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In this study, the Bald Eagle Search Algorithm performed hydrogen consumption and battery cycle optimization of a fuel cell electric vehicle. To save time and cost, the digital vehicle model created in Matlab/Simulink and validated with real-world driving data is the main platform of the optimization study. The digital vehicle model was run with the minimum and maximum battery charge states determined by the Bald Eagle Search Algorithm, and hydrogen consumption and battery cycle values were obtained. By using the algorithm and digital vehicle model together, hydrogen consumption was minimized and range was increased. It was aimed to extend the life of the parts by considering the battery cycle. At the same time, the number of battery packs was included in the optimization and its effect on consumption was investigated. According to the study results, the total hydrogen consumption of the fuel cell electric vehicle decreased by 57.8% in the hybrid driving condition, 23.3% with two battery packs, and 36.27% with three battery packs in the constant speed driving condition. [ABSTRACT FROM AUTHOR]

In response to the issues of low merging success rates and poor safety in the on-ramp merging scenario within autonomous driving, we propose an on-ramp merging model for unmanned vehicles based on the Multi-Agent Proximal Policy Optimization (MAPPO) algorithm. Firstly, we introduce an Action-Mask (AM) to prevent the sampling of invalid actions during merging, thus enhancing safety by ensuring only valid actions are considered. Secondly, we incorporate noise advantage values to encourage unmanned vehicles to thoroughly explore the environment and avoid being trapped in local optimal solutions. Experimental results demonstrate that the AM-MAPPO algorithm model improves both safety and traffic efficiency. [ABSTRACT FROM AUTHOR]

This paper considers external disturbances imposed on vehicle systems. Based on a vehicle dynamics model of the vehicle with three degrees of freedom (3-DOFs), a union disturbance observer (UDO) composed of a nonlinear disturbance observer (NDO) and an extended state observer (ESO) was designed to obtain external disturbances and unmodeled items. Meanwhile, an improved adaptive unscented Kalman filter (iAUKF) with anti-disturbance and anti-noise properties is proposed, based on the UDO and the unscented Kalman filter (UKF) method, to evaluate the sideslip angle of vehicle systems. Finally, a vehicle yaw stability controller was designed based on UDO and the global fast terminal sliding mode control (GFTSMC) method. The results of co-simulation demonstrated that the proposed UDO was effectively able to observe external disturbances and unmodeled items. The proposed iAUKF, which considers external disturbances, not only achieves adaptive updating and adjustment of filtering parameters under different sensor noise intensities but can also resist external disturbances, improving the estimation accuracy and robustness of the UKF. In the anti-disturbance performance test, the maximum estimation error of the sideslip angle of the iAUKF under the three working conditions was less than 0.1°, 0.02°, and 0.5°, respectively. Based on the UDO and the GFTSMC, a vehicle yaw stability controller is described, which improves the accuracy of control and the robustness of the vehicle's stability control system and greatly strengthens the driving safety of the vehicle. [ABSTRACT FROM AUTHOR]

Advanced finite element (FE) modeling and simulations were performed on vehicular crashes into a three-bar metal bridge rail (TMBR). The FE models of a sedan, a pickup truck, and a TMBR section were adopted in the crash simulations subject to Manual for Assessing Safety Hardware (MASH) Test Level 2 (TL-2) and Test Level 3 (TL-3) requirements. The test vehicle models were first validated using full-scale physical crash tests conducted on a two-bar metal bridge using a sedan and a pickup truck with similar overall physical properties and sizes to their respective vehicles used in the simulations. The validated vehicular models were then used to evaluate the crash performance of the TMBR using MASH evaluation criteria for structural adequacy, occupant risk, and post-impact trajectory. The TMBR met all MASH TL-2 requirements but failed to meet the MASH TL-3 Criteria H and N requirements when impacted by the sedan. The TMBR was also evaluated under in-service conditions (behind a 1.52 m wide sidewalk) and impacted by the sedan under MASH TL-3 conditions. The simulation results showed that the TMBR behind a sidewalk met all safety requirements except for the occupant impact velocity in the longitudinal direction, which exceeded the MASH limit by 3.93%. [ABSTRACT FROM AUTHOR]

Lane-keeping assistance (LKA) systems aim to address the vehicle deviation problem from normal lanes due to driver distraction, and their performance is affected by factors such as the stability of the steering system and external interference. This study proposed a vehicle LKA strategy based on the electric power steering system (EPS) to reveal the relationship between the control strategy and the performance of LKA systems. Firstly, an EPS system, a vehicle three-degree-offreedom dynamics model, and a driver model were established for the nonlinear electromechanically coupled vehicle EPS system. Both the serial lane-keeping control strategy for the EPS system controller based on robust sliding mode observers and the model prediction controller based on vehicle trajectory were devised. Then, based on various driving conditions, the vehicle's intervention coefficient outputs were dynamically adjusted in accordance with the lateral safety deviation and the driver's manual force. Finally, the intervention coefficients of the lane-keeping system were designed, and the accuracy of the control strategy was verified through experiments. Results show that after adding lane-keeping auxiliary control, the wheel turning angle is reduced by a maximum of 9°, the traverse angular velocity is decreased by a maximum of 0.52 rad, and the vehicle lateral offset is reduced by a maximum of 3.4 m. This study provides a significant reference for constructing vehicle models and evaluating the performance of LKA control strategies. [ABSTRACT FROM AUTHOR]

The huge growth in the number of vehicles is causing serious traffic management problems. Existing roads must handle traffic more than expected which presents serious challenges including congestion and safety. Intelligent traffic management systems were proposed as a solution to solve such problems. This intelligent system is charged to estimate vehicle density and manage the traffic flow accordingly. A wide variety of sensors was deployed for the traffic management system such as cameras, ultrasonic sensors, radar, infrared sensors, and acoustic sensors. In this work, an intelligent traffic flow management system was proposed based on data provided by public surveillance cameras. For this purpose, a real-time vehicle detection and counting model based on You Look Only Once (YOLO) v6 was deployed. The proposed model was trained and evaluated on different publicly available datasets such as BDD 100K and KITTI. Extensive experimentations proved the efficacy of the proposed model for detecting vehicles while operating under real-time constraints. The proposed traffic flow management system was evaluated using real videos for different traffic scenarios, and good results were achieved. [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]

The investigation on vehicle–bridge interaction systems (VBIS) plays an important role in evaluating the performance of bridges and/or vehicles. However, due to the limitations of traditional simulation methods and testing methods, the impact of vehicle jumping (VJ) on the VBIS has never been precisely disclosed. In order to accurately and comprehensively reveal the impact of VJ on the dynamic performance of VBIS, one real-time hybrid simulation method (RHSM) for VBIS considering the impact of VJ (VBIS-VJ) is proposed in this paper. In the proposed method, the vehicle system is chosen as the experimental substructure loaded by servo-hydraulic actuator loading system in real-time, whilst the bridge system is chosen as the numerical substructure numerically simulated in one computer. During the vehicle jumping, the vehicle and bridge vibrate freely respectively, otherwise, the experimental substructure and numerical substructure are synchronized at the coupling points in terms of force equilibrium and deformation compatibility in RHSM for VBIS-VJ. Firstly, the principle and numerical models of RHSM for VBIS-VJ are proposed. Furthermore, numerical simulations of RHSM for VBIS-VJ are conducted for verifying the feasibility of the proposed method. Finally, the influence of jumping position, jumping height, and driving speed on the VBIS is disclosed by hybrid simulations. It is shown that compared with the ever-known investigation methods, the proposed method could economically and accurately disclose the dynamic performance of VBIS-VJ. It is shown from the simulation results that compared with the referenced results, the quantitative errors of the proposed method with quarter vehicle model and half vehicle model are less than 2.5152% and 3.5599% respectively, which prove the effectiveness and accuracy of proposed RHSM for VBIS-VJ. The peak displacement of the bridge at a quarter-span and mid-span exceeds the three-quarters span by about 1.9–2.9 mm. The influence of jumping position of a quarter span and midspan of bridge is much huger than that in the three-quarters span. The dynamic response of bridge increases with the increase the jumping height, but the deformation of the bridge is reduced when the jumping height is more than 0.05 m. Meanwhile, the requirements for experimental loading equipment are acquired through the hybrid simulations, which could be used as a reference for selecting the loading system of the RHSM for VBIS-VJ. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) have gained importance in recent years, prompting the development of several control systems to improve their efficiency and performance. In this work, a quarter electric vehicle (QEV) was controlled using a conventional proportional integral derivative (PID) and fuzzy controller to examine and compare with the response of the adaptive neurofuzzy inference system (ANFIS) controller. The response of the ANFIS controller was evaluated using MATLAB/Simulink according to different parameters and compared with those of other controllers. In addition, the simulation was based on different driving conditions such as the acceleration and deceleration modes and the type of road: wet and dry. The simulations were carried out on a longitudinal electric vehicle model based on a brushless DC motor, including the Pacejka tire model. The results showed that the ANFIS controller outperformed the PID and fuzzy logic controllers, providing superior dynamic responsiveness and stability when the ANFIS controller smoothly followed the input speed and the longitudinal slip value reached 3%. [ABSTRACT FROM AUTHOR]

The increase in population is accompanied by an increase in the number of vehicles. It is inevitable that the number of vehicle accidents will also increase, which can be caused by various factors. Driver factors reviewed in this study include socioeconomic characteristics, movement characteristics, accident characteristics, and driver behavior characteristics. the purpose of this study is to study the vehicle accident model using interviews and Driving Behavior questionnaires with a total of 307 motorist respondents who have experienced accidents. Driver factors reviewed in this study include socioeconomic characteristics, movement characteristics, accident characteristics, and driver behavior characteristics using interviews and Driving Behavior questionnaires with a total of 307 motorist respondents who have experienced accidents. This investigate used SEM (Structural Equation Modeling) with SmartPLS computer software. Two-wheeled vehicle accident modeling results Y = –0.234X1+0.153X3+ +ei2; R² = 0.102. The greatest influence occurs in the characteristics of driver behavior (X3), namely Ordinary Violation, and for four-wheeled vehicle accident modeling results, Y = –0.343X1+0.284X3+ei2; R² = 0.217. The greatest influence occurs in driver behavior characteristics (X3), namely Ordinary Violation. Ordinary Violation is defined as a deliberate deviation from the rule of law. Thus, from the research results, the most influential variable was the behavior of drivers who committed ordinary violations such as ignoring speed limits, breaking through intersections, and driving under the influence of alcohol. So, there needs to be collaboration between the police and related parties in tackling accidents and reducing the risk of traffic accidents, such as long as socialization or information through newspapers or electronic media to the public in Jayapura City regarding the importance of collective awareness of driving safety [ABSTRACT FROM AUTHOR]

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]

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]

Precise trajectory prediction is pivotal for autonomous vehicles operating in real-world traffic conditions, and can help them make the right decisions to ensure safety on the road. However, state-of-the-art approaches consider limited information about the historical movements of vehicles. On highways, drivers make their next judgments according to the behavior of the ambient vehicles. Thus, vehicles need to consider temporal and spatial interactions to reduce the risk of future collisions. In the current work, a trajectory prediction method is put forward in accordance with a graph attention mechanism. We add the absolute and relative motion information of vehicles to the input of the model to describe the vehicles' past motion states more accurately. LSTM models are employed to process the historical motion information of vehicles, as well as the temporal correlations in interactions. The graph attention mechanism is applied to capture the spatial correlations between vehicles. Utilizing a decoder rooted in an LSTM framework, the future trajectory distribution is generated. Evaluation on the NGSIM US-101 and I-80 datasets substantiates the superiority of our approach over existing state-of-the-art algorithms. Moreover, the predictions of our model are analyzed. [ABSTRACT FROM AUTHOR]

Background: Hydraulically interconnected suspension (HIS) system has drawn wide attention in vibration control fields. The pressure difference problem due to long time use of HIS system will cause unsafe driving of the entire vehicle. Purpose: To solve the pressure difference problem and improve the anti-pitch and ride comfort performance of the HIS system, the displacement-sensitive grooves are added to the oil cylinder wall of the HIS system. The displacement-sensitive HIS (DSHIS) system has different damping characteristics depending on the position of the piston. Methods: The DSHIS system and a seven-DOF vehicle are modeled to lay a foundation for the simulation analysis. Then, the damping characteristics of the displacement-sensitive cylinder are studied. Next, the influence of the parameters on the DSHIS system's performance is studied. Finally, a full car simulation is performed to compare the performance of the proposed DSHIS system to the original suspension and the HIS system. Results: In the brake simulation, the pitch angles of vehicle 3 and vehicle 2 are reduced by 56% and 62.3% compared with that of vehicle 1, respectively. In the random road simulation, the acceleration RMS values of vehicle 3 and vehicle 2 are decreased by 48.17% and 26.73% than the original suspension, respectively. In the loading shock simulation, the maximum suspension deflection of vehicle 3 is decreased by 17.39% and 3.37% than vehicle 1 and vehicle 2, respectively. The maximum dynamic tire load of the vehicle 3 is decreased by 4.35% and 1.32% than vehicle 1 and vehicle 2, respectively. Conclusion: The full vehicle simulations prove that the proposed DSHIS system can effectively improve the ride comfort and anti-pitch performance of the mining vehicle. Besides, the pressure difference problem can also be solved by the additional grooves on the cylinder wall. [ABSTRACT FROM AUTHOR]

This paper proposed a novel direct yaw moment control (DYC) system to enhance vehicle stability and handling performance in various driving conditions and overcome the chattering problem of traditional sliding mode control. Accordingly, a DYC strategy is developed for eight-wheeled DDEVs by utilizing a super-twisting sliding mode (STSM) algorithm. Initially, a three-degrees-of-freedom model, nonlinear tire model, and motor model are established for vehicles. Subsequently, the reference yaw rate is obtained based on the reference model of the vehicle to serve as a control target. The DYC strategy is then established using the error between the actual yaw rate and the reference yaw rate as the input. Moreover, a traditional sliding mode (SM) controller is developed to enhance vehicle stability. A second-order SM controller is designed by incorporating a STSM control algorithm to address the chattering problem associated with traditional SM controllers. The algorithm adaptively adjusts the sliding surface and controls the gains based on the dynamic state of the vehicle. The effectiveness of the proposed control strategy is validated via hardware-in-the-loop simulations. [ABSTRACT FROM AUTHOR]

How to improve the driving performance of the vehicle while carrying out path tracking control has become a hot issue in current research. In this paper, an MPC (Model predictive control) path tracking control algorithm incorporating differential braking control is proposed. By establishing a vehicle dynamics model of a semi-trailer train, the model predictive control theory is adopted for path tracking. Then, the vehicle dynamics model, considering the additional yaw moment, is established to design the differential braking control strategy. Under low-speed working conditions, the PID (Proportional Integral Derivative) algorithm is used to solve the additional yaw moment with the yaw rate of the tractor traveling alone as the desired value. Under high-speed working conditions, the Fuzzy PID algorithm is used to solve the additional yaw moment with the control objective of reducing the articulation angle. Simulation models are built using MATLAB/Simulink, and TruckSim for numerical experimental validation. The numerical experimental results show that the differential braking control method proposed in this paper can improve the maneuverability of vehicles driving in low-speed conditions and the stability of vehicles driving in high-speed conditions without decreasing the precision of path tracking control. [ABSTRACT FROM AUTHOR]

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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]

Vehicle stability control strategies can improve driving safety effectively; however, there is still a lack of unified evaluation criteria for different control strategies. This paper proposes a vehicle control strategy evaluation method based on the driving stability region and is analyzed by using direct yaw moment control (DYC) and four-wheel steering (4WS) as examples. Firstly, the five-degree-of-freedom (5DOF) vehicle system models including DYC and 4WS are established, and the effectiveness of the control strategies is verified by nonlinear analysis methods; the dynamic characteristics of the system are also analyzed. Following this, a hybrid algorithm combining the Genetic Algorithm (GA) and Sequential Quadratic Programming (SQP) methods is used to solve the system equilibrium points, and the driving stability regions under different control strategies are obtained. Finally, the driving stability regions are tested based on the CarSim and Simulink simulations, and the control performance is evaluated. The results indicate that DYC and 4WS can improve vehicle stability and expand the range of driving stability regions. When the initial longitudinal velocity is below 30 m/s, the driving stability regions under DYC and 4WS expand to different extents compared to the original driving stability region. The expanded driving stability regions show that the stability region of the vehicle with DYC is larger than that of 4WS; thus, the control effect of DYC is better than that of 4WS. The proposed method can be used to evaluate the effective range of different control strategies. [ABSTRACT FROM AUTHOR]

Wheel odometry is a simple and low-cost localization technique that can be used for localization in GNSS-deprived environments; however, its measurement accuracy is affected by many factors, such as wheel slip, wear, and tire pressure changes, resulting in unpredictable and variable errors, which in turn affect positioning performance. To improve the localization performance of wheel odometry, this study developed a wheel odometry error prediction model based on a transformer neural network to learn the measurement uncertainty of wheel odometry and accurately predict the odometry error. Driving condition characteristics including features describing road types, road conditions, and vehicle driving operations were considered, and models both with and without driving condition characteristics were compared and analyzed. Tests were performed on a public dataset and an experimental vehicle. The experimental results demonstrate that the proposed model can predict the odometry error with higher accuracy, stability, and reliability than the LSTM and WhONet models under multiple challenging and longer GNSS outage driving conditions. At the same time, the transformer model's overall performance can be improved in longer GNSS outage driving conditions by considering the driving condition characteristics. Tests on the experimental vehicle demonstrate the model's generalization capability and the improved positioning performance of dead reckoning when using the proposed model. This study explored the possibility of applying a transformer model to wheel odometry and provides a new solution for using deep learning in localization. [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]

This paper presents a novel torque vectoring control (TVC) method for four in-wheel-motor independent-drive electric vehicles that considers both energy-saving and safety performance using deep reinforcement learning (RL). Firstly, the tire model is identified using the Fibonacci tree optimization algorithm, and a hierarchical torque vectoring control scheme is designed based on a nonlinear seven-degree-of-freedom vehicle model. This control structure comprises an active safety control layer and a torque allocation layer based on RL. The active safety control layer provides a torque reference for the torque allocation layer to allocate torque while considering both energy-saving and safety performance. Specifically, a new heuristic random ensembled double Q-learning RL algorithm is proposed to calculate the optimal torque allocation for all driving conditions. Finally, numerical experiments are conducted under different driving conditions to validate the effectiveness of the proposed TVC method. Through comparative studies, we emphasize that the novel TVC method outperforms many existing related control results in improving vehicle safety and energy savings, as well as reducing driver workload. [ABSTRACT FROM AUTHOR]

To explore the mixed traffic characteristics of vehicles and aircraft on the airport surface and solve the problem of real-time conflict detection at key intersections. According to the actual taxiing procedures and airport control rules in China, this paper focus on abstracting the mixed motion process of aircraft and vehicles in the maneuvering area, defining the convergent cross-safety operation scenario. To quantify the driver's attention to safety separation and the degree of conservatism in adjusting speed, the vehicle deceleration rate and acceleration rate are defined with α as the exponent. Under the same spacing, the vehicle deceleration rate is directly proportional to α, and α is named the vehicle safety sensitivity. At the same time, the rules of speed change of vehicles and aircraft will be designed, and a convergent operation model of aircraft and vehicles will be proposed. Based on real-time speed and separation dynamic assessment of safety and efficiency under different traffic strategies, the computer simulation results show that vehicle safety sensitivity and deceleration rules determine the sequence of vehicles and aircraft when they are passing through in the short term and can affect the proportion of mixed traffic flow in the long run. The safety probability of vehicles passing the intersection first is negatively correlated with vehicle safety sensitivity, while the safety probability of aircraft passing the intersection first is correlated positively with vehicle safety sensitivity. The efficiency of passing without conflict with mixed traffic has an inverse relationship with vehicle safety sensitivity. When the vehicle safety sensitivity takes the value in the interval [0.4, 0.6], a mixed traffic flow with higher safety and efficiency, better stability, and a balanced ratio of locomotives can be obtained. [ABSTRACT FROM AUTHOR]

The rapid development of the automobile industry has made life easier for people, but traffic accidents have increased in frequency in recent years, making vehicle safety particularly important. This paper proposes an improved YOLOv5s algorithm for vehicle identification and detection to reduce vehicle driving safety issues based on this problem. In order to solve the problems of a disappearing model training gradient in the YOLOv5s algorithm, difficulty in recognizing small objects and poor recognition accuracy caused by the boundary frame regression function, it is necessary to implement a new function. These aspects have been enhanced in this article. On the basis of the traditional YOLOv5s algorithm, the ELU activation function is used to replace the original activation function. The attention mechanism module is then added to the YOLOv5s algorithm's backbone network to improve the feature extraction of small and medium-sized objects. The CIoU Loss function replaces the original regression function of YOLOv5s, thereby enhancing the convergence rate and measurement precision of the loss function. In this paper, the constructed dataset is utilized to conduct pertinent experiments. The experimental results demonstrate that, compared to the previous algorithm, the mAP of the enhanced YOLOv5s is 3.1% higher, the convergence rate is 0.8% higher, and the loss is 2.5% lower. [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]

This study uses the wind–vehicle–bridge coupling vibration analysis method to investigate the bridge stiffness problem of a large-span cable-stayed-suspension cooperative system. On the basis of the particle-damping-spring vehicle model, the TMeasy surface contact tire model is introduced, and a set of universal wind–vehicle–bridge coupling analysis algorithm is built in the framework of the whole process iterative method. Based on the Latin supercube sampling principle, random traffic flow is generated and loaded onto bridge structures with different stiffness conditions to analyze the driving comfort and safety under each stiffness condition. Combining the specification requirements, engineering experience, and research results, the vertical stiffness limit applicable to the bridge of the highway cable-stayed-suspension collaborative system is proposed. Existing engineering experience shows that the vertical deflection-to-span ratio of a cable-stayed bridge under live load is distributed between 1/400 and 1/1600, and the vertical deflection span ratio under the action of lane load is recommended based on numerical analysis. [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]

Carbon emission changes significantly when the vehicle is driving at a variable speed. Given that the rule of vehicle carbon emissions under different driving behaviors is underexplored, this study filled this research gap by establishing a theoretical carbon emission model when vehicles are driving at fluctuating speed. The model was established based on the law of conservation of mechanical energy, the first law of thermodynamics, the theory of vehicle dynamics. Two passenger cars and three trucks were selected as typical vehicles. A field test was carried out under different driving behaviors with uniform acceleration. The proposed model can forecast the carbon emissions of vehicles that fluctuate in speed during travel and showed strong accuracy in model validation. As the speed fluctuation increased, the influence of acceleration on carbon emissions became more apparent. Gradient has a more significant influence on vehicle carbon emissions, followed by acceleration. By controlling the driving behavior of the vehicle on the round trip, the same carbon emissions as driving on a flat route at a constant speed can be achieved. The findings of this study will provide a theoretical basis for low-carbon transportation. [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]

• 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]

Aiming at the problem of body instability caused by actuator failure in a distributed electric vehicle drive system, a fault-tolerant control strategy of longitudinal and lateral force cooperative reconstruction with active steering control was proposed, and a layered control structure was adopted based on the vehicle model. In the upper controller, the resultant force and torque are calculated according to the vehicle parameter state and MPC algorithm; the lower controller is the cooperative reconfiguration allocation layer, and the minimum tire load rate, longitudinal and lateral force constraints and front wheel angle control are considered. Finally, offline simulation experiments and hardware-in-the-loop experiments are completed to verify the effectiveness and real-time performance of the designed strategy. The results show that the designed strategy can significantly improve the driving stability and safety of the vehicle when the actuator fails. [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]

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

In order to solve the problems of low path-tracking accuracy, poor safety, and stability of intelligent vehicles with variable speeds and obstacles on the road, a double-layer adaptive model predictive controller (MPC) is designed. A vehicle point mass model is used in an obstacle avoidance planning controller, and the safety collision distance model is established according to the distance relationship between the vehicle and the obstacle to improve the driving safety of the vehicle. The design of the path-tracking controller is based on the three-degrees-of-freedom dynamics model. According to the relationship between the predictive horizon and vehicle speed in the MPC algorithm, an adaptive path-tracking control strategy which can update the prediction horizon in real time is proposed to improve the accuracy of vehicle path tracking. To increase the vehicle stability, a sideslip angle and an acceleration control variable are added to the vehicle dynamics model as soft constraint conditions. The proposed method is simulated based on a CarSim and MATLAB/Simulink co-simulation platform. The simulation results show that the maximum lateral path deviation and the maximum centroid sideslip angle of the designed controller are 0.13 m and 0.4 ∘ , respectively. Compared with the traditional MPC, the adaptive MPC maximum lateral path deviation and the maximum centroid sideslip angle are reduced by 0.51 m and 1.57 ∘ , respectively, which proves the effectiveness of the proposed method. [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]

There will be a complex driving environment formed by manned and unmanned vehicles with highly uncertain and dynamic interaction when autonomous vehicles enter actual traffic flow. Autonomous vehicles need to detect and analyze the movement of surrounding vehicles to make safe driving decisions. This paper proposes a Clustering Convolution-LSTM (CC-LSTM) vehicle trajectory prediction model. Using Fuzzy Clustering method, similar trajectories of surrounding vehicles are clustered to mine the temporal features of vehicles trajectory. The result is carried out to classify the features of historical trajectory by Density Clustering and find the similarity during segments which are used as the spatial features of target vehicles trajectory. The screened spatio-temporal features are fused by Las Vegas Wrapper (LVW) algorithm to obtain new input data of Convolution-LSTM network to make prediction. This model is trained by different features of data. Simulation shows that the CC-LSTM prediction model with surrounding vehicle interaction information and selected features can meet the real-time and accuracy requirements of prediction. [ABSTRACT FROM AUTHOR]

This paper proposes an autonomous vehicle trajectory tracking system that fully considers road friction. When an intelligent vehicle drives at high speed on roads with different friction coefficients, the difficulty of its trajectory tracking control lies in the fast and accurate identification of road friction coefficients. Therefore, an improved strategy is designed based on traditional recursive least squares (RLS), which is utilized for accurate identification of the friction coefficient. First, the tire force and slip rate required for the estimation of the road friction coefficient by constructing the vehicle dynamics model and tire effective model are calculated. In this paper, a variable forgetting factor recursive least squares (VFF-RLS) method is proposed for the construction of the friction coefficient estimator. Second, the identified results are output to the model predictive controller (MPC) constructed in this paper as a way to improve tire slip angle constraints, to realize the trajectory tracking of the intelligent vehicle. Finally, the joint simulation test results of Carsim and Matlab/Simulink show that the trajectory tracking system based on the VFF-RLS friction coefficient estimator has outstanding tracking performance. [ABSTRACT FROM AUTHOR]

Automotive biofuels offer a promising alternative to traditional fossil fuels. Accurate evaluation of combustion and emissions in IC engines and vehicles is crucial. This research aimed at developing and validating an engine and vehicle simulation methodology to assess the fuel effect on vehicle consumption and emissions considering different driving cycles and the road slope (barely evaluated for fuels widely used in emerging markets). Two blends were tested: 20 % biodiesel (B20) and 20 % hydrotreated vegetable oil (HVO20) with Ultra-Low-Sulfur Diesel (ULSD). A light-duty diesel vehicle model was developed in GTSuite®, using emission maps from a calibrated steady-state engine model. Good agreement with experiments was found. Road slope in local DC significantly increased fuel consumption and CO, CO 2 , NOx, and PN emissions, reducing HC. Compared to ULSD, B20 reduced PN and HC by 27–35 % and 12–22.5 %, respectively. HVO20 had a smaller effect on PN but reduced HC emissions by up to 19.5 %. Neither blend significantly affected CO and CO 2. B20 slightly increased NOx and fuel consumption, while HVO20 had no significant impact on these. [Display omitted] • Full maps of fuel consumption and emissions were developed through an engine model. • Emission factors were obtained with a vehicle model using a map-based engine approach. • PN emissions decreased up to 35 % with B20 and up to 5.3 % with HVO20, compared to ULSD. • HC decreased up to 22.5 % with B20 and up to 19.5 % with HVO20, compared to ULSD. • Fuel consumption, CO 2 and CO emissions barely changed with the biofuels, but the fossil-fuel carbon-emissions may be reduced. • NOx emissions increased with B20, especially in Andean cycle 2 + S, while no significant change was obtained with HVO20. • Inclusion of slope in the driving cycles had the most significant influence on emissions. [ABSTRACT FROM AUTHOR]

Based on the predigestion of the dynamic model of the intelligent firefighting vehicle, a linear 2‐DOF lateral dynamic model and a preview error model are established. To solve the problems of a highly non‐linear vehicle model, time‐varying parameters, output chattering, and poor robustness, the Radial Basis Function neural network sliding mode controller is designed. Then, different driving speeds are used to conduct simulation tests under standard double‐shifting and smooth curve road conditions, and the simulation results are used to analyse the tracking effect of the lateral motion controller on the desired path. The simulation results reveal that the controller designed has high accuracy in tracking the desired path and has good robustness to the disturbance of intelligent firefighting vehicle speed changes. [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]

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]

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]

To advance the active safety performance for vehicles, especially in extreme conditions, an active rear steering (ARS) control system is designed in this paper. A driver model is established to simulate the driving behaviour of a human driver who is in charge of the front steering control. In the ARS control system, the sliding mode predictive control (SMPC) approach is applied to the ARS controller design based on a 3 degrees of freedom (DoF) nonlinear vehicle model. In the ARS controller design, four kinds of active safety performances are considered, namely, path-tracking performance, handling performance, lateral stability, and rollover prevention. Furthermore, the priority of the four kinds of active safety performance is defined. According to the control priority, an event-triggered mechanism (ETM) is designed to adjust the SMPC controller of the ARS system to address different driving conditions. Finally, two simulation cases are conducted to evaluate the performance of the proposed ARS system. The results show that the ARS system is in favour of the active safety performance advancement for human drivers. Additionally, the comparative simulation indicates that the SMPC algorithm is superior to the fast terminal sliding mode control (FTSMC) algorithm. [ABSTRACT FROM AUTHOR]

In order to further improve the stability of path tracking control and fuel economy of new energy vehicles, an optimal control strategy of path tracking and braking energy recovery is proposed. First, a model predictive controller is designed based on the three-degrees of freedom dynamics model of the vehicle according to the idea of hierarchical control, and a fuzzy yaw torque controller is established with the desired yaw velocity and side slip angle of the mass center as constraints. Second, at high-speed driving conditions, the executive layer of the component distributes the braking torque according to the braking energy recovery control strategy. Finally, the optimal control strategy of path tracking and braking energy recovery is verified by Carsim/Advisor/Simulink software under different driving speeds. The results show that the optimized control strategy can improve the tracking accuracy and driving stability of a vehicle with large curvature turning and further improve the fuel economy of new energy vehicles under the premise of meeting the control requirements. [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]