteristics of a heavy commercial vehicle after optimization is 15.26%. Finally, the field road test results show that the scoring error between the comprehensive scoring optimization model and the real vehicle test was controlled at 0.4%, which proves the accuracy of the optimization model established in this study and effectively improves the handling stability of heavy commercial vehicles. [ABSTRACT FROM AUTHOR]

In the research of cooperative adaptive cruise control, the coupling relationship between the communication delay, the following time headway, and the string stability leads to a stringent limit on the minimum allowable following time headway. To deal with this limitation, this paper proposes the Disturbance Observer-Smith Predictor Compensation (DOB-SPC)-based Master-Slave architecture. The SPC is adopted to move the communication delays and actuator delays outside the feedback loop in the Master-Slave architecture. It theoretically realizes decoupling when parameters are estimated accurately and achieves zero-headway-string-stability for arbitrary values of communication delay. Subsequently, the robust performance of SPC with estimation deviation of vehicle model dynamic lag, actuator delay, and communication delay in practical application is discussed through the changing trend of the minimum string-stable time headway. To alleviate this problem, the DOB is designed to compensate for the interference caused by the perturbation of vehicle dynamic parameters and improve the estimation accuracy. The simulation results of a four-vehicular platoon show that the string stability and following accuracy are fully guaranteed by applying the proposed strategy, even with smaller headway in the presence of estimation deviation. [ABSTRACT FROM AUTHOR]

Vehicle rear-end collisions are primarily caused by tight car following in a continuous traffic flow, as well as a driver's incorrect perception of the traffic environment ahead and delayed response. To facilitate an investigation pertaining to rear-end collision mechanisms and accurately measure the risk, the concept of a vehicle group is introduced herein. A risk measurement model for a vehicle group (RMVG) based on temporal and spatial similarities is proposed. First, vehicles are categorized based on their temporal and spatial similarities. Risk measurement metrics are defined based on the traffic composition, movement state, and conflict extent. Subsequently, vehicle group risk identification and risk measurement models based on an isolation forest are established. the rear-end collision risk of the vehicle groups is analyzed both qualitatively and quantitatively. Finally, the RMVG is tested using the vehicle trajectory data set of Longpan South Road, Nanjing City, Jiangsu Province, China, and the results are compared with those of a support vector machine and local outlier factor. The results show that the accuracy of the RMVG is higher than those of other models: its accuracy rate and specificity are 95.68% and 88.89%, respectively, whereas its false alarm rate is only 3.47%. [ABSTRACT FROM AUTHOR]

With the rise of unmanned driving and intelligent transportation research, great progress has been made in vehicle detection technology. The purpose of this paper is employing the method of deep learning to study the vehicle detection algorithm, in which primary-stage target detection algorithms, namely, YOLOv3 algorithm and SSD algorithm, are adopted. Therefore, the method first processes the image data in the open-source road vehicle dataset for training. Then, the vehicle detection model is trained by using YOLOv3 and SSD algorithms to show the detection effect, respectively. The result is by comparing the detection effects of the two models on vehicles. The researchers accomplished the result analysis and summarized the characteristics of various models obtained by training, to apply to target tracking, semantic segmentation, and unmanned driving. [ABSTRACT FROM AUTHOR]

Airport ferry vehicles are used to transport passengers between the far apron and terminals. During the peak hours of flight arrival or departure, the demand for ferry vehicles will increase, and improper scheduling will result in significant delays. In this study, we construct a 0–1 integer programming scheduling model for ferry vehicles with multiple objectives: minimize flight delays and vehicle travel distances and balance vehicle workloads when there is an insufficient number of ferry vehicles. To solve the multiobjective model, this study selects the second version of the nondominated sorting genetic algorithm-II as the main framework and designs a modified coding and decoding scheme to solve the constraint problem in the model. The proposed model and algorithm are verified using actual data from Kunming Changshui International Airport, China. Compared with the scheduling method of manual first come first serve, the total delay time of the proposed model is reduced by 24.52%, the transit distance by 6.63%, and the balance index by 24.05%, which helps the airport solve the problem of insufficient ferry vehicles during peak hours. Comparison results show that the proposed model has a shorter calculation time and better distribution of solution sets than three state-of-the-art multiobjective optimization algorithms: MOPSO, MOACO, and SPEA-II. This research can help airports reduce flight delays, vehicle purchases, and operation costs. [ABSTRACT FROM AUTHOR]

Automated guided vehicle technology has become a hot area of scientific research due to its increasing use in manufacturing and logistics. Its main features are programming and control, remote computer eye tracking, command receiving and execution, autonomous route planning, and autonomous driving execution of tasks, with the advantages of high intelligence and flexibility. In this work, a simple vehicle model is used to study the route planning and tracking control of automatic guided vehicles. This paper uses wireless communication to find the optimal route planning problem. Using geometric methods, we develop a model of the working environment of the mobile automatic guided vehicle and develop a route finding algorithm. Based on the kinematic model, an advanced routing controller is designed to conduct experimental simulation of two trajectories and verify the effectiveness of the trajectory tracking controller. When the time is after 2 s, the position error is almost completely zero. In the path planning, when the number of iterations is greater than 10, the path length remains constant, verifying the effectiveness of the method in this paper. [ABSTRACT FROM AUTHOR]

In order to solve the problem of genetic algorithm, the author proposes a research on optimization of a logistics transportation network. First, combined with the distribution characteristics of logistics, starting from the relevant research theories of vehicle routing problems, the vehicle travel distance is considered in the basic model of vehicle routing optimization, a vehicle routing model with soft time window is established to minimize the total cost, respectively, and a vehicle routing model with multiple distribution centers. Second, on this basis, according to the actual problems and model needs of the research, using the basic principle of genetic algorithm, the solution algorithm design of the built model is carried out and we use MATLAB for programming, so that the solution of the built model is finally realized. We set T = 100, S = 50, Pc = 0.95, Pm = 0.1. β1 = 0.005, β2 = 1, and β3 = 0.005. Finally, the actual distribution data of the G supermarket chain in the region are used as the experimental data; by picking two shipping instances, the two constructed models and the designed genetic algorithm are verified, respectively. [ABSTRACT FROM AUTHOR]

Centrifugal force is what causes the vehicle to roll over. This force is generated when the vehicle suddenly changes direction when moving at high speed. The solution of using a stabilizer bar is suggested to minimize this phenomenon. There are currently three types of stabilizer bars in use: mechanical stabilizer bars, hydraulic stabilizer bars, and electronic stabilizer bars. The content of this article is aimed at introducing and reviewing the characteristics of the stabilizer bar. Besides, two oscillation simulation models of vehicles equipped with stabilizer bars are also analysed in this article. Additionally, the characteristics of the control algorithms for the stabilizer bar are clearly analysed. The half-dynamics model is suitable for algorithms that need to use oscillatory state matrices such as SMC, LQR, and LQG. The spatial dynamics model is suitable for some algorithms such as PID, fuzzy, and neural. The roll angle of the vehicle has been significantly improved when the stabilizer bar is fitted. In general, the stability and safety of the vehicle can be guaranteed if the vehicle uses a stabilizer bar. [ABSTRACT FROM AUTHOR]

Connected and autonomous vehicles (CAVs) are emerging technology that attracts the interests of many transportation professionals and computational scientists. Several recent studies have investigated different model frameworks of CAVs in different transportation environments, such as on freeways and at conventional intersections. Nevertheless, few efforts have been made to investigate the performances of CAVs at innovative intersections, and the lack of knowledge can result in an inaccurate prediction of CAVs performances in the existing transportation network. This research intends to mitigate this research gap by studying the traffic delay and fuel consumption of CAVs in the environment of the superstreet and its equivalent conventional intersection through simulation-based experiments. A real-world superstreet in Leeland, NC, is selected and used. A conventional intersection with equivalent road designs is established in the simulation platform to make a comparison with the selected superstreet. This research develops both platooning and trajectory planning modeling frameworks to examine the implications of CAVs with different capabilities. The Intelligent Driver Model (IDM) is selected and applied to model the CAV behaviors, while Wiedemann 99 (W99) is used to model Human-Driven Vehicles (HDVs). The simulation results demonstrate the efficiency of both platooning and trajectory planning, respectively. Different effects of CAVs in the superstreet and its equivalent conventional intersection are observed. The findings from this research can provide an important reference for transportation planners and policymakers in predicting the influence of CAVs on the existing transportation infrastructure. [ABSTRACT FROM AUTHOR]

In order to improve the intelligent vehicle lane-changing performance, an active lane-changing control algorithm is proposed considering the changes of road curvature and vehicle speed. Firstly, the vehicle dynamics model considering vehicle speed variation and lane-changing safety distance is established, and the expected lane-changing trajectory model under the curved road is designed simultaneously. Then, taking the yaw rate and longitudinal speed as the control objectives of lateral and longitudinal motions, respectively, the sliding-mode variable structure control method based on Lyapunov stability condition is adopted, and the trajectory tracking controller is designed by combining the inverted method to track the desired lane-changing trajectory. Finally, the lane-changing trajectory model and trajectory tracking controller are verified in simulation platform of CarSim/Simulink and hardware-in-the-loop (HIL) test bench. The results show that the proposed trajectory tracking control method can perform the lane-changing behavior well under different road curvatures and vehicle speeds while maintaining high trajectory tracking control accuracy. [ABSTRACT FROM AUTHOR]

The integration of solar photovoltaic (PV) power generation technology into electric vehicle (EV) charging systems is of great significance, and it is very important to analyze the influencing factors of vehicle operating parameters on the generation power of vehicle PV system. In this paper, a model is developed to predict the power generated by vehicle PV system through a combination of a Fluent simulation study and Simulink modeling. Then the generation power of vehicle PV system under 7 different vehicle speeds in different months in Northeast China is analyzed. Through the Spearman correlation analysis and multilevel function fitting of the data, the mathematical model of a vehicle PV system power is proposed. Its variation with vehicle speed is obtained, as affected by solar radiation intensity and ambient temperature. According to the mathematical model analysis, the relevant vehicle characteristics and rules for vehicle PV systems were obtained. The simulation results show that the generation power of vehicle PV system in Northeast China increases significantly with the increase of vehicle speed in summer, but does not change significantly with the increase of vehicle speed in winter. [ABSTRACT FROM AUTHOR]

With increasing development of Internet of Things (IoT) technology, wireless communications, big data, and smart applications, vehicular communications have become ubiquitous in smart cities, smart transportation systems, and Internet of Vehicles (IoV) environments. In this paper, a new Energy-aware Intrusion Detection System (EIDS) based on intelligent two-phase contract management model is presented for vehicle-to-vehicle (V2V) strategy in the IoV environments. In this strategy, the proposed EIDS predicts safe and energy-efficient end-to-end points for communication between existing vehicles in the IoV. The contract management process shows how the vehicles are connected together with a safe condition to transfer information. For prediction phase, a regression algorithm is applied to evaluate the proposed EIDS according to NSLKDD data set in the IoV environments. Simulation experiments show that the proposed regression-based EIDS strategy can effectively improve the accuracy and precision factors with 90% and 84%, respectively, and greatly minimize execution time by 4 seconds with respect to other machine learning algorithms. [ABSTRACT FROM AUTHOR]

Lane changing behavior has a significant impact on traffic efficiency and may lead to traffic delays or even accidents. It is important to plan a safe and efficient lane-changing trajectory that coordinates with the surrounding environment. Most conventional lane-changing models need to establish and solve constrained optimization models during the whole process, while reinforcement learning can just take the current state as input and directly output actions to vehicles. This study develops a lane-changing model using the deep deterministic policy gradient method, which can simultaneously control the lateral and longitudinal motions of the vehicle. To optimize its performance, a reward function is properly designed by combining safety, efficiency, gap, headway, and comfort features. To avoid collisions, a safety modification model is developed to check and correct acceleration at every time step. The driving trajectory data of 1169 lane-changing scenarios extracted from the Next Generation Simulation (NGSIM) dataset are used to train and test the model. The proposed model can quickly converge in training phase. Testing results show it can complete safe and efficient lane changing in different lane-changing scenarios with both shorter time headway and lane-changing duration than human drivers. Compared with the conventional dynamic lane-changing trajectory planning model, our model can reduce collision risk. It is also evaluated in automated and nonautomated mixed traffic in SUMO. Simulation results show that the proposed model also has a positive effect on the average speed of overall traffic flow. [ABSTRACT FROM AUTHOR]

Firstly, the MBD (multi-body dynamics) model of the whole vehicle is built according to the measured vehicle parameters. Then, the road spectra acquisition test is carried out. Under the test road conditions, the acceleration, displacement, and force signals of multiple positions of the whole vehicle are collected. Taking the road test signals as the iterative target and the vehicle MBD model as the iterative carrier, the vehicle equivalent excitation is obtained by using the VIM (virtual iteration method). By applying the equivalent excitation obtained by VIM to the vehicle MBD model, the load spectra of key points of the driving rear axle are obtained. The accuracy of the model is verified by comparing the measured signals with the iterative signals, and the reliability of the key points load spectra is enhanced. Secondly, the FEA (finite element analysis) model of the rear axle is built, and the static analysis results of each key point under unit load are obtained with the help of FEA software. Thirdly, the FEA results, key points load spectra, and material fatigue characteristic curve of the rear axle are input into the fatigue software to conduct the fatigue simulation of the rear axle. According to the fatigue analysis results, the position where the fatigue life does not meet the set goal is accurately located. Finally, the structure is optimized based on the above results. The fatigue life evaluation of the optimized rear axle shows that its durability has been significantly improved. [ABSTRACT FROM AUTHOR]

The phenomenon of vehicle rollover usually occurs when the driver suddenly steers at high speed. The centrifugal force will appear and cause the vehicle's body to tilt. To overcome this situation, the solution of using the stabilizer bar is proposed. The passive stabilizer bar has a simple structure, low cost, and long service life. As a result, it is now a standard on most vehicles. This paper has established a dynamic model to describe a vehicle's oscillation. As a result of the study, the maximum roll angle of the vehicle was reduced from 9.0° to 8.2° when the stabilizer bar was used. Besides, the minimum value of the vertical force at the wheel reached 485(N) and 1162(N), respectively, corresponding to the two survey cases. The movement trajectory of the vehicle when using the stabilizer bar does not have a big difference compared to the case when the vehicle does not use the stabilizer bar. So, the vehicle's stability and safety can be effectively improved. This is the basis for further developing complex stable bar patterns in the future. [ABSTRACT FROM AUTHOR]

We propose a multiagent, large-scale, vehicle routing modeling framework for the simulation of transportation system. The goal of this paper is twofold. Firstly, we investigate how individual and social knowledge interact and ultimately influence the effectiveness of resulting traffic flow. Secondly, we evaluate how different discrete-event simulation designs (delays vs. queuing) affect conclusions within the model. We present a new agent-based model that combines the efficient discrete-event approach to modeling with the intelligent drivers who are capable to learn about their environment in the long-term perspective from both, individual experience, and widely available social knowledge. The approach is illustrated as practical application to modeling commuter behavior in the city of Winnipeg, Manitoba, Canada. All simulations in the paper are fully reproducible as they have been carried out by utilizing a set of opensource libraries and tools that we have developed for the Julia programming language and that are openly available on GitHub. [ABSTRACT FROM AUTHOR]

Aiming at solving problem of vehicle state estimation, an adaptive fading unscented Kalman filter(AFUKF) algorithm was proposed. Based on this purpose, a 7-DOF nonlinear vehicle model with the Pacejka nonlinear tire model was established firstly. Then, the vehicle state estimator based on Kalman filter was designed to solve the problem of vehicle state estimation. The simulation verification shows the effectiveness and reliability of the designed estimator for vehicle state estimation. Compared with other traditional methods, the calculation accuracy is higher for the AFUKF algorithm to solve the problem of vehicle state estimation. The study can help drivers easily identify key state estimation in safe driving area. [ABSTRACT FROM AUTHOR]

As a type of horizontal curve, the reverse horizontal curves (RHCs) are one of the most critical types of road locations where many accidents occur due to slippage resulting from the centrifugal force of the vehicles. These crashes can increase dramatically if the RHC is combined with a longitudinal slope (downgrade or upgrade). In this study, by using the vehicle dynamic modeling (VDM) method, the lateral acceleration of vehicles (including E-Class Sedan, E-Class SUV, and Truck) passing through the RHC combined with three scenarios of downgrades, upgrades, and no slope was evaluated. Finally, in order to present the final model, the RHC lateral acceleration model was presented based on the effective parameters, including different design velocities (V), the direct distance between two horizontal curves (D), and different longitudinal slopes (G) by the multiple regression model method. The results of the VDM modeling showed that the slide potential of Sedans, SUVs, and Trucks when crossing the RHC combined with the downgrade was more significant than the upgrade and the no-slope paths. On the other hand, the modeling results showed that the proposed models for Sedans and SUVs with a very high level of importance (R2 = 0.920 and R2 = 0.967) could be used to assess the safety of vehicles crossing the RHC. According to the mentioned models, increasing the speed (V) and reducing the direct distance (D) in both models reduced the lateral acceleration (increased safety) of both Sedans and SUVs. [ABSTRACT FROM AUTHOR]

The evaluation of take-over performance and take-over safety performance is critical to improving the take-over performance of conditionally automated driving, and few studies have attempted to evaluate take-over safety performance. This study applied a binary logistic model to construct a take-over safety performance evaluation model. A take-over driving simulator was established, and a take-over simulation experiment was carried out. In the experiment, data were collected from 15 participants who took over the vehicle and performed emergency evasive maneuvers while performing non-driving-related task (NDRT). Then, to calibrate the abnormal trajectory, the Kalman filter is adopted to filter the disturbed vehicle positioning data and the belief rule-based (BRB) method is proposed to warn irregular driving behavior. The results revealed that the accident rate of male participants is higher than that of female participants in the three frequency take-over experiment, and the overall driving performance of female participants is higher than that of male participants. Meanwhile, medium and high take-over frequencies have a significant effect on the prevention of vehicle collisions. In the take-over safety performance evaluation model, the minimum time to collision (TTC) of 2.3 s is taken as the boundary between the dangerous group and the safety group, and the model prediction accuracy rate is 87.7%. In sum, this study enriches existing research on the safety performance evaluation of conditionally automated driving take-over and provides important implications for the design of driving simulators and the performance and safety evaluation of human-machine take-over. [ABSTRACT FROM AUTHOR]

Rutting is one of the major asphalt pavement distresses that could cause hydroplaning and lead to roadway safety concerns on a rainy day. However, there is still no theoretical methodology with simulation to evaluate and predict the driving safety caused by rut-induced hydroplaning using vehicle dynamics. This paper proposes a methodology, based on a new developed "vehicle-water-filled rutting" open-loop vehicle dynamics model analyzed by CarSim (a professional vehicle dynamic software), to simulate and compute the impact of unbalanced water-filled rutting on driving stability/safety with a special focus on the vehicle's lateral dynamic stability, including lateral offset and lateral acceleration. Analysis results show the following: (1) The unbalanced water depths in left rutting and right rutting lead to the different friction between left rutting and right rutting and make the vehicle wander left and right uncontrollably along the roadway. (2) When the vehicle speed is greater than 80 km/h and the rutting width exceeds 0.7 m, the unbalanced water-filled rutting begins to affect the vehicle's lateral stability apparently and very likely threaten the driver's life. (3) By computing the vehicle's lateral offset and acceleration in different water widths and lengths in the hydroplaning situation, this paper proposes the rutting width and length thresholds for vehicle oversteer and instability, based on which the vehicle risk level is proposed, RISK I is the situation where vehicle's lateral offset exceeds 1.025 mm which likely causes vehicle running into the adjacent lane (the danger can be avoided if there is no vehicle in the adjacent lane), RISK II is the situation where the vehicle's tires begin to work into the nonlinear zone (the vehicle's lateral acceleration exceeds 0.4 g) with the increase of rutting width and length, which makes the vehicle lose controls irreversibly for drivers, and this situation should arouse more concerns for transportation agencies. The proposed methodology would enable transportation agencies to predict the security risk of rutting and play a vital role for transportation agencies to scientifically establish the relevant specifications (e.g., rutting maintenance) that proactively trigger the maintenance and rehabilitation to prevent potential safety concerns from happening. Recommendations on future researches, including taking braking stability and real rutting conditions of the proposed model into consideration, are also continued in the future study. [ABSTRACT FROM AUTHOR]

Summary: Intelligent battery management systems (BMSs) require immediate and precise state of charge (SoC) estimation of the lithium‐ion cell for efficient use in electric vehicles (EVs). To solve the problem of real‐time SoC estimation for physics‐based lithium‐ion cell models in BMSs, rigorous physics‐based models are required to be reasonably order‐reduced. In this paper, an improved order‐reduced physics‐based model is proposed and validated with experimental data. Next, state equations of the reduced model are established by adopting finite difference approaches. Moreover, three reduced model‐based estimators including the extended Kalman filter (EKF), the uniform reaching law sliding‐mode observer (SMO‐URL), and the exponential reaching law sliding‐mode observer (SMO‐ERL) are developed to estimate battery SoC by using input current and external measured voltage. To demonstrate the effectiveness of the proposed SoC estimation algorithms, their performances are evaluated and compared via experimentation under the hybrid pulse power characteristic (HPPC) test. The results show that the proposed order‐reduced model‐based SMO‐ERL algorithm has faster convergence ability and higher estimation accuracy compared with the other two algorithms under incorrect SoC initial conditions. [ABSTRACT FROM AUTHOR]

Reverse Horizontal Curves (RHCs) are among the most accident-prone road points, with many annual fatalities and injuries. These fatalities can increase dramatically if the RHCs and longitudinal slopes are combined. The importance of increasing the safety of RHCs, especially in mountainous routes, is doubled due to the possibility of combining RHCs with vertical extensions or combining them with so-called steep slopes. This study used vehicle dynamic modeling to evaluate the lateral friction of various vehicles. Including the E-Class Sedan, E-Class SUV, Truck, and Bus, moving on RHCs combined with a longitudinal slope (downgrade, upgrade, and direct distance). Then, the RHC lateral friction model was presented using the multiple regression model based on the effective parameters, including design speeds, direct distance, and different longitudinal slopes. The results showed that speed, longitudinal slope, and vehicle type had the most impact, and direct distance had the most negligible impact in friction coefficient models. Based on the modeling results, the higher the design's speed and the shorter the direct distance, the lower the lateral friction coefficient for the Sedan and SUV. Hence, the safety of the vehicles is greater. For trucks, reduced speed, increased direct distance, and reduced slope led to increased safety. In the results, the most critical state was the lateral friction coefficient at a speed of 80 km/h and a direct distance of 116 m for the SUV. [ABSTRACT FROM AUTHOR]

With the development of various vehicle applications, such as vehicle social networking, pattern recognition, and augmented reality, diverse and complex tasks have to be executed by the vehicle terminals. To extend the computing capability, the nearest roadside-unit (RSU) is used to offload the tasks. Nevertheless, for intensive tasks, excessive load not only leads to poor communication links but also results to ultrahigh latency and computational delay. To overcome these problems, this paper proposes a joint optimization approach on offloading and resource allocation for Internet of Vehicles (IoV). Specifically, assuming particle tasks assigned for vehicles in the system model are offloaded to RSUs and executed in parallel. Moreover, the software-defined networking (SDN) assisted routing and controlling protocol is introduced to divide the IoV system into two independent layers: data transmission layer and control layer. A joint approach optimized offloading decision, offloading ratio, and resource allocation (ODRR) is proposed to minimize system average delay, on the premise of satisfying the demand of the quality of service (QoS). By comparing with conventional offloading strategies, the proposed approach is proved to be optimal and effective for SDN-enabled IoV. [ABSTRACT FROM AUTHOR]

Autonomous driving has become a prevalent research topic in recent years, arousing the attention of many academic universities and commercial companies. As human drivers rely on visual information to discern road conditions and make driving decisions, autonomous driving calls for vision systems such as vehicle detection models. These vision models require a large amount of labeled data while collecting and annotating the real traffic data are time-consuming and costly. Therefore, we present a novel vehicle detection framework based on the parallel vision to tackle the above issue, using the specially designed virtual data to help train the vehicle detection model. We also propose a method to construct large-scale artificial scenes and generate the virtual data for the vision-based autonomous driving schemes. Experimental results verify the effectiveness of our proposed framework, demonstrating that the combination of virtual and real data has better performance for training the vehicle detection model than the only use of real data. [ABSTRACT FROM AUTHOR]

The rapid development of science and technology has created favorable conditions for Connected and Automated Vehicles (CAVs). Accurate localization is one of the fundamental functions of CAV to realize some advanced operations such as vehicle platooning. However, complicated urban traffic environments, such as the flyover, significantly influence vehicular positioning accuracy. The inability of CAV to accurately perceive self-localization information has become an urgent issue to be addressed. This paper proposed a novel cooperative localization method by introducing the relative Direction-of-Arrival (DOA) and Relative Distance (RD) into CAV to improve the localization accuracy of CAV in the multivehicle environment. First, the three-dimensional positioning error model of the host vehicle concerning adjacent vehicles in azimuth angle and pitch angle and intervehicle distances under the vehicle-to-vehicle communication was established. Second, two least-squares estimation algorithms, linear and nonlinear, are established to decrease the position errors by combining relative DOA and RD measurement information. To verify the proposed algorithm's effect, the PreScan-Simulink joint simulation is carried out. The results show that the host vehicle's localization accuracy by the proposed method can be improved by 25% compared with direct linearization. Besides, by combining relative DOA and relative RD measurement, the locating capability of the least-square-based nonlinear optimization method can be enhanced by 22%. [ABSTRACT FROM AUTHOR]

The deep-sea mining miner requires good passability to operate in complex and changeable terrain environment of the seabed. Among them, the track tension is an important factor to ensure the normal running of the vehicle. Aiming at the complex track tension problem on track link, the forces on each component of the tracked system were analyzed, and the theoretical formulas of track tension were established. The theoretical estimation and dynamic simulation of track tension in uniform speed were carried out by using the multibody dynamics model of the tracked vehicle, and the rationality of the theoretical estimation formula was verified. The influencing factors of track tension also were analyzed, and the significance of each factor on track was discussed by dynamic simulation. The results provided a theoretical basis for design of tracked vehicles. [ABSTRACT FROM AUTHOR]