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In response to the strength performance requirements of the new energy Off road Vehicle steering system, this article analyzes and studies the strength performance and failure modes of the new energy Off road Vehicle steering rod based on a certain new energy Off road Vehicle model through simulation, bench experiments, and vehicle strength testing teams. Based on various vehicle misuse conditions, the force law of the steering rod under different vehicle driving conditions was analyzed, and a load spectrum for the steering rod strength bench test was developed to meet the actual vehicle usage conditions. The results indicate that the steering rod strength failure is mainly manifested as bending failure of the compression rod under alternating tension and compression conditions. Finally, based on the stability principle of the compression rod, the requirements for the strength performance index of the steering rod are proposed. The research results can provide certain technical support for the strength assessment of the vehicle steering rod. [ABSTRACT FROM AUTHOR]

Multi-intelligent vehicles forming and maintaining a specific formation during traveling can save energy consumption and road resources, and improve traffic efficiency and driving safety. Centered around the Cooperative Vehicle Infrastructure(CVI) framework of the Internet of Vehicles(IOV), this paper delves into the control objectives of forming multi-intelligent vehicle formation, explores the theory of formation shaping process, and investigates the optimization of energy consumption in the formation shaping process. First, the clustering design is used to take the kinematic process of vehicles as an optimization process from the bionics, taking the instantaneous consumption indexes as the optimization objectives. Then, optimize the acceleration inputs of the vehicle formation process using the improved Artificial Fish Swarm Algorithm(AFSA), and iterate with the clustering algorithm part, so as to realize the formation shaping process with energy consumption as the optimization objective. Finally, ajoint simulation platform built by Simulation of Urban Mobility (SUMO) and Python is used for simulation verification, and the results show that the formation shaping process can be realized and achieve the optimal energy consumption under the scenarios proposed in this paper, thus verifying the validity of the clustering model of vehicle formation shaping and the energy consumption optimization algorithm designed in this paper. [ABSTRACT FROM AUTHOR]

Within the urban road system, traffic conflicts most frequently are concentrated at road intersections, and left-turning traffic is a prominent and pivotal factor leading to intersection conflicts. The scarcity of applied models that can comprehensively elucidate the shifting trajectory dynamics and exit lane selection behavior of left-turning vehicles has posed challenges to the accurate analysis and prediction of intersection vehicle conflict patterns. This study extracted trajectories of left-turning vehicles at intersections from video data. A vehicle left-turn trajectory model was formulated, hinging on changes in trajectory curvature. Moreover, leveraging geometric intersection parameters and video data, a vehicle exit lane selection model was devised using random utility theory. This model integrates variables such as intersection turning angle, vehicle type, entrance lane position, vehicle speed, following clearance, and the lead vehicle’s position in the exit lane. The outcomes indicate that left-turn trajectories can be approximated well with a flat curve, and the trajectory’s curvature has a quadratic parabolic correlation with the intersection’s aspect ratio. The trajectory model presented in this paper adeptly characterizes left-turning vehicle movement across diverse intersections. Similarly, the exit lane selection model adeptly forecasts the distribution of left-turning vehicles in exit lane positions. This predictive efficacy stems from the recognition that turning angle, entrance lane vehicle position, and vehicle speed profoundly impact the exit lane choice for left-turning vehicles. This research sheds light on the intricacies of driving behavior and exit lane selection of left-turning vehicles at signalized intersections. By focusing on these critical aspects, this study offers valuable insights that contribute to the improvement of intersection design and overall traffic safety. [ABSTRACT FROM AUTHOR]

Remaining driving range (RDR) research has continued to consistently evolve with the development of electric vehicles (EVs). Accurate RDR prediction is a promising approach to alleviate distance anxiety when power battery technology is not yet fully matured. This paper first introduces the research motivation of RDR prediction, summarizes the previous research progress, and classifies the influencing factors of RDR. Second, conduct research and analysis on the physical model of EVs, mainly including battery and vehicle models. Based on the physical model, the energy flow problem of EVs is analyzed and discussed. Third, four key challenges of RDR prediction are summarized: battery state estimation, driving behavior classification and recognition, driving condition prediction and speed prediction, and RDR calculation method. Finally, given the four challenges faced by RDR, a driving range prediction method based on vehicle‐cloud collaboration is proposed, which combines the advantages of cloud computing and machine learning to provide further research trends. [ABSTRACT FROM AUTHOR]

For rail-cum-road bridges with single-level arrangement, there are not only dynamic interference, but also aerodynamic interference between trains and vehicles, which means that the train-induced winds generated by high-speed trains are non-negligible for vehicles. In this work, the test method of moving vehicle model is used to test the aerodynamic characteristics of vehicles under train-induced winds. Then, the model of coupled vibration of train–vehicle–bridge system considering train-induced winds is established. Finally, taking a rail-cum-road bridge with single-level arrangement as an example, the coupled vibration of train–vehicle–bridge system considering train-induced winds is analyzed, and the influence of train-induced winds, lateral spacing between trains and vehicles on train–vehicle–bridge system is discussed. The results show that train-induced winds decrease with the increase of the lateral spacing between trains and vehicles. Without considering train-induced winds, the moving train has a greater impact on the vertical response of the vehicle, and a smaller impact on the lateral response of the vehicle. When considering train-induced winds, the lateral response of the vehicle increases significantly, but decreases with the increase of the lateral spacing. In addition, road conditions and the setting of anti-dazzle plates can change traffic safety under train-induced winds. [ABSTRACT FROM AUTHOR]

Advanced active safety systems play a crucial role in ensuring the safe driving of vehicles in critical conditions such as an obstacle avoidance manoeuvre. However, conventional techniques relying mainly on braking interventions may not result in the desired vehicle response in such situations. Over-actuation through the control of individual motion actuators could potentially improve the safety performance of vehicles. This study evaluates various configurations of motion actuators for path following and yaw stability control of vehicles in critical driving scenarios. The configurations include active front steering (S), active front steering + torque vectoring (ST), active front steering + active camber (SC) and active front steering + torque vectoring + active camber (STC). The evaluation is achieved based on a nonlinear model predictive control formulation, which considers yaw stability and the physical limits of motion actuators. This problem formulation uses a double-track vehicle model, combined with the Dugoff tyre model and its variant with the camber effect, to model the vehicle dynamics. The actuator configurations are evaluated regarding the passing velocity, tracking accuracy, safety distance and robustness to reference trajectory variation. The results indicate that the integrated control of STC performs the best among all the four configurations while S performs the worst. Furthermore, SC is generally superior to ST. [ABSTRACT FROM AUTHOR]

To solve the problems of narrow driving condition range, partial evaluation method and insufficient road adaptive control of trajectory tracking, the trajectory tracking accuracy and driving safety under full speed and full road adhesion coefficient were investigated. In the road adaptive model predictive control (MPC), the discrete linear time-varying predictive model was obtained by the three- degree-of-freedom nonlinear dynamic model of the vehicle. The road adaptive speed range matching was realized according to the road adhesion coefficient detected by sensors, and the constraint condition of the speed increment was formulated. The maximum and standard deviation of path tracking error, lateral acceleration, centroid sideslip angle and front wheel sideslip angle with the change of velocity and road adhesion coefficient were obtained by integrating CarSim and Matlab soft wares. Combined with the mechanism of tire sideslip, the driving safety was evaluated, and the trajectory tracking stability/ instability area of vehicle under all working conditions was divided. The results show that the vehicle speed can be adaptively adjusted according to the road information to achieve excellent trajectory tracking accuracy and driving safety under all working conditions. [ABSTRACT FROM AUTHOR]

Hydrogen fuel cells have developed rapidly in bus because of their cleanliness. The driving parameters of hydrogen fuel cell bus and the internal temperature distribution of hydrogen fuel stack are investigated in this study. The 1-D model of hydrogen fuel cell bus and the 2-D model of hydrogen fuel cell stack are established based on AMESim. In the cooling process, for the existing typical thermal management system, the influence of ambient temperature on the fuel cell bus driving parameters is analyzed by the 1-D model. In the heating process, the internal temperature distribution of the stack is analyzed under three heating modes by the 2-D model. As the ambient temperature increases, to ensure the safe operating temperature of the fuel cells, the bus top speed should be reduced, and the acceleration time should be extended. When the stack needs to be heated, the gas-liquid heating mode can supply a more uniform temperature distribution inside the stack. • The 1-D vehicle model and 2-D stack model of hydrogen fuel cell are established. • The effect of ambient temperature on driving parameters is analyzed. • The internal temperature distribution of the stack is analyzed under three heating modes. • Gas-liquid heating mode can give a more uniform stack temperature distribution. [ABSTRACT FROM AUTHOR]

Next-generation accurate vehicle localization and connectivity technologies will enable significant improvements in vehicle dynamics control. This study proposes a novel control function, referred to as pre-emptive braking, which imposes a braking action if the current vehicle speed is deemed safety-critical with respect to the curvature of the expected path ahead. Differently from the implementations in the literature, the pre-emptive braking input is designed to: a) enhance the safety of the transient vehicle response without compromising the capability of reaching the cornering limit, which is a significant limitation of the algorithms proposed so far; and b) allow — in its most advanced implementation — to precisely constrain the sideslip angle to set levels only through the pre-emptive control of the longitudinal vehicle dynamics, without the application of any direct yaw moment, typical of conventional stability control systems. To this purpose, a real-time-capable nonlinear model predictive control (NMPC) formulation based on a double track vehicle prediction model is presented, and implemented in its implicit form, which is applicable to both human-driven and automated vehicles, and acts as an additional safety function to compensate for human or virtual driver errors in extreme conditions. Its performance is compared with that of: i) two simpler — yet innovative with respect to the state-of-the-art — pre-emptive braking controllers, namely an NMPC implementation based on a dynamic point mass vehicle model, and a pre-emptive rule-based controller; and ii) a benchmarking non-pre-emptive rule-based trail braking controller. The benefits of pre-emptive braking are evaluated through vehicle dynamics simulations with an experimentally validated vehicle model, as well as a proof-of-concept implementation on an automated electric vehicle prototype. [ABSTRACT FROM AUTHOR]

The present research aims to understand the safety over the midblock road sections and proposes a safety framework using the conventional Time to Collision (TTC) measure. In the present work, the safety framework underlines a supporting structure connecting the actions of the surrounding vehicles and assesses the collisions changes for a given subject vehicle. The Framework principally checks the likelihood of lateral overlap and the time gap between the subject vehicle and its surrounding vehicles. Later, for the trajectory data development, an automated trajectory data development tool is built with the help of image processing for generating the trajectory data from the study sections. In supporting the developed safety framework, the lateral movement of the vehicles is modeled precisely with the help of deep learning. Further, the conceptualized safety framework is tested with the developed trajectory data sets over the study sections. From the results, it is observed that, in mixed traffic, the collision points are over the entire geometry of the study section. In the case of homogeneous traffic, the collision instincts are clustered toward the median lanes. With the advancement of technology, trajectory data development can be a real-time exercise, and the safety framework can be implemented. By applying the study methodology, the critical spots over the road network can be flagged for better treatment and improve safety over the sections. [ABSTRACT FROM AUTHOR]

In this paper, an original construction of a vehicle vibration damper controlled by means of a valve based on piezoelectric actuator is presented and investigated. The presented valve allows us to control dissipation characteristics of the damper faster than in other solutions adjusting the size of the gap through which the oil flows between the chambers of the damper. The article also presents the results of the experimental investigation of the above-mentioned damper showing the possibility of changing the value of the damping force five times in about 10 ms by changing the voltage supplying the piezoelectric actuator. Based on these results, dissipative characteristics were determined which enabled the identification of the parameters of the damper numerical model. The article also presents the results of numerical investigations a vehicle model equipped with the developed dampers. The results showed that the developed damper controlled by the use of the piezoelectric actuator can significantly affect vehicle traffic safety by reducing the variation of vertical forces acting on the wheels. The results obtained are so promising that the authors undertook preparations to conduct road tests of a vehicle equipped with the developed dampers. [ABSTRACT FROM AUTHOR]

The use of mobile phones has become one of the major threats to road safety, especially in young novice drivers. To avoid crashes induced by distraction, adaptive distraction mitigation systems have been developed that can determine how to detect a driver's distraction state. A driving simulator experiment was conducted in this paper to better explore the relationship between drivers' cognitive distractions and traffic safety, and to better analyze the mechanism of distracting effects on young drivers during the driving process. A total of 36 participants were recruited and asked to complete an n-back memory task while following the lead vehicle. Drivers' vehicle control behavior was collected, and an ANOVA was conducted on both lateral driving performance and longitudinal driving performance. Indicators from three aspects, i.e., lateral indicators only, longitudinal indicators only, and combined lateral and longitudinal indicators, were inputted into both SVM and random forest models, respectively. Results demonstrated that the SVM model with parameter optimization outperformed the random forest model in all aspects, among which the genetic algorithm had the best parameter optimization effect. For both lateral and longitudinal indicators, the identification effect of lateral indicators was better than that of longitudinal indicators, probably because drivers are more inclined to control the vehicle in lateral operation when they were cognitively distracted. Overall, the comprehensive model built in this paper can effectively identify the distracted state of drivers and provide theoretical support for control strategies of driving distraction. [ABSTRACT FROM AUTHOR]

In view of the easy steering instability of vehicles running sideways, this paper considers the variation of tire cornering stiffness to avoid saturation of tire lateral force and improve driving safety and constructs a vehicle stability control method. The front and rear axle tire sideslip angle segmented fitting method is used to establish a tire cornering stiffness fitting model, and then the fitted cornering stiffness is introduced into the vehicle dynamics model to accurately describe the current dynamic performance of the vehicle. In order to avoid the steering instability caused by the saturation of the tire lateral force, this paper proposes a vehicle stability control method based on the model predictive control (MPC) algorithm that considers the constraints of the front and rear axle tire sideslip angles to optimize the vehicle’s steering performance. Simulation results show that the proposed vehicle stability control method can suppress the tire sideslip angle of the front axles within a certain range, while taking into account the changes in the cornering stiffness, thereby avoiding the occurrence of vehicle sideslip and improving the stability performance of the vehicle. [ABSTRACT FROM AUTHOR]

• Very old vehicles continue to be driven on US streets and carry a relative risk of crash fatality that is approximately 4–17 times that of newer vehicles. • These very old vehicle crashes tend to occur on rural roads at speeds below highway speeds in fair weather conditions. • Older occupant age, lack of restraint use, and alcohol use are associated with fatality in these events. Introduction: Older vehicles, commonly referred to as "classic," "vintage," or "historic" vehicles (CVH), share the roadways with newer vehicles. Older vehicles lacking safety systems likely come with an increased risk of fatality, however there is no study examining the typical conditions for crashes involving CVH. Method: This study utilized information from crashes occurring in 2012 to 2019 to estimate fatal crash rates for vehicles grouped by model year deciles. Data from crashes documented in the National Highway Traffic Safety Administration's (NHTSA) FARS and GES/CRSS data sets were utilized to examine roadway, temporal, and crash types for passenger vehicles produced in 1970 or earlier (CVH). Results : These data show CVH crashes are rare (<1% of crashes), but carry a relative risk of fatality from 6.70 (95th CI: 5.44–8.26) for impacts with other vehicles, which was the most common crash, to 9.53 (7.28–12.47) for rollovers. Most crashes occurred in dry weather, typically during summer, in rural areas, most frequently on two lane roads, and in areas with speed limits between 30 and 55 mph. Factors associated with fatality for occupants in CVH included alcohol use, lack of seat belt use, and older age. Conclusions and Practical Applications: Crashes involving a CVH are a rare event but have catastrophic consequences when they do occur. Regulations that limit driving to daylight hours may lower the risk of crash involvement, and safety messaging to promote belt use and sober driving may also help. Additionally, as new "smart" vehicles are developed, engineers should keep in mind that older vehicles remain on the roadway. New driving technologies will need to safely interact with these older, less safe vehicles. [ABSTRACT FROM AUTHOR]

Microscopic traffic flow models enable predictions of traffic operations, which allows traffic engineers to assess the efficiency and safety effects of roadway designs. Modeling vehicle trajectories inside intersections is challenging because there is an infinite number of possible paths in a two-dimensional space, and drivers can simultaneously adapt their speeds as well. To date, human driver models for simultaneous longitudinal and lateral vehicle control based on the infrastructure characteristics and interactions with other drivers inside an intersection are still lacking. The contribution of this paper is threefold. First, it proposes an integrated microscopic traffic flow model to describe human-driven vehicle maneuvers under interactions. Drivers plan their heading and acceleration in the predicted future to minimize costs representing undesirable situations. The model works with a joint optimization for an interaction cost term. The weights associated with the interaction cost reflect how selfish or altruistic drivers are. Second, the proposed model endogenously gives the order of vehicles in case of crossing paths. Third, the paper develops a clustered validation method for microscopic traffic flow models with interacting vehicles, which account for interdriver variations. Results show that the model can accurately describe vehicle passing orders of interacting maneuvers, paths, and speeds against empirical data. The model can be applied to assess various intersection designs. Funding: This work was supported by the National Natural Science Foundation of China [Grants 71971140 and 52122215] and the Natural Science Foundation of Shanghai [Grant 20ZR1439300]. [ABSTRACT FROM AUTHOR]

For the improvement of active safety and the reduction of traffic collisions, significant efforts have been made on collision avoidance technology. However, the majority of the current research focus on the relative motion of the vehicle, with less consideration of the road adhesion condition. This paper designs Multiple fading factors Strong Tracking Unscented Kalman Filter (MSTUKF) algorithm to identify road adhesion coefficient and proposes the collision avoidance strategy based on road adhesion. First, according to sensor information, the most dangerous target is selected. Then, multiple fading factors are introduced to construct MSTUKF algorithm, and combined with the vehicle dynamics model to identify the current road adhesion coefficient. For better collision avoidance effect, the dual risk assessment of vehicle and road are proposed, in which TimeTo-Collision (TTC) is established according to the relative motion state of ego vehicle and the target, and Time-To-Avoidance (TTA) is calculated based on the road adhesion coefficient. Finally, using the feedforward and feedback control, the desired braking deceleration can be obtained by adjusting the master cylinder brake pressure. The simulation results show that the proposed algorithm can identify road adhesion coefficients with high accuracy and stability. Furthermore, the proposed strategy can provide good collision avoidance control effect under different scenarios. [ABSTRACT FROM AUTHOR]

Analyzing of the energy consumption (EC) in bus operation is important for reducing operating costs, improving sustainable solutions and creating environmentally friendly cities. The purpose of this paper is to identify the factors, such as passenger load, speed and acceleration, that affect significantly EC in bus operation. This paper builds a simulation framework for describing the level of energy based on the vehicle-engine combined (VEC) EC model. On the basis of the relationship between engine torque, power, traction and EC, the simulation framework mainly includes the road model, vehicle model, engine model and driver strategy. Furthermore, the correlations between energy consumption, passenger load, vehicle speed and acceleration are analyzed in different station spacing. The results show that the passenger load has a significant impact on EC of buses, and is related to the vehicle's speed and acceleration. Generally, the higher the maximum driving speed, the higher the EC of the bus. Acceleration strategies and maximum speed limits are critical factors determining the EC of bus for a certain passenger load and station spacing. For the same station spacing and maximum driving speed, the acceleration phase is under a greater contribution to the increase of EC. In addition, the greater the maximum speed limit or the acceleration, the greater the contribution percentage of EC increase in the acceleration phase. The simulation framework based on vehicle-engine combined EC model and specific fuel consumption maps can obtain the operating EC of buses for situations with different station spacing and maximum speed, which is conducive to vehicle operation EC analysis. Acceleration strategies and maximum speed limits are critical factors determining the EC of bus for a certain passenger load and station spacing. Therefore, energy savings can be obtained by optimizing the driving strategy. [ABSTRACT FROM AUTHOR]

A vehicle simulation model, CO 2 MPAS supports the introduction of the new WLTP-based certification system for CO 2 emissions in Europe. This paper investigates the possibility to use the underlying simulation methodology to accurately calculate CO 2 emissions over real-world trips, thus to extend the use of the methodology beyond vehicle certification. As a reference, the analysis used measurement data obtained from four vehicles over two different routes under real-world driving conditions. The CO 2 emissions were measured using portable emissions measurement systems. The formal CO 2 MPAS methodology and two modified versions of it that require a reduced number of input data were assessed about their capacity to predict the measured CO 2 emissions. The analysis focused on the accuracy and uncertainty of the three different methodology configurations. As an additional benchmark, the analysis considered the CO 2 emissions estimates obtained from the EMEP/EEA Guidebook methodology used for emissions inventorying in Europe. Results show that the basic CO 2 MPAS configuration demonstrates good performance in predicting CO 2 emissions over on-road tests, reaching a prediction accuracy over an entire test trip of −0,3% and a standard deviation of 3,1%. The modified versions showed slightly higher biases up to 3% and uncertainties (5–7%), but remaining within reasonable limits considering the reduced number of inputs used in each case. Given its ability to predict CO 2 emissions accurately on a local base, CO 2 MPAS could be used for the prediction of instantaneous CO 2 emissions in traffic micro-simulation exercises. [ABSTRACT FROM AUTHOR]

This paper presents a model predictive trajectory optimization and tracking framework to avoid collisions for autonomous vehicles in highly constrained environments. Firstly, a vehicle model is established in road coordinate system to describe the relationship between the vehicle and the reference road. Secondly, a numerical optimization method is applied to smoothen the reference path generated by waypoints. Then, a multilayer searched method is used to establish a safe driving corridor in highly constrained environments. In addition, an optimal path optimization and tracking framework based on model predictive control is formulated to improve the driving safety and comfort. The proposed framework considers the constraints of path boundaries and vehicle dynamics to provide the optimal control command. Furthermore, the speed profile is optimized based on the longitudinal motion model in space domain to ensure the constraints of speed limits and vehicle acceleration. Finally, the proposed algorithms are evaluated through experiments in various scenarios to demonstrate the effectiveness. [ABSTRACT FROM AUTHOR]

To solve the problems of low efficiency and unstable system in the traditional control, a longitudinal following model of the intelligent vehicle fleet was proposed based on the fuzzy model predictive control (MPC) method. According to the longitudinal following model of the fleet, the objective function of MPC including following performance and riding comfort was derived, and the fuzzy strategy was introduced based on the MPC controller. The followability weight coefficient in the MPC objective function was adjusted in real time, and the ideal acceleration was outputted to meet the needs of the driving scene. The lower-level controller was established by combining the longitudinal inverse dynamics model and PID control to convert the expected acceleration into throttle opening or brake pressure. The joint simulation platform of Carsim and Simulink was established to simulate the high-speed driving conditions of the intelligent vehicle fleet, and the simulation result was compared with those of PID method and traditional MPC method, respectively. The results show that the dynamic parameters of the fuzzy MPC controller under high-speed conditions can meet the constraints with the distance error less than 8 m. Compared with PID control and traditional MPC control, the maximum speed error is reduced by 6. 6 and 2. 5 m · s-1, respectively. In the emergency braking scenario, the speed change is more stable, and the riding comfort of the vehicle fleet is improved. [ABSTRACT FROM AUTHOR]

Developing vehicle finite element (FE) models that match real accident-involved vehicles is challenging. This is related to the intricate variety of geometric features and components. The current study proposes a novel method to efficiently and accurately generate case-specific buck models for car-to-pedestrian simulations. To achieve this, we implemented the vehicle side-view images to detect the horizontal position and roundness of two wheels to rectify distortions and deviations and then extracted the mid-section profiles for comparative calculations against baseline vehicle models to obtain the transformation matrices. Based on the generic buck model which consists of six key components and corresponding matrices, the case-specific buck model was generated semi-automatically based on the transformation metrics. Utilizing this image-based method, a total of 12 vehicle models representing four vehicle categories including family car (FCR), Roadster (RDS), small Sport Utility Vehicle (SUV), and large SUV were generated for car-to-pedestrian collision FE simulations in this study. The pedestrian head trajectories, total contact forces, head injury criterion (HIC), and brain injury criterion (BrIC) were analyzed comparatively. We found that, even within the same vehicle category and initial conditions, the variation in wrap around distance (WAD) spans 84–165 mm, in HIC ranges from 98 to 336, and in BrIC fluctuates between 1.25 and 1.46. These findings highlight the significant influence of vehicle frontal shape and underscore the necessity of using case-specific vehicle models in crash simulations. The proposed method provides a new approach for further vehicle structure optimization aiming at reducing pedestrian head injury and increasing traffic safety. • A new approach for generating case-specific vehicle models from a single-view vehicle image. • Parameterized shape-matched buck model for car-to-pedestrian collision FE simulations. • Head injury analysis based on 12 vehicle models representing four vehicle categories. • Emphasized the necessity of using case-specific vehicle models in crash simulations. • High potential for vehicle structure optimization aiming at reducing pedestrian head injury. [ABSTRACT FROM AUTHOR]

Appropriate vehicle lateral stability control is the key to ensure vehicle driving safety, whereas accurate lateral stability recognition can help improve the performance of vehicle control. In this article, the vehicle stability recognition and coordinated control are studied. Firstly, the vehicle dynamic model is established, through vehicle simulation software Carsim, the attribute dataset representing the vehicle lateral stability is further obtained. Then, clustering by fast search and find of density peaks method (CFSFDP) based procedure for the classification of the lateral stability as ‘Absolutely stability’, ‘Nearly stability’ and ‘Hardly stability’ is applied. The brain emotional learning network combined with genetic algorithm (GA-BEL) model is used to train datasets to recognize vehicle stability categories during driving. For the different recognition results, a coordinated control strategy based on active front steering control (AFS) and direct yaw moment control (DYC) is designed finally. Through the Carsim/Simulink co-simulations and Hardware-in-the-Loop tests under several typical driving conditions, the superiorities of the stability recognition method and the coordinated control strategy proposed in this paper are verified. [ABSTRACT FROM AUTHOR]

The fitted Graph HT ht conditional bivariate probability distributions clearly depict that the acceleration characteristics vary with the type of subject vehicle involved. Further, the modal values of the bivariate Graph HT ht copula models were estimated corresponding to speeds exceeding 20 kmph for all vehicle types. [Extracted from the article]

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]

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]

Vehicle windshield wiper system increases the driving safety by contributing a clear shot viewing to the driver. In this paper, modelling, designing and simulation of a vehicle windshield wiper system with robust control theory is done successfully. H∞ loop shaping and robust pole placement controllers are used to improve the wiping speed by tracking a reference speed signals. The reference speed signals used in this paper are step and sine wave signals. Comparison of the H∞ loop shaping and robust pole placement controllers based on the two reference signals is done and convincing results have been obtained. Finally the comparative results prove the effectiveness of the proposed H∞ Loop Shaping controller to improve the wiping mechanism for the given two reference signals. [ABSTRACT FROM AUTHOR]

Based on the demand of vehicle emission research and control, this paper presents the development of a portable vehicle measurement system (PEMS) based on SEMTECH-DS and ELPI+, the vehicle emission tests carried out on actual roads, and the data obtained for the establishment and validation of a vehicle emission model. Based on the results of the vehicle emission test, it was found that vehicle driving conditions (speed, acceleration, vehicle specific power (VSP), etc.) had a significant impact on the pollutant emission rate. In addition, local driving cycles were generated and the frequency distribution of VSP-bin under different cycles was analyzed. Then, through the establishment of an emission rate database, calculation of emission factors and validation of the emission model, a vehicle emission model based on actual road driving conditions was developed by taking VSP as the "surrogate variables". It showed that the emission factor model established in this study could better reflect the vehicle transient emissions on the actual road with high accuracy and local adaptability. Through this study, it could be found that due to the great differences in traffic development modes and vehicle driving conditions in different cities in China, the emission model based on driving conditions was a better choice to carry out the research on vehicle emission in Chinese cities. Compared with directly applying international models or quoting the recommended values of relevant macroscopic guidelines, the emission factor model established in this study, using actual driving conditions, could better reflect the vehicle transient emissions on the actual road with high accuracy and local adaptability. In addition, due to the rapid development of China's urban traffic and the rapid change of driving conditions, it was of great significance to regularly update China's urban conditions to improve the accuracy of the model, no matter which model was chosen. [ABSTRACT FROM AUTHOR]

There is consensus in industry and academia that Highly Automated Vehicles (HAV) and Connected Automated Vehicles (CAV) will be launched into the market in the near future due to emerging autonomous driving technology. In this paper, a mixed traffic simulation framework that integrates vehicle models with different automated driving systems in the microscopic traffic simulation was proposed. Currently, some of the more mature Automated Driving Systems (ADS) functions (e.g., Adaptive Cruise Control (ACC), Lane Keeping Assistant (LKA), etc.) are already equipped in vehicles, the very next step towards a higher automated driving is represented by Level 3 vehicles and CAV which show great promise in helping to avoid crashes, ease traffic congestion, and improve the environment. Therefore, to better predict and simulate the driving behavior of automated vehicles on the motorway scenario, a virtual test framework is proposed which includes the Highway Chauffeur (HWC) and Vehicle-to-Vehicle (V2V) communication function. These functions are implemented as an external driver model in PTV Vissim. The framework uses a detailed digital twin based on the M86 road network located in southwestern Hungary, which was constructed for autonomous driving tests. With this framework, the effect of the proposed vehicle models is evaluated with the microscopic traffic simulator PTV Vissim. A case study of the different penetration rates of HAV and CAV was performed on the M86 motorway. Preliminary results presented in this paper demonstrated that introducing HAV and CAV to the current network individually will cause negative effects on traffic performance. However, a certain ratio of mixed traffic, 60% CAV and 40% Human Driver Vehicles (HDV), could reduce this negative impact. The simulation results also show that high penetration CAV has fine driving stability and less travel delay. [ABSTRACT FROM AUTHOR]

The purpose is to study the handling stability and energy saving mode of energy saving electric vehicles. First, the differences between electric vehicles and traditional fuel vehicles are analyzed. Moreover, based on active safety technology, the control stability, handling stability and driving safety of electric vehicles are improved. Then, based on the vehicle dynamics theory and the proposed assumptions, the modular electric vehicle dynamics model is established, including the wheel model, tire model and driver model, which can effectively control the error of the vehicle model and reduce the difficulty and complexity of vehicle modeling. Next, based on the model predictive control theory, the vehicle steady-state control strategy is established, which can predict and control the state of the electric vehicle in the compound condition and improve the control stability of the vehicle. Finally, the vehicle dynamic model and steady-state control system are simulated and tested. The experimental results show that the simulation results of the established dynamic model of the electric vehicle in angle step condition and single line shifting condition are consistent with the results of professional software, which shows that the established dynamic model can accurately describe the dynamic state of electric vehicle and provides the basis for the research of vehicle stability control strategy. Experimental results of the control strategy under the condition of double line shifting show that the designed control strategy can accurately track the state of the system and constrain the deviation in a small range, so as to ensure the driving stability of the electric vehicle. This exploration provides a reference for the study of dynamic analysis and handling stability control strategy of electric vehicles. [ABSTRACT FROM AUTHOR]

present study aims to develop vehicle type-wise operating-speed prediction (OSP) models for heterogeneous traffic on twolane rural highways in Indian hilly terrains. For the present study, 27 curves with varying geometric characteristics located along the National Highway (NH-953) connecting Netrang and Rajpipla in the western state of Gujarat, India, were selected. Speed data were collected using radar guns at three curve locations (entry point, midpoint, and exit point) in each travel direction for three dominant types of vehicles: motorized two-wheelers (2W), cars, and heavy commercial vehicles (HCVs). OSP models were developed for different vehicle types at three curve points using the backward elimination stepwise regression (BSR) technique. The results revealed that the preceding curve point's operating speed, curve length, and tangent length positively affected operating speed. In contrast, deflection angle, curve sharpness, and grade had adverse effects. The curve geometric characteristics had the most negligible impact on the operating speed of 2Wand a significant effect on HCV. Among all the curve-related aspects, curve length was the most significant variable and affected the speed of all three vehicle types, followed by curve sharpness. Further, the developed OSP models were applied to the other hilly terrain to check the transferability of the model. As an important outcome, the developed OSP models were used to evaluate geometric design consistency. This highlights the need for geometric and traffic-calming measures to improve highway operating-speed consistency and driver safety. [ABSTRACT FROM AUTHOR]

A novel longitudinal, lateral, and vertical integrated stability control system is proposed for distributed electric vehicle in this paper. The integrated stability control system is based on model predictive control and takes advantage of its multi-objective optimization feature to takes manipulability, stability and comfort requirements into consideration at the same time, and realize the optimal longitudinal $/$ lateral $/$ vertical forces distribution with constraints overall considered. The advantage of the proposed integrated control system is that it can solve the control objective conflict of the cooperative motion control subsystems and the interference of the actuators compared to distributed control system. Finally, the simulation and comparison analysis are given in several different driving conditions under Carsim and Matlab $/$ Simulink environment. Results show that an improvement of over 80 $\%$ in the pitch angle and roll angle is realized compared to the simple control strategy. [ABSTRACT FROM AUTHOR]

To evaluate rutting based on driving quality of vehicles, three pavement models were established in view of the morphological characteristic of pavement ruts and friction coefficients, and the control models of vehicles conducting the braking were established according to different driving conditions. Lateral acceleration, roll angle, yaw angle, and lateral offset were adopted to describe the driving quality and safety of vehicles. The effects of different rut depths and width-height ratios on driving quality of vehicles under different driving conditions were analyzed. Results show that when a vehicle drives across a rut on a dry pavement at a speed of 100 km/h, the rut depth should be less than 20 mm to ensure the driving stability of vehicle. Road engineers and researchers should pay attention to this situation that width-height ratios of sidewall are 1 or smaller. When one wheel of a vehicle is braked in a rut groove with water, the driving speed of vehicles should be strictly controlled within 120 km/h, and the rut depth for road with a high driving speed should be less than 10 mm to ensure the driving stability and safety. The presented findings may make a contribution to maintenance decision-making. [ABSTRACT FROM AUTHOR]

This paper introduces the LQR control algorithm for the active suspension system. Because the model of the vehicle dynamics used in this paper includes a hydraulic actuator, therefore, this model will include five state variables. Besides, the process of linearization of the hydraulic actuator is also shown in this paper. This is a completely novel and original method. It is possible to describe almost all the characteristics of hydraulic actuators with just one linear differential equation. Also, the parameters of the LQR controller are optimized through the in-loop optimization algorithm. The results of the research showed that the values of displacement and acceleration of the sprung mass were significantly reduced when this algorithm was used. In the investigation cases, these values usually do not exceed 2.68% and 43.00% compared to the situation of the vehicle using only a passive suspension system. Therefore, ride comfort and stability can be enhanced in many driving conditions when the active suspension system with the LQR control algorithm is used. [ABSTRACT FROM AUTHOR]

This paper presents a novel estimation and identification approach for lateral vehicle dynamics. The algorithm leverages on a Linear Fraction Transform (LFT) reformulation of vehicle and tyre models, allowing for a simple and computationally efficient inclusion of complex and nonlinear dynamic models, like, for example, two-wheels, four-wheels or single-track as vehicle model, and Pacejka, brush or Fiala as tyre model. As a result, this technique can be easily adopted in the development of an online identification system, able to run on a standard embedded device, implementing a flexible identification procedure that can handle different driving conditions, up to the limits of handling, different vehicle modelling approaches, and different input measurements. Experimental results demonstrate the effectiveness of the proposal, either in a persistent excitation and in a non-persistent excitation scenario. [ABSTRACT FROM AUTHOR]

Path planning is a critical part for improving the driving safety and driver comfort of autonomous vehicles (AVs), especially in complex maneuvering conditions. In addition, different drivers have different preferences for AVs, thus, how to provide personalized trajectories for different drivers is a vital issue for AVs. The collision-free path planning problem in conditions with large road curvatures is investigated in this paper, with the consideration of environmental safety constraints, drivers’ comfort, vehicle actuator constraints, etc. Firstly, a Driver-Vehicle-Road (DVR) system is established based on the combination of the kinematic vehicle model and the two-point visual preview driver model, such that the driver's individual handling characteristics can be considered in the controller. The kinematic vehicle model is modified to have the similar understeering characteristics with those of the nonlinear full car models, and then the proposed DVR system can satisfy different groups of drivers and cars. Secondly, for environmental constraints, a new artificial potential field (APF) method is proposed, which can form a banana-shaped 3-D dangerous imaginary mountain and a lane boundary cliff suitable for arbitrary curvature roads to generate a collision-free evasive path. Finally, the Linear-Time-Varying (LTV) model predictive control (MPC) method is adopted to design the path planner. The CarSim-Simulink joint simulation illustrates that with the proposed planner, the host vehicle is capable of avoiding obstacles with a safer and more comfortable maneuver on large curvature roads. And the proposed path planner can provide individually safe trajectories for different drivers with good maneuverability. [ABSTRACT FROM AUTHOR]

• A latent class-based framework to model motorized two-wheeler driver behaviour incorporating drivers' strategic intents, multi-vehicle anticipation, and two-dimensional (2D) movements in heterogeneous, disorderly (HD) traffic conditions. • An empirical application using vehicular trajectory data of motorized two-wheelers from an arterial road in Chennai, India. This study formulates a latent class-based driving behaviour framework for modelling vehicles' two-dimensional (2D) movements while considering drivers' strategic intents and multi-vehicle anticipation (MVA) in heterogeneous, disorderly (HD) traffic conditions. Specifically, five extensions are proposed to a typical stimulus–response based driving behaviour framework. First, the subject vehicle's 2D movements are represented as a combination of the angular direction of movement with respect to the longitudinal axis and the magnitude of acceleration or deceleration along the angle. Second, a latent class framework is used to recognise drivers' strategic intents (latent to the analyst) in two aspects: (a) the intent to accelerate, decelerate, or maintain a constant speed, and (b) the intent to steer to the left of, right of, or straight along the longitudinal axis. It is hypothesised that these strategic intents precede tactical decisions, such as how much to accelerate or decelerate and which specific angular direction to move along. Third, the MVA effect is accommodated to recognise that drivers consider stimuli from multiple vehicles in their vicinity. Fourth, a multi-stimuli model of acceleration (deceleration) is formulated, assuming that drivers choose an angle of movement that allows them to move with the highest (lowest) possible longitudinal acceleration (deceleration). Fifth, drivers' execution errors are recognised as the difference between their planned acceleration and executed acceleration. The proposed framework is applied for an analysis of motorised two-wheeler driver behaviour using a vehicular trajectory dataset from India. The empirical results highlight the importance of incorporating MVA and considering driver's intents while modelling 2D movements of vehicles in HD traffic conditions. Further, the microscopic traffic environment variables are found to have a stronger influence on drivers' higher-level, strategic intents than on their lower-level, tactical decisions. [ABSTRACT FROM AUTHOR]

• Propose the first multi-fidelity method for reliability-based single-vehicle crash (SVC) risk assessment. • Develop a versatile high-fidelity SVC model and interface for reliability analysis. • Develop a low-fidelity SVC model to provide prior information at low cost. • Propose a multi-fidelity improved cross entropy-based importance sampling algorithm. • Demonstrate the method with case studies including comparisons with counterparts. Road vehicles are highly susceptible to single-vehicle crashes (SVCs) under complex road geometry and inclement weather, which can significantly threaten traffic safety and mobility of the whole traffic system. Most existing studies involve various simplifications and approximations to assess the associated SVC risks promptly, and therefore the assessment accuracy is often compromised. A novel multi-fidelity approach is developed for the reliability-based risk assessment of SVCs to balance the simulation accuracy and efficiency. Specifically, a high-fidelity transient dynamic vehicle model is introduced for a robust estimation of the vehicle dynamics under various driving environments, assisted by a low-fidelity simplified physics-based vehicle model to improve the computational efficiency. Based on the simulations of the two models, a new multi-fidelity improved cross entropy-based importance sampling (MFICE) algorithm is proposed for integrating multi-fidelity information and facilitating accurate and efficient reliability analysis. Five demonstrative cases are studied to evaluate the performance of the proposed approach, including the comparison with existing representative approaches. The results show that the proposed innovative multi-fidelity approach can provide a reliability evaluation of SVCs both accurately and efficiently, with obviously superior performance over typical state-of-the-art counterparts. Therefore, the proposed approach bears great potential on developing proactive and near real-time intelligent traffic operation and management strategies against SVCs in both normal and hazardous conditions. [ABSTRACT FROM AUTHOR]

In this study, we selected four real-world rear-end crash scenarios with different crash characteristics. The vehicles involved in those crashes were not equipped with any crash avoidance systems. We then used the accident reconstruction method to build those crash scenarios in PC-Crash software. Then, different FCW/AEB safety algorithms have been defined for a subject vehicle model in each crash scenario and each scenario was simulated for a set of input parameters such as vehicle speed, brake intensity, and driver reaction time. The range and distribution of input parameters were extracted from the related field crash data and available literature. A total number of 16000 simulations have been conducted which produced input-output datasets for further investigations. Finally, the effects of input parameters on simulation outcomes including crash occurrence, AEB activation, injury risk, and vehicle damage have been quantified using the Boruta algorithm. The results indicated that the overall effectiveness of the AEB system was a 57% reduction of rear-end crashes, a 52% reduction of injury severity (striking vehicle's passengers), and a 47% reduction of damages for striking vehicles. The results also showed that the available AEB algorithms were more effective for the average speed equal to or less than 80 kmph. The speed of the subject vehicle, type of AEB algorithm, sensor detection range, and driver reaction time were the most important parameters on crash outcomes. In addition, the results indicated that the performance of FCW had a direct impact on the effectiveness of the AEB system for the integrated FCW + AEB system. [ABSTRACT FROM AUTHOR]

By installing on-board diagnostics (OBD) on tested vehicles, the after-treatment exhaust emissions can be monitored in real time to construct driving cycle-based emission models, which can provide data support for the construction of dynamic emission inventories of mobile source emission. However, in actual vehicle emission detection systems, due to the equipment installation costs and differences in vehicle driving conditions, engine operating conditions, and driving behavior patterns, it is impossible to ensure that the emission monitoring data of different vehicles always follow the same distribution. The traditional machine learning emission model usually assumes that the training set and test set of emission test data are derived from the same data distribution, and a unified emission model is used for estimation of different types of vehicles, ignoring the difference in monitoring data distribution. In this study, we attempt to build a diesel vehicle NOx emission prediction model based on the deep transfer learning framework with a few emission monitoring data. The proposed model firstly uses Spearman correlation analysis and Lasso feature selection to accomplish the selection of factors with high correlation with NOx emission from multiple sources of external factors. Then, the stacked sparse AutoEncoder is used to map different vehicle working condition emission data into the same feature space, and then, the distribution alignment of different vehicle working condition emission data features is achieved by minimizing maximum mean discrepancy (MMD) in the feature space. Finally, we validated the proposed method with the diesel vehicle OBD data that were collected by the Hefei Environmental Protection Bureau. The comprehensive experiment results show that our method can achieve the feature distribution alignment of emission data under different vehicle working conditions and improve the prediction performance of the NOx inversion model given a little amount of NOx emission monitoring data. [ABSTRACT FROM AUTHOR]

The driving state of a self-driving vehicle represents an important component in the self-driving decision system. To ensure the safe and efficient driving state of a self-driving vehicle, the driving state of the self-driving vehicle needs to be evaluated quantitatively. In this paper, a driving state assessment method for the decision system of self-driving vehicles is proposed. First, a self-driving vehicle and surrounding vehicles are compared in terms of the overtaking frequency (OTF), and an OTF-based driving state evaluation algorithm is proposed considering the future driving efficiency. Next, a decision model based on the deep deterministic policy gradient (DDPG) algorithm and the proposed method is designed, and the driving state assessment method is integrated with the existing time-to-collision (TTC) and minimum safe distance. In addition, the reward function and multiple driving scenarios are designed so that the most efficient driving strategy at the current moment can be determined by optimal search under the condition of ensuring safety. Finally, the proposed decision model is verified by simulations in four three-lane highway scenarios. The simulation results show that the proposed decision model that integrates the self-driving vehicle driving state assessment method can help self-driving vehicles to drive safely and to maintain good maneuverability. [ABSTRACT FROM AUTHOR]

The vehicle needs to be able to assist the driver in realizing the control strategy of emergency collision avoidance to ensure the safety of car drivers during driving. This article introduces the basic principles of vehicle emergency collision avoidance using time to collision (TTC) and Berkeley safety distance models. In addition, the Berkeley model was improved so that it could be adjusted for different driving styles. On the simulation platform, simulation experiments were carried out on the driving conditions of the front vehicle at a standstill state, at a constant speed and at a deceleration state. The performance of three collision avoidance strategies were analysed. The results showed that the extroverted driver had the fastest response speed, followed by the moderate driver and the introverted driver; no matter what the working condition, the increase in the driving speed of the driver could reduce the distance between the vehicle and the vehicle in front when the vehicle stopped due to braking, increasing the risk of rear-end collision; no matter what the working condition, the anti-collision strategy that adopted the improved Berkeley model could effectively adapt to different driving styles, keeping a stable distance from the front vehicle when stopping due to braking and the distance between the vehicles was larger than the other two collision avoidance strategies. [ABSTRACT FROM AUTHOR]

An 8-DOF three-axle vehicle model with semi-active suspension is built in this paper, of which the accuracy is verified through simulations and experiments. Based on the optimal control theory, the linear quadratic Gaussian controller for semi-active suspension is designed with 10 evaluation indicators. Considering the deficiency of linear quadratic Gaussian control weight coefficients based on experience, analytic hierarchy process is employed to determine the weight coefficients of each indicator. The control effect is analyzed through MATLAB/Simulink. The adaptability of proposed control strategy under 25 driving conditions is analyzed with different road grades and speeds. The driving condition of "70 km/h travel speed on the road of grade B" is selected, under which the comparison of vehicle responses between semi-active suspension and passive suspension is made. Results show that the vertical vibration is effectively diminished by using semi-active suspension with linear quadratic Gaussian controller. Compared with passive suspension, the riding comfort is improved and the adverse effect on handling stability is eliminated. The three-axle vehicle with semi-active suspension has good adaptability to various working conditions. [ABSTRACT FROM AUTHOR]

Each year the National Highway Traffic Safety Administration and Insurance Institute for Highway Safety release safety ratings for popular makes and models of vehicles produced. We link these safety ratings with the crash data provided in the National Automotive Sampling System General Estimates System to study the efficacy of safety ratings as a predictor of the likelihood a passenger will be injured in a crash. We also consider the case of predicting a severe injury or death as a function of safety rating. A web-based dashboard was developed to graphically explore the relationship among these datasets. By considering the proportion of occupants (severely) injured per crash, we find that vehicle safety ratings generally do not have as much of an effect on likelihood of injury as one might expect, although vehicles with higher ratings can result in significant relative improvement. Our web application allows a user to explore different aspects of crashes (e.g., use of alcohol, speeding, etc.) and compare injuries and safety rating performance based on these conditions. Lastly, our dashboard allows a user to see differences between the NHTSA and IIHS ratings systems and how they correspond to the crash records. [ABSTRACT FROM AUTHOR]

Safety at high speed is the most important performance of autonomous vehicle. Wherein, yaw stability control (YSC) is a key technology to improve vehicle safety. However, the high-speed vehicles are easy to be out of control in nonlinear region, which will result in serious accidents. This study proposes a model predictive control (MPC) method based on the Takagi-Sugeno (T-S) fuzzy model to realize YSC in the nonlinear region. Firstly, a T-S fuzzy model of vehicle is established based on a two-degree-of-freedom model to describe the vehicle dynamic characteristics in the nonlinear region. Then, a robust MPC is designed and its solution is given in the linear matrix inequalities (LMIs) form. Next, a sliding mode controller (SMC) is proposed to prevent wheel lock in the YSC process. Finally, the proposed strategy is verified in simulations and experiments. The results show that the proposed strategy has similar performance in vehicle stable region with linear MPC, and shows the ability to suppress the instability of the vehicle in the nonlinear region, and its computation burden is acceptable. [ABSTRACT FROM AUTHOR]

To improve the curve driving stability and safety under critical maneuvers for four-wheel independent drive autonomous electric vehicles, a three-stage direct yaw moment control (DYC) strategy design procedure is proposed in this work. The first stage includes modeling of the tire nonlinear mechanical properties, i.e. the coupling relationship between the tire longitudinal force and the tire lateral force, which is crucial for the DYC strategy design, in the STI (Systems Technologies Inc.) form based on experimental data. On this basis, a 7-DOF vehicle dynamics model is established and the direct yaw moment calculation problem of the four-wheel independent drive autonomous electric vehicle is solved through the nonsingular fast terminal sliding mode (NFTSM) control method. Thus optimal direct yaw moment can be obtained. To achieve this direct yaw moment, an optimal allocation problem of the tire forces is further solved by using the trust-region interior-point method, which can effectively guarantee the solving efficiency of a complex optimization problem such as the tire driving and braking forces allocation of four wheels in this work. Finally, effectiveness of the DYC strategy proposed for autonomous electric vehicles is verified through the CarSim-Simulink co-simulation results. [ABSTRACT FROM AUTHOR]

Active safety systems contribute significantly to the safe driving of autonomous vehicles in hazardous circumstances. However, conventional active safety systems that mainly depend on braking intervention may not yield the desired vehicle behaviour in critical situations where the tyre forces tend to saturate. Over-actuation through individually cambering the wheels provides a possibility of overcoming this difficulty, as extra lateral tyre forces can potentially be produced. This paper presents active camber for improving the path following and yaw stability performance of over-actuated autonomous electric vehicles (AEVs). With a modified Dugoff tyre model, the camber effect on the lateral tyre force is modelled as an effective linear component. The modified tyre model, together with a double-track vehicle model, is utilised for active camber of the AEVs. The camber controller is developed in the framework of model predictive control (MPC), including both actuator- and safety-related constraints. The camber controller is investigated at different camber rates and road friction levels, in terms of path following, yaw stability and vehicle velocity gain. [ABSTRACT FROM AUTHOR]

• Heterogeneity of drivers and vehicles makes platoons more unstable, on average. • L ∞ characterization of string stability is more appropriate than L 2 one. • Analytical sufficient condition for L ∞ string stability is derived. • L p norm variability due to platoon length, speed, and parameters pdfs is analysed. • Parameters pdfs shape and correlation sensibly affect string stability. This paper shows that the heterogeneity of drivers' and vehicles characteristics makes platoons, on average, more string-unstable. However, the string instability degree of unstable platoons is much higher in a homogeneous flow than in a heterogeneous one. These results are based on an L ∞ characterization of string stability, which is shown to be the most appropriate one from a traffic safety viewpoint. Mechanisms and conditions are discussed in which an L 2 characterization is not able to capture the amplification of a speed drop through a string of vehicles. An analytical sufficient condition for the L ∞ string stability of heterogeneous vehicles, which move according to a general class of car-following models, is derived. Above all, a thorough comparison of L ∞ and L 2 string stability characterizations between a homogeneous and a heterogenous flow, is performed. To this aim, the L p norms of heterogeneous platoons are calculated within a quasi-Monte Carlo framework. The variability of the L p norm values due to the platoon length, the equilibrium speed, and the probability distribution model of the uncertain vehicle parameters, is analysed. Overall, it is shown that the platoon stability behaviour sensibly changes with the shape and the correlation structure of vehicle model parameter distributions. Therefore, traffic heterogeneity needs to be modelled in order to correctly characterize the string stability of a mixed traffic flow. [ABSTRACT FROM AUTHOR]

In view of the current situation awareness and early warning model of vehicle collision risk at traffic intersection does not consider the discrimination threshold, which leads to low early warning accuracy and high early warning missing rate, a situation awareness and early warning model of vehicle collision risk based on Internet of vehicles technology is proposed. Using the wireless communication technology of Internet of vehicles, the traffic data information is transmitted to the cloud platform to extract the vehicle running state; using the situation awareness theory, the situation awareness framework of vehicle collision risk is established, and the situation rules of vehicle collision risk are extracted by analyzing the observation data of safe driving state. Through the attribute reduction algorithm, the redundant information and standard attributes which are less related to the decision attributes in the universe space are removed, and the discrimination threshold is determined; the vehicle position information is obtained by using the GPS positioning module of the Internet of vehicles, and the collision position and collision time difference are calculated through different vehicle trajectories, so as to realize the situation awareness and early warning of vehicle collision risk at traffic intersections. The experimental results show that the accuracy of the proposed method is 92%, and the missing rate is only 0.2%. [ABSTRACT FROM AUTHOR]