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This paper explores the role and challenges of Artificial Intelligence (AI) algorithms, specifically AI-based software elements, in autonomous driving systems. These AI systems are fundamental in executing real-time critical functions in complex and high-dimensional environments. They handle vital tasks like multi-modal perception, cognition, and decision-making tasks such as motion planning, lane keeping, and emergency braking. A primary concern relates to the ability (and necessity) of AI models to generalize beyond their initial training data. This generalization issue becomes evident in real-time scenarios, where models frequently encounter inputs not represented in their training or validation data. In such cases, AI systems must still function effectively despite facing distributional or domain shifts. This paper investigates the risk associated with overconfident AI models in safety-critical applications like autonomous driving.

During the vehicle lifecycle, customers are able to directly perceive the outer panel stiffness of vehicles in various environmental conditions. The outer panel stiffness is an important factor for customers to perceive the robustness of the vehicle. In the real test of outer panel stiffness after prototype production, evaluators manually press the outer panel in advance to identify vulnerable areas to be tested and evaluate the performance only in those area. However, when developing the outer panel stiffness performance using FEA (Finite Element Analysis) before releasing the drawing, it is not possible to filter out these areas, so the entire outer panel must be evaluated. This requires a significant amount of computing resources and manpower. In this study, an approach utilizing artificial intelligence was proposed to streamline the outer panel stiffness analysis and improve development reliability.

Roller offsetting is an incremental forming technique used to generate offset stiffening or mating features in sheet metal parts. Compared to die forming, roller offsetting utilizes generic tooling to create versatile designs at a relatively lower forming speed, making it well-suited for low volume productions in automotive and other industries. However, more significant distortion can be generated from roller offset forming process resulting from springback after forming. In this work, we use particle swarm optimization to identify the tool path and resulting feature geometry that minimizes distortion. In our approach, time-dependent finite element simulations are adopted to predict the distortion of each candidate tool path using a quarter symmetry model of the part. A multi-objective fitness function is used to both minimize the distortion measure while constraining the minimal radius of curvature in the tool path.

Typically, modern automotive engine designs include separate cylinder heads and cylinder blocks and utilize a multilayer steel head gasket (MLS) to seal the resulting joint. Cylinder head bolts are used to hold the joint together and the non-linear properties of head gasket provide capability to seal the movement within the joint, which is essential for engine durability and performance. The current design of cylinder head gasket mainly evaluates the sealing performance in hot and cold state through finite element analysis. The sealing performance of cylinder head gasket is mainly determined by sealing pressure, fatigue and lateral movement in the joint, which have been widely studied [1]. However, no one has been involved in the study of factors affecting sealing pressure and lateral movement in the joint.

Highway safety remains a significant concern, especially in mixed traffic scenarios involving heavy-duty vehicles (HDV) and smaller passenger cars. The vulnerability of HDVs following closely behind smaller cars is evident in incidents involving the lead vehicle, potentially leading to catastrophic rear-end collisions. This paper explores how automatic speed enforcement systems, using speed cameras, can mitigate risks for HDVs in such critical situations. While historical crash data consistently demonstrates the reduction of accidents near speed cameras, this paper goes beyond the conventional notion of crash occurrence reduction. Instead, it investigates the profound impact of driver behavior changes within desired travel speed distribution, especially around speed cameras, and their contribution to the safety of trailing vehicles, with a specific focus on heavy-duty trucks in accident-prone scenarios.

A fully instrumented Tesla Model 3 was used to collect thousands of hours of real-world automated driving data, encompassing both Autopilot and Full Self-Driving modes. This comprehensive dataset included vehicle operational parameters from the data busses, capturing details such as powertrain performance, energy consumption, and the control of advanced driver assistance systems (ADAS). Additionally, interactions with the surrounding traffic were recorded using a perception kit developed in-house equipped with LIDAR and a 360-degree camera system. We collected the data as part of a larger program to assess energy-efficient driving behavior of production connected and automated vehicles. One important aspect of characterizing the test vehicle is predicting its car-following behavior. Using both uncontrolled on-road tests and dedicated tests with a lead car performing set speed maneuvers, we tuned conventional adaptive cruise control (ACC) equations to fit the vehicle’s behavior.

The knowledge of representative load collectives and duty cycles is crucial for designing and dimensioning vehicles and their components. For human driven vehicles, various methods are known for deriving these load spectra directly or indirectly from fleet measurement data of the customer vehicle operation. Due to the lack of market penetration of highly automated and autonomous vehicles, there is no sufficient fleet data available to utilize these methods. As a result of increased demand for ride comfort compared to human driven vehicles, autonomous vehicle operation promises reduced driving speeds as well as reduced lateral and longitudinal accelerations. This can consequently lead to decreasing operation loads, thus enabling potentially more light-weight, cost-effective, resource-saving and energy-efficient vehicle components.

Abrasion of the Electromechanical brake (EMB) brake pad during the braking process leads to an increase in brake gap, which adversely affects braking performance. Therefore, it is imperative to promptly detect brake pad abrasion and adjust the brake gap accordingly. However, the addition of extra gap adjustment or sensor detection devices will bring extra size and cost to the brake system. In this study, we propose an innovative EMB gap active adjustment strategy by employing modeling and analysis of the braking process. This strategy involves identifying the contact and separation points of the braking process based on the differential current signal. Theoretical analysis and simulation results demonstrate that this gap adjustment strategy can effectively regulate the brake gap, mitigate the adverse effects of brake disk abrasion, and notably reduce the response time of the braking force output. Monitoring is critical to accurately control EMB clamping force.

Human driving behavior's inherent variability, randomness, individual differences, and dynamic vehicle-road situations give human-machine cooperative (HMC) driving considerable uncertainty, which affects the applicability and effectiveness of HMC control in complex scenes. To overcome this challenge, we present a novel data-enabled game output regulation approach for HMC driving. Firstly, a global human-vehicle-road (HVR) model is established considering the varied driver's steering characteristic parameters, such as delay time, preview time, and steering gain, as well as the uncertainty of tire cornering stiffness and variable road curvature disturbance. The robust output regulation theory has been employed to ensure the global DVR system's closed-loop stability, asymptotic tracking, and disturbance rejection, even with an unknown driver's internal state. Secondly, an interactive shared steering controller has been designed to provide personalized driving assistance.

Driver's driving style has a great impact on lane changing behavior, especially in scenarios such as freeway on-ramps that contain a strong willingness to change lanes, both in terms of inter-vehicle interactions during lane changing and in terms of the driving styles of the two vehicles. This paper proposes a study on game-theoretic decision-making for lane-changing on highway on-ramps considering driving styles, aiming to facilitate safer and more efficient merging while adequately accounting for driving styles. Firstly, the six features proposed by the EXID dataset of lane-changing vehicles were subjected to Principal Component Analysis (PCA) and the three principal components after dimensionality reduction were extracted, and then clustered according to the principal components by the K-means algorithm. The parameters of lane-changing game payoffs are computed based on the clustering centers under several styles.

Rubber mount as an important element can reduce the vibration transmitted by the engine to the frame. And under small and medium deformation conditions, Mooney-Rivlin model can well describe the mechanical properties of the rubber mount. The accurate parameters of Mooney-Rivlin model are the basis of describing the mechanical properties of the mount. First, taking powertrain rubber mount as the research object, the influence of preload on the static characteristics of the mount is studied by the preload test rig. Second, Particle swarm optimization-Back propagation neural network (PSO-BPNN) model and Back propagation neural network (BPNN) model was established. After the number and step length of hidden layer neurons were determined, the prediction accuracy of the two models is compared.

To address the issue of PID control for automotive vibration, this paper supplements and develops the evaluation of automotive vibration characteristics, and proposes a vibration response quantity for evaluating the energy dissipation characteristics of automotive vibration. A two-degree-of-freedom single wheel model for automotive vibration control is established, and the conventional vibration response variables for ride comfort evaluation and the energy consumption vibration response variables for energy dissipation characteristics evaluation are determined. This paper uses the Adaptive Differential Evolution (ADE) algorithm to tune the PID control parameters and introduces an adaptive mutation factor to improve the algorithm's adaptability. Several commonly used adaptive mutation factors are summarized in this paper, and their effects on algorithm improvement are compared.

Proportional integral derivative (PID) control technique is a famous and cost-effective control strategy, in real implementation, applied in various engineering applications. Also, the ant colony optimization (ACO) algorithm is extensively applied in various industrial problems. This paper addresses the usage of the ACO algorithm to tune the PID controller gains for a semi-active heavy vehicle suspension system integrated with cabin and seat. The magnetorheological (MR) damper is used in main suspension as a semi-active device to enhance the ride comfort and vehicle stability. The proposed semi-active suspension consists of a system controller that calculate the desired damping force using a PID controller tuned using ACO, and a continuous state damper controller that predict the input voltage that is required to track the desired damping force.

The performance of suspension system has a direct impact on the riding comfort and smoothness. For the traditional suspension can not effectively alleviate the impact of road surface and the poor anti-vibration performance, The dynamics model of vehicle suspension system is established, and the control model of vehicle four-degree-of-freedom active suspension is designed with fuzzy control strategy. On this basis, a comprehensive simulation model of the control model of vehicle active suspension coupled with road excitation is established. and the ride comfort of vehicles under different types of suspension are tested through Simulink. The simulation results show that compared with the passive suspension, the reduction of vehicle acceleration and dynamic deformation of the active suspension controlled by fuzzy PID can reach 33.76% and 22.45%. and the reduction of pitch Angle speed and dynamic load of the active suspension controlled by fuzzy PID can reach 16.18% and 10.72%.

The study investigates the ride comfort of a rail vehicle with semi-active suspension control and its effect on train vertical dynamics. The Harmony Search algorithm optimizes the gains of a proportional integral derivative (PID) controller using the self-adaptive global best harmony search method (SGHS) due to its effectiveness in reducing the tuning time and offering the least objective function value. Magnetorheological (MR) dampers are highly valuable semi-active devices for vibration control applications rather than active actuators in terms of reliability and implementation cost. A quarter-rail vehicle model consisting of six degrees of freedom (6-DOF) is simulated using MATLAB/Simulink software to evaluate the proposed controller's effectiveness. The simulated results show that the optimized PID significantly improves ride comfort compared to passive.

Intelligent tyres can offer crucial insights into tyre dynamics, serving as a fundamental information source for vehicle state estimation and thereby enabling vehicular safety control. Among the numerous tyre parameters, slip ratio stands out as a direct influencer of vehicle motion characteristics. Accurate estimation of tyre slip ratio is essential for vehicle safety. Firstly, an analysis of the fundamental composition of tyres was conducted, and appropriate simplifications were applied to the tyre structure. Additionally, a finite element model of the tyre was constructed using ABAQUS software. To validate the reliability of the model, a real vehicle testing system was established, consisting of the experimental vehicle, data acquisition system, and supervisory computer. The reliability of the finite element model was confirmed by assessing the consistency of acceleration signals in three different directions of the tyre.

Road roughness is the most important source of vertical loads for road vehicles. To capture this during durability engineering, various mathematical models for describing road profiles have been developed. The Laplace process has turned out to be a suitable model, which can describe road profiles in a more flexible way than e.g., Gaussian processes. The Laplace model essentially contains two parameters called C and ν (to be explained below), which need to be adapted to represent a road with certain roughness properties. Usually, local road authorities provide such properties along a road on sections of constant length, say, 100 m. Often the ISO 8608 roughness coefficient or the IRI (International Roughness Index) are used. In such cases, there are well known explicit formulas for finding the parameters C and ν of the Laplace process, which best fits the road under certain assumptions.

The soft and rough terrain on the planet's surface significantly affects the ride and safety of rovers during high-speed driving, which imposes high requirements for the control of the suspension system of planet rovers. To ensure good ride comfort of the planet rover during operation in the low-gravity environment of the planet's surface, this study develops an active suspension control strategy for torsion spring and torsional damper suspension systems for planet rovers. Firstly, an equivalent dynamic model of the suspension system is derived. Based on fractal principles, a road model of planetary surface is established. Then, a fuzzy-PID based control strategy aimed at improving ride comfort for the planet rover suspension is established and validated on both flat and rough terrains.

The steer-by-wire (SBW) system, an integral component of the drive-by-wire chassis responsible for controlling the lateral motion of a vehicle, plays a pivotal role in enhancing vehicle safety. However, it poses a unique challenge concerning steering wheel return control, primarily due to its fundamental characteristic of severing the mechanical connection between the steering wheel and the turning wheel. This disconnect results in the inability to directly transmit the self-aligning torque to the steering wheel, giving rise to complications in ensuring a seamless return process. In order to realize precise control of steering wheel return, solving the problem of insufficient low-speed return and high-speed return overshoot of the steering wheel of the SBW system, this paper proposes a steering wheel active return control strategy for SBW system based on the backstepping control method.

To characterize the stress flow behavior of engineering plastic glass fiber reinforced polypropylene (PPGF) commonly used in automotive interior and exterior components, mechanical property is measured using a universal material testing machine and a servo-hydraulic tensile testing machine under quasi-static, high temperature, and high strain rate conditions. Stress versus strain curves of materials under different conditions are obtained. Based on the measured results, a new parameter identification method of the Johnson-Cook (J-C) constitutive model is proposed by considering the adiabatic temperature rise effect. Firstly, a material-level experiment method is carried out for glass fiber reinforced polypropylene (PPGF) materials, and the influence of wide strain rate range, and large temperature span on the material properties is studied from a macroscopic perspective.