Search

In the switching process of driving authority for man-machine cooperative driving, it should be ensured that the driver can take over the vehicle safely, stably and efficiently. This paper proposes a driving authority allocation strategy based on real-time allocation domain (RAD), which mainly includes the establishment of RAD and the dynamic optimization. The RAD is a comprehensive dynamic allocation internal, whose range is determined by the corresponding allocation values of driver's cognitive state, driver's muscle state and environment state. The dynamic optimization is to search for the optimal driving authority in RAD at any time, and its optimization objectives and constraints all contain time-varying parameters. This work proposes an improved simulated annealing algorithm to solve this problem. Simulation results show that the proposed driving authority allocation strategy can effectively allocate the driving authority according to the current condition and driver's state, and ensure the safety, stability and efficiency of the take-over process.

On-ramp merging scenario has a great impact on traffic efficiency and fuel economy. At present, most research on-ramp merging focuses on the optimization of merging sequence in the single main lane scenario, which fails to give full play to the capacity of multi-lane roads. To overcome this problem, an efficient on-ramp merging strategy (ORMS) is proposed to coordinate vehicle merging in multi-lane traffic. First, we built a model of the unevenness of traffic flow between lanes. Based on this model, we established a lane selection model by reinforcement learning for the coordination of vehicles in multi-lane traffic. Before vehicles enter the merging zone, the decision of lane selection is made by analyzing the unevenness of traffic flow between lanes to relieve local congestion in the outside lane that may be caused by ramp vehicle inflow. Then, we adopted a vehicle motion planning algorithm based on the time-energy optimal control, so that all vehicles travel according to the optimal trajectory to reach the merging zone. The simulation results show that the traffic efficiency and fuel economy of the proposed on-ramp merging strategy are significantly improved compared with the existing optimal control algorithm.

The existing vehicle obstacle avoidance path planning methods generally aim at obtaining the collision-free path, ignoring the impact of the planned path on the vehicle stability in the obstacle avoidance process, so that the controlled vehicle has the risk of rollover in the obstacle avoidance process. To solve the above problems, a two-layer obstacle avoidance path planning algorithm considering path pre-planning and re-planning is proposed in this paper. In the path pre-planning layer, an improved APF algorithm with road boundary function constraints is proposed. By introducing the repulsion field adjustment factor, the shortcomings of GNRON and local optimization existing in the existing artificial potential field method are effectively solved. In the path re-planning layer, taking the rollover stability index as the constraint, a pre-planning result optimization method based on particle swarm optimization algorithm is proposed. The simulation results show that the obstacle avoidance path planning algorithm proposed in this paper can not only generate the obstacle avoidance path in real-time, but also reduce the yaw rate and yaw angle of the main vehicle in the process of obstacle avoidance, and effectively improve the rollover stability of the vehicle in the process of obstacle avoidance.

This work proposes an active collision avoidance between autonomous driving vehicle and pedestrian with motion uncertainty under urban road. A candidate trajectory planning method considering spatial and time sequences is proposed, which combines the polynomial path planning and the velocity planning with variable safety velocity. Then, a pedestrian-vehicle interaction model is constructed, which takes the pedestrian's uncertain motion as a superposition of the Markov process without interference and the motion caused by the vehicle, and predicts the pedestrian's motion probabilistically. On these bases, the optimal trajectory is evaluated from the candidate trajectories by safety, stability, and efficiency, as well as different driving styles. The proposed collision avoidance strategy is verified in conventional and emergency simulation scenarios. Simulation results show that it can effectively plan a safe, stable and efficient trajectory under normal and emergency conditions.

The electro-hydraulic compound steering combines the function of electric power steering and hydraulic power steering, which can reduce the energy consumption of steering, ensure the stability of vehicles, and meet the steering demand under unmanned driving. In order to fully coordinate the steering stability and path tracking requirements in the unmanned steering process, this paper proposes a hierarchical control strategy of displacement characteristics for commercial vehicle electro-hydraulic compound steering. The mixed H2/H∞ controller for the vehicle stability is designed in the upper layer, which takes the vehicle yaw rate as the control variable. Based on the motor model and sliding-mode control theory, the sliding-mode variable structure controller of motor angle is designed in the lower layer, and the hydraulic power distribution is completed by estimating the steering resistance torque. Simulation results show that the designed controller can effectively track the yaw rate on the premise of ensuring path tracking effect. Combined with steering torque estimation and hydraulic power distribution rules, it can effectively realize the coordinated control of hydraulic power and motor rotation angle.

In order to solve the accuracy problem of the future motion prediction of the surrounding vehicles with different types of drivers, this paper proposes a comprehensive lateral motion prediction method that combines driver intention prediction and vehicle behavior recognition. For different drivers, different driver optimization models are established: personal optimal model and system optimal model. Then, the driver’s intention prediction probability is obtained through the game theory. Different from the traditional game theory, we apply different model for different driver on the premise of getting the type of driver instead of using the Nash equilibrium for all players. According to the results of driver intention prediction and the vehicle behavior recognition, the optimized polynomial trajectory is used to obtain the driver’s intention prediction trajectory and the vehicle behavior recognition trajectory. Next, the Nash-optimization function of the intention prediction trajectory and the behavior recognition trajectory are established respectively according to the trajectory error, and the balance between the two is our comprehensive trajectory. In order to verify the effectiveness of the proposed method, we conducted simulations under mandatory lane changing and discretionary lane changing conditions. The simulation results show that our algorithm can accurately predict the future motion of the vehicle under different drivers, different speeds and different gaps between vehicles. Its prediction performance is much better than other algorithms.

State of energy (SOE) is a critical index of lithium battery. The problem of the inaccurate available energy and recovered energy of lithium battery affects the accuracy of SOE estimation. In order to solve the problem, this paper proposes a method to estimate the available discharge energy of lithium batteries based on response surface model. In this method, the energy efficiency of lithium batteries in different states is obtained by establishing the relationship model of external charge voltage and external discharge voltage, so as to estimate the actual available energy of lithium batteries in different charge states. On this basis, a correction method based on radial basis function (RBF) neural network is proposed to estimate the actual energy released by the recovered energy when the current direction of the battery is changed. The proposed energy correction method is combined with the adaptive particle filter algorithm to estimate SOE. This method is not limited to the assumption of Gaussian function and can accurately predict the noise variance, so as to improve the estimation accuracy of SOE. Simulations under urban dynamometer driving schedule (UDDS) are conducted, and the result shows that the proposed method can effectively estimate the battery energy and improve the accuracy of SOE estimation.

In order to explore the nature of the human-vehicle game problem and analyze the torque-angle interaction between the two agents, i.e. the driver and advanced driver assistance system (ADAS), a human-vehicle game stability control strategy based on Nash negotiation principle is proposed. First a six-order vehicle dynamic model, a driver neuromuscular (NMS) model, etc., are set up to simulate drivers’ steering characteristics and vehicles’ response to the input of the two agents and external disturbance. Secondly, several significant parameters in NMS model are recognized, and an active rear steering (ARS) controller is designed using the sliding mode variable structure algorithm. Then, the Nash negotiation solution is worked out according to Nash negotiation principle, and a self-tuning method for the weight of ARS controller is put forward employing the fuzzy control theory. Finally simulations are carried out under the standard double-lane change maneuver. The results indicate that the stability control strategy proposed in this paper can effectively solve the game problem and achieve good vehicular stability control performance.

To improve the agility and efficiency of the highway decision-making system and overcome the local optimal dilemma of the existing safety field, this paper builds an improved safety field to reflect the advantage of the reachable states and the learning process is further employed to make the decision long-term optimal. Firstly, the improved safety field is prepared by the kinematic model-based prediction of surrounding vehicles and the boundary is determined elaborately to ensure real-time performance. Then, the field is constructed by three individual fields. One is the kinematic field, which is built based the safe-distance model to measure the colliding risk of both moving or no-moving objects accurately. Another is the road field that reflects the lane-marker constraint. The last is the efficiency field, which is introduced creatively to improve efficiency. Furthermore, the learning algorithm is adopted to learn the long-term optimal state-action sequence in the safety field. Finally, the simulations are conducted in Prescan platform to validate the feasibility of the improved safety field in complex scenarios. The results show that the proposed decision algorithm can always drive autonomous vehicle to the state with a long-term optimal payoff and can improve the overall performance compared to the existing pure safety field and the interaction-aware method.

In order to make safe and reasonable decisions in some high-risk environments such as the mandatory lane change, we propose an IMM-based partially observable Markov decision process (POMDP) decision algorithm using the collision-risk function which combines the time-to-collision (TTC), the intervehicular time (IT), and the collision function for mandatory lane change. The newly proposed collision-risk function contains two parts: the vehicle impact factor and the collision function, which is used to assess the risk and determines whether the autonomous vehicle collides with surrounding vehicles. The IMM-base POMDP is used for decision-making and we apply the Monte Carlo Tree Search (MCTS) to solve the problem. In the decision-making process, the belief state is obtained by the Interacting Multiple Model (IMM) algorithm. With the collision-risk function and the probability distribution of the states of surrounding vehicles in the future, the proposed POMDP decision algorithm can determine whether the autonomous vehicle accelerates lane changing or decelerates lane changing, and obtain the acceleration corresponding to each path point. Finally, in order to verify the effectiveness of the algorithm, we perform a driver-in-the-loop simulation through Prescan. We use aggressive driver and conservative driver to control the rear vehicle of the target lane, respectively. Simulation results show that the proposed algorithm can accurately predict the accelerations of surrounding vehicles and make safe and reasonable decisions under two scenarios, which is superior to the general POMDP.

In order to improve the accuracy of fault detection results, this paper proposes a novel fault detection strategy of vehicle wheel angle signal via long short-term memory network (LSTM) and improved sequential probability ratio test (SPRT). Firstly, a signal estimation method based on data-driven modeling is presented, which fuses the vehicle current status information and adopts the LSTM based on deep learning to estimate the vehicle wheel angle signal. Then, the signal residual sequence is obtained by comparing the estimated wheel angle signal with the measured wheel angle signal. Based on this, the improved SPRT method based on mathematical statistics is used to analyze the signal residual sequence, so as to detect the fault signal timely and accurately. Finally, the accuracy of the estimation results is analyzed under sinusoidal condition, double-lane change condition and sinusoidal sweep frequency condition, and the effectiveness of the fault detection strategy proposed in this paper is further verified under the stuck fault condition and drift fault condition. The results indicate the effectiveness of the proposed fault detection strategy, which is of great significance to improve the safety and reliability of the vehicle.

To improve handling and stability for distributed drive electric vehicles (DDEV), the study on four wheel steering (4WS) systems can improve the vehicle driving performance through enhancing the tracking capability to desired vehicle state. Most previous controllers are either a large amount of calculation, or requires a lot of experimental data, these are relatively time-consuming and laborious. According to the front and rear wheel steering angle of DDEV can be distributed independently, a novel controller named internal model controller with fractional-order filter (IMC-FOF) for 4WS systems is proposed and studied in this paper. The IMC-FOF is designed using the internal model control theory and compared with IMC and PID controller. The influence of time constant and fractional-order parameters which is optimized using quantum genetic algorithms (QGA) on tracking ability of vehicle state are also analyzed. Using a production vehicle as an example, the simulation is performed combining Matlab/Simulink and CarSim. The comparison results indicated that the proposed controller presents performance to distribute the front and rear wheel steering angle for ensuring better tracking capability to desired vehicle state, meanwhile it possesses strong robustness.

Vehicle lateral velocity is critical for the high-level automatic driving technology, andyyfusing Global Position System (GPS) in the lateral velocity estimation method can greatly improve the estimation accuracy. However, the method fusing GPS is seldom reported, since the problem of GPS-outage often appears. Accordingly, this article proposes a novel lateral velocity estimation method (VLVEM) based on SBI-LSTM in GPS-outage environment. Additionally, VLVEM integrating Inertial Navigation System-aided GPS (INS-aided GPS) is derived from Federated Kalman Filter (FKF) algorithm. Furthermore, during GPS-outage, induced from the Stack Bidirectional Long Short-Term Memory Recurrent Neural Network (SBI-LSTM RNN), an INS-aided GPS fault reconstructor (IGFR) is designed to reconstruct INS-aided GPS model. Finally, the simulation results show that compared with most of the existing methods which only consider the in-vehicle sensors’ signals, the proposed method fusing GPS has higher lateral velocity estimation accuracy. Besides, when GPS-outage causes INS-aided GPS failure, IGFR can reconstruct INS-aided GPS model, and VLVEM still has high estimation accuracy. Combining the in-vehicle sensors and GPS, VLVEM exhibits great robustness and fault tolerance.

In order to solve the problems of longitudinal and lateral control coupling, low accuracy and poor real-time of existing control strategy in the process of active collision avoidance, a longitudinal and lateral collision avoidance control strategy of intelligent vehicle based on model predictive control is proposed in this paper. Firstly, the vehicle nonlinear coupling dynamics model is established. Secondly, considering the accuracy and real-time requirements of intelligent vehicle motion control in pedestrian crossing scene, and combining the advantages of centralized control and decentralized control, an integrated unidirectional decoupling compensation motion control strategy is proposed. The proposed strategy uses two pairs of unidirectional decoupling compensation controllers to realize the mutual integration and decoupling in both longitudinal and lateral directions. Compared with centralized control, it simplifies the design of controller, retains the advantages of centralized control, and improves the real-time performance of control. Compared with the decentralized control, it considers the influence of longitudinal and lateral control, retains the advantages of decentralized control, and improves the control accuracy. Finally, the proposed control strategy is simulated and analyzed in six working conditions, and compared with the existing control strategy. The results show that the proposed control strategy is obviously better than the existing control strategy in terms of control accuracy and real-time performance, and can effectively improve vehicle safety and stability.

Recently, inferring lane change intention has received considerable attention. Due to the high nonlinearity and complexity of traffic contexts, traditional methods cannot satisfy the requirements of long-term prediction tasks and lack the ability of capturing nonlinear temporal dependencies. This paper proposes an intention inference model based on Recurrent Neural Networks (RNN), to tackle time series prediction problems. Considering dynamic interaction among surrounding vehicles, our model takes the sequence motion information of surrounding vehicles as inputs and calculates the congestion of different lanes, integrated with vehicle states of the object vehicle. To illustrate the availability of the proposed RNN intention inference model, a motion planning controller considering intention was developed. A Nonlinear Model Predictive Control (NMPC) was established to planning a safe, sub-optimal path for autonomous driving vehicle under collision avoidance constraints. The experiments on the proposed model were conducted, based on two RNN structure Long-Short Term Memory (LSTM) and Generalized Recurrent Unit (GRU), by Tensorflow with NGSIM data. The motion planning controller is modeled and simulated by Carsim with Simulink for some typical scenarios. Subsequently, experimental results demonstrate that RNN achieves best performance, inferring intention with 96% accuracy, compared with other approaches.

The prediction and estimation of the lane-changing state of the host car and surrounding cars are important parts of an advanced driving assistant system, which mainly depend on the understanding of the driver lane-changing behavior. To learn driver lane-changing maneuver well, this article provides a novel stochastic driver lane-changing model based on an improved input–output hidden Markov model (IOHMM) framework. First, an improved IOHMM is proposed to address the deficiency that the traditional IOHMM cannot remember previous data and describe continuous output. Then, based on the improved IOHMM framework, a driver lane-changing model is established considering the intention and behavior of the driver in the lane-changing process. The model parameters can be learned from the collected lane-changing data using the maximum likelihood estimation and generalized estimation-maximization methods. Finally, the model is applied to a real driver lane-changing process. It is verified that the proposed model has good performance in predicting the future motion maneuver of the host vehicle and estimating the current motion state of the surrounding cars.

This article proposes an individual auxiliary and fault-tolerant control (IAFTC) for the electric vehicles with the steer-by-wire system considering different drivers steering characteristics. It is composed of an individual auxiliary controller, a fault detection and diagnosis controller (FDDC), and an individual fault-tolerant controller. The individual auxiliary controller is used to specifically assist the drivers’ steering behaviors and maintain their steering style when the steering motors are healthy. The FDDC detects and estimates the state and parameter of actuators in real time as well as feedbacks the condition or the extent of partial damage concerning motors to ECU. After the fault-tolerant command is transmitted from ECU, an individual fault-tolerant controller will be turned on to deal with the influence of faulty motor on different drivers. The IAFTC strategy can specifically assist the drivers to track the reference path, reducing the physical and mental workloads of drivers in the no-fault or fault vehicle steering process. The results of simulation using the Matlab and hardware-in-the-loop tests indicate that the controller can provide appropriate assistance control to different drivers in a human–vehicle cooperative method so as to deal with complex actuator's condition.

In order to improve the agility and applicability of trajectory planning algorithm for autonomous vehicles, this paper proposes a novel actor-critic based learning method for decision-making and planning in multi-vehicle complex traffic. It is the coupling planning of vehicle’s path and speed thus to make the trajectory more flexible. First, generations from the decided action to the planned trajectory are described by the end-point of the trajectory. Then, the actor-critic based learning method is built to learn an optimal policy for the decision process. It can update the policy by the gradient of the current policy’s advantage. In this process, features of the real traffic are carefully extracted by time headway (TH) and speed distribution. Reward function is built by the safety, efficiency and driving comfort. Furthermore, to make the policy network have better convergency, the policy network is modularized in two parts: the lane-changing network and the lane-keeping network, which decide the optimal end-point of the path and speed candidates respectively. Finally, the curved overtaking scenario and the interaction process with human driver are conducted to illustrate the feasibility and superiority. The results show that the proposed method has better real-time performance and can make the planned coupling trajectory more continuous and smoother than the existing rule-based method.

Door system plays a very important role in the domain of automobile passive safety. In order to improve its side crashworthiness, some affiliated impact components are assembled. Conventional impact parts can make the door system possess enough stiffness and strength so as to ensure the integrity of car body when side collision occurs. But for occupant protection, the excessive rigidity may increase the risk of occupant injury. To address this problem, this work first introduces a kind of negative Poisson’s ratio (NPR) structure, and proposes a novel door system which is composed of NPR energy-absorbing block, NPR impact beam, inner panel, outer panel, and reinforcing plate. Next, parameter sensitivity analysis for each performance index is conducted to determine the corresponding variables when constructing the approximate model. Then, aiming at the disadvantages of the Technology for Order Preference by Similarity to Ideal Solution (TOPSIS) and the Mean Square Deviation Method (MSDM) in processing performance index data, a novel integrated weighting method is used to determine the weighting coefficient of each performance index. Finally, considering side structural crashworthiness, occupant protection, and lightweight, the hierarchical optimization for the novel door system is conducted to further enhance its overall performance. The result demonstrates that compared with the conventional door, the optimal door can improve the performance of occupant protection and ensure the side crashworthiness more effectively.

In order to improve the real-time and computational efficiency of autonomous vehicles’ decision-making process, this paper draws on the decision-making behavior of human drivers with the motivation as the core and proposes a decision-making and planning method based on motivation and risk assessment. On the one hand, it analyzes and determines the motivations that cause the driving state to change for decision-making and planning. On the other hand, on the basis of the lateral trajectory prediction of surrounding vehicles, the longitudinal trajectory propensity prediction of different drivers is added to construct a risk assessment model that can reflect risk of the future time domain. Based on this, the motivation-based decision method is mapped into the risk assessment model, and a cost function is established to decouple the path and speed, so that the geometry and speed can be flexibly adjusted according to environmental risks. The simulation results show that the proposed method can effectively make driving behavior decisions and plan the trajectory in real time according to the current environment, which can improve the computational efficiency of the decision-making process and guarantee the safety at the same time.

The battery pack placed on the chassis of an electric vehicle is easily to be damaged in a side collision because of its large volume. Therefore, the power battery pack should comprise a protective structure that can cover the entire side. Because the traditional hollow structure has limited performance, this paper proposes a large-size tubular negative Poisson's ratio (LTNPR) protection structure. A 2D LTNPR structure that can protect the side of battery pack entirely is designed and its mechanical properties are calculated. Then, the FE model of the battery pack equipped with LTNPR structure is analyzed under side pole impact by CAE simulation, which verifies the superiority of the LTNPR structure over the traditional hollow structure. Finally, deterministic optimization and reliability optimization are applied. The study and results demonstrate that compared with traditional structure, the LTNPR structure can improve the crashworthiness of the power battery pack significantly. Furthermore, the specific energy absorption (SEA) of LTNPR structure is increased by 28.81% and the maximum acceleration of battery pack is reduced by 15.29% through deterministic optimization, while the σ level is increased from 2.8448 to 8 through reliability optimization. The passive safety of electric vehicle is improved.

Due to the absence of vacuum sources for electric vehicle, the vacuum booster is replaced by electronic brake boosting (EBB) system. In order to improve the driver’s brake feeling based on EBB, this work fully exploits the flexible and variable power-assisted characteristics, and designs different transmission ratios according to the driver’s target braking strength. To identify the driver’s braking strength, an improved radial basis function (IRBF) neural network, combining self-organization method and supervised learning method, is proposed to establish the relationship between the driver’s braking strength and the characteristic parameters. Based on this, the variable transmission ratio strategy is designed, and its main optimized parameters are optimized by means of multi-objective optimization algorithm to provide the driver with a satisfactory brake feeling. The strategies under fixed and variable transmission ratios are simulated and analyzed in low-speed with gentle-brake and high-speed with emergency-brake. The simulation results show that, compared with the fixed transmission ratio, the proposed variable transmission ratio shows excellent performances in both brake feeling and brake safety.

In order to reduce steering energy consumption and improve steering feeling of heavy commercial vehicle, a novel electro-hydraulic compound steering system is proposed, which combines the function of electro-hydraulic power steering and electric power steering. The electro-hydraulic compound steering system dynamic model is established and the coordinated control strategy of dual actuators is proposed by analyzing the structure and dynamic characteristics of electro-hydraulic compound steering system. The genetic algorithm is used to optimize the steering assist torque distribution, and an Elman neural network predictive controller with fuzzy compensation is designed to solve the nonlinear problem of the electro-hydraulic compound system. Finally, the simulation is carried out by using Amesim/Simulink. The simulation results show that the coordinated control strategy designed in this paper enables the electro-hydraulic compound steering system significantly better than the traditional electro-hydraulic power steering system in steering feeling and energy consumption.

Purpose This study aims to contrastively investigate the effects of biodiesel and diesel on the power, economy and combustion characteristics of a compression ignition aviation piston engine for unmanned aerial vehicles. Design/methodology/approach Biodiesel used as alternative fuel will not be mixed with diesel during experimental study. Pure diesel fuel is used for the comparative test. Same fuel injection strategies, including pilot and main injection, are guaranteed for two fuels in same test points. Findings The engine-rated power of biodiesel is lower than diesel, which results in higher specific fuel combustion (SFC) and effective thermal efficiency (ETE). Biodiesel has the faster burning rate, shorter combustion duration. The crank angle of 50% mass fraction burned (CA50) is earlier than diesel. The ignition delay angle of biodiesel and diesel in the pilot injection stage is almost the same at high engine speed. As the speed and load decrease, the ignition delay angle of biodiesel in the pilot injection stage is smaller than diesel. At 100% high load conditions, the fuel-burning fraction of biodiesel in the pilot injection is the same as diesel. The peak heat release rate (HRR) of biodiesel is slightly lower than diesel. At 20% part load conditions, the fuel-burning fraction of biodiesel in the pilot injection stage is lower than diesel. Because of the combustion participation of unburned pilot injected fuel, the peak HRR of biodiesel in the main injection is equal to or even higher than diesel. Originality/value The application feasibility of alternative fuel and its effects on aviation engine power, economy and combustion characteristics will be evaluated according to the “drop-in“ requirements and on the low-cost premise without changing the aviation engine structure and parameters.

Random network delay will introduce uncertainty into trajectory tracking model of the autonomous vehicle, which seriously deteriorates the vehicle's control system stability and trajectory tracking accuracy. In this paper, considering steering angle oscillation caused by random network delay, trajectory tracking system robustness and stability is analyzed and a linear uncertain time-delay system is established. Comprehensively considering control system accuracy, robustness, and computational efficiency in the rolling optimization of Model Predictive Control (MPC), Adaptive Model Predictive Control for Uncertain model (UM-AMPC) algorithm is proposed to predict control variables for the next sampling time and alleviate the target angle discontinuity. This is achieved by operating target angle signal and augmented state variables, which are received by the lower nodes during the period from the current sampling time to network delay upper bound. The hardware-in-the-loop simulation results show that the proposed algorithm can effectively guarantee system stability and tracking accuracy of the autonomous vehicle under random network delay.

To improve vehicle side crashworthiness, this paper first introduces the negative Poisson’s ratio structure to the traditional B-pillar and proposes a negative Poisson’s ratio B-pillar. Then, the performance of the negative Poisson’s ratio B-pillar is studied in detail by comparison with a traditional B-pillar and honeycomb B-pillar. Aiming at the problem that the side crashworthiness is also significantly affected by the side structure parameters of vehicle body, the factorial analysis theory is adopted to screen out the side structure parameters with significant effect. Based on this, by combining the optimal Latin hypercube design and response surface model, a multi-objective optimization design is conducted for those structure parameters based on non-dominated sorting genetic algorithm II. Finally, the normal boundary intersection method is adopted to seek the Pareto optimal solution, and the simulation results show that compared with the traditional B-pillar, the negative Poisson’s ratio B-pillar optimized by non-dominated sorting genetic algorithm II has better comprehensive crashworthiness. The results of this paper can provide some basis for the design and optimization of vehicle side crashworthiness.

In order to decide a safe and reliable trajectory for autonomous driving vehicles, the threat of surrounding vehicles need to be assessed quantitatively and consider the potential risk. This paper proposes a novel integrated threat assessment algorithm for the decision-making system. First, the motion of the surrounding vehicle is predicted probabilistic based on the interact multiple model (IMM) to consider the potential threat. Then, we build an integrated threat assessment function to assess the threat in each state quantitatively and objectively, which synthesizes the existing time-to-collision (TTC), time-headway (TH), and the original proposed time-to-front (TTF). Based on this, the decision-making system is established according to the Markov decision process (MDP) and the feedback value of each decision sequence is calculated by the integrated threat assessment function, thus the safest trajectory for the current moment can be determined by optimal search. Finally, the decision-making system is verified in the overtaking and cut-in scenario by Carsim and Simulink co-simulation. The results show that the proposed threat assessment algorithm for the decision-making system can help autonomous vehicles decide a safe trajectory in real-time and maintain good maneuverability.

In extreme working conditions such as poor road conditions, the low road adhesion coefficient is easy to cause the tire cornering characteristics curve to be in the nonlinear domain, making the vehicle system in the critical instability state. To this end, this paper proposes a cascade deep learning framework combining multi-model predictive control (MMPC) and LSTM tire cornering stiffness estimation (TCSE) neural network and designs a stability control strategy of vehicle four-wheel steering system considering tire nonlinear cornering characteristics. The four-wheel steering system and vehicle tire dynamic model are analyzed and established, and the online estimation method of tire cornering stiffness and MMPC's sub-model classification method is developed. On this basis, the tire angle is creatively introduced as the phase plane stability region boundary, used to design the MMPC controller's boundary condition. The hardware in the loop test results shows that compared with the existing research, the strategy proposed in this paper can effectively improve the tracking accuracy of the target steering signal and ensure the system's stability when the road adhesion coefficient is low.

Reliable and precise temperature prediction is one of the most crucial challenges for improving battery performance and preventing thermal runaway. This paper uses a highly adaptive Elman neural network (Elman-NN) to construct a temperature prediction model for lithium-ion batteries in a metal foam aluminum thermal management system. Numerical modeling methods obtain experimental data sets for model training and testing. The input parameters of the neural network prediction model are ambient temperature, battery discharge rate, cooling air flow rate, and state of charge; the output parameters are the maximum, minimum, and average battery temperature. However, due to the limitations of the gradient descent algorithm, the training process of the Elman neural network tends to fall into local optimum solutions. To further improve the prediction accuracy, the Elman-NN structure was optimized using the PSO algorithm, and the model performance was tested and validated. Compared with the original Elman-NN, the hybrid PSO-Elman-NN has smaller MSE and MAE values, with a maximum reduction of 43% and 25%, respectively. For the three test conditions, the maximum predicted temperature difference does not exceed 1.5 K, and the temperature difference decreases further as the discharge time increases. Moreover, the hybrid model’s prediction accuracy is significantly improved, with the coefficients of determination ( R2) increasing by 1.736%, 0.706%, and 1.851%, respectively. The PSO-Elman-NN performed well in terms of compatibility and accuracy of the battery temperature prediction.