A Hybrid Model for Predicting Steering Brake Squeal Based on Multibody Dynamics and Finite Element Methods

In recent years, the problem of automotive brake squeal during steering braking has attracted attention. Under the conditions of squealing, the loading of sprung mass is transferred, and lateral force is generated on the tire, resulting in stress and deformation of the suspension system. To predict the steering brake squeal propensity and explore its mechanism, we established a hybrid model of multibody dynamics and finite element methods to transfer the displacement values of each suspension connection point between two models. We successfully predicted the occurrence of steering brake squeal using the complex eigenvalue analysis method. Thereafter, we analyzed the interface pressure distribution between the pads and disc, and the results showed that the distribution grew uneven with an increase in the steering wheel angle. In addition, changes in the contact and restraint conditions between the pads and disc are the key mechanisms for steering brake squeal.

Design and Evaluation of Remote Driving Architecture on 4G and 5G Mobile Networks

Despite the progress in the development of automated vehicles in the last decade, reaching the level of reliability required at large-scale deployment at an economical price and combined with safety requirements is still a long road ahead. In certain use cases, such as automated shuttles and taxis, where there is no longer even a steering wheel and pedals required, remote driving could be implemented to bridge this gap; a remote operator can take control of the vehicle in situations where it is too difficult for an automated system to determine the next actions. In logistics, it could even be implemented to solve already more pressing issues such as shortage of truck drivers, by providing more flexible working conditions and less standstill time of the truck. An important aspect of remote driving is the connection between the remote station and the vehicle. With the current roll-out of 5G mobile technology in many countries throughout the world, the implementation of remote driving comes closer to large-scale deployment. 5G could be a potential game-changer in the deployment of this technology. In this work, we examine the remote driving application and network-level performance of remote driving on a recently deployed sub-6-GHz commercial 5G stand-alone (SA) mobile network. It evaluates the influence of the 5G architecture, such as mobile edge computing (MEC) integration, local breakout, and latency on the application performance of remote driving. We describe the design, development (based on Hardware-in-the-Loop simulations), and performance evaluation of a remote driving solution, tested on both 5G and 4G mobile SA networks using two different vehicles and two different remote stations. Two test cases have been defined to evaluate the application and network performance and are evaluated based on position accuracy, relative reaction times, and distance perception. Results show the performance of the network to be sufficient for remote driving applications at relatively low speeds (<40 km/h). Network latencies compared with 4G have dropped to half. A strong correlation between latency and remote driving performance is not clearly seen and requires further evaluation taking into account the influence of the user interface.

Substantiation of the influence of changes in the coefficient of axle distribution of braking force on the handling of a passenger car

The article considers the change of the radius of the instantaneous center of rotation of a car moving along a curved trajectory during braking, taking into account the lateral input of the wheels of both axles of cars, both equipped with electronic tracking systems and not equipped with such. A criterion for assessing the controllability of cars moving on a curved trajectory in a braked state, by comparing the ratio of the current speed of the car to the longitudinal base with the ratio of the coefficients of lateral tire input to the product of the longitudinal base of the car, mass and cosines. It is established that the radius of instantaneous rotation of the longitudinal axis of the car moving along a curved trajectory during braking depends on the speed of the center of mass of the car, the coefficient of axle distribution of braking force, physical characteristics of applied tires, steering wheel angle and design and weight parameters. As a result, it allows you to set controllability. The authors obtained dependences that will create new algorithms for the operation of modern electronic control systems for stabilizing the longitudinal axis of a braked car, taking into account the speed of the car, its design and weight characteristics, the main characteristics of its braking system (coefficient of axle braking force distribution), physical characteristics used tires on wheels and connect them to the angles of the steered wheels, controlling the deviation of the longitudinal axis, which allows the driver to maintain the possibility of quite sharp maneuvers directly in the braking process, moving along a curved trajectory.

Features of driving of the steering wheel driving cars

Existing methods of diagnosing steering can be characterized by low efficiency. For various reasons, both declarative and actual (supported by the equipment) methods, as a rule, have low accuracy and inability to localize faults. The car's built-in diagnostics cannot affect the situation due to the small number of sensors in the steering system. The reasons for the low accuracy of the methods include design features, low availability of components (low maintainability). Difficulties in localization of malfunctions are caused by the structural scheme which is characterized by parallel - consecutive construction. The parameters of diagnostic methods are analyzed, the proposed method is based on the structure of the steering, in the implementation of which test effects are applied to the steered wheels. In total it is necessary to carry out three measurements of backlashes and as a result of mathematical processing of results it becomes possible to localize malfunction in three links of consecutive elements of the steering mechanism or a steering drive. In accordance with this approach, steering is considered as a set of three structures - parallel and two sequential. Rack and pinion steering was used as a model. Here, the parallel structure includes elements of the steering linkage: swing arm, left and right; steering rod, left and right; steering rack - left and right hinges. The sequential structure - left, includes a swing arm, left; steering rod, left; steering rack hinge, left; steering gear, steering shaft, steering wheel. Accordingly, the sequential structure of the right includes similar elements with the attribute "right". The structure of the steering play is considered in a similar way. As a result, it becomes possible to obtain a transformed system of three algebraic equations connecting clearances in three groups of mates and backlashes in parallel and two sequential steering structures. To measure the backlash, the turntables of the BOSCH FWA 4410 stand were used; in another version, the wheels were hung out. As a result of tests carried out on VW GOLF, VW PASSAT and RENAULT 25 vehicles with significant mileage, data was obtained indicating the need for technical interventions on localized groups of interfaces.

Autonomous Vehicles

The first car was invented in 1870 by Siegfried Marcus. Actually, it was just a wagon with an engine but without a steering wheel and without brakes. Instead, it was controlled by the legs of the driver. Converting traditional vehicles into autonomous vehicles was not just one step. The first step was just 28 years after the invention of cars, that is to say 1898. This step's concept was moving a vehicle by a remote controller. Since this first step and as computers have been becoming advanced and sophisticated, many functions of modern vehicles have been converted to be entirely automatic with no need of even remote controlling. Changing gears was one of the first actions that could be done automatically without an involvement of the driver, so such cars got the title of “automatic cars”; however, nowadays there are vehicles that can completely travel by themselves although they are not yet allowed to travel on public roads in most of the world. Such vehicles are called “autonomous vehicles” or “driverless cars”.

Influence of the position of the center of mass of a trailer category O1 on the stability of the road train

In a number of operational properties of motor vehicle (ATZ) at the tendency of increase of speeds of movement the most important indicators of the kept quality, in any modes, are stability and controllability. The choice of constructive parameters of ATZ providing these properties increases active safety of operation and reduces probability of road accidents during the execution of transport operations. From the point of view of practical purposes at operation of ATZ not only the reason of infringement of stability becomes important, and reaction of ATZ to it and control actions of the driver which are ambiguous and unstable. Therefore, it is assumed that the stability and controllability of the ATZ movement should be provided by the design parameters of the machine itself. The result of the analysis of the course stability of the road train was the expression of the critical speed of rectilinear motion. According to the developed mathematical model, the critical velocity is determined. Calculations were made for a road train consisting of a VAZ-2107 car and the uniaxial trailer for different loads of the trailer and different location of its center of mass. According to the initial data inherent in the nominal load of the car and the maximum load of the trailer and the location of the center of mass of the trailer on the longitudinal axis and in the center of mass of the loading platform, the critical speed is about 36 m/s (129.6 km/h). In transient modes of movement, such as "entering the circle and moving in a circle", "jerk of the steering wheel", "shift", "snake", displacement of the center of mass of the trailer in both the longitudinal and transverse planes, the critical speed decreases, and more significantly reduction occurs when the transverse displacement of the center of mass. Thus, if at the maximum displacement of the center of mass of the trailer on the x-axis (x = -0.75 m) the rate of oscillation instability decreases by 36.4% (Gn = 350 kg), 38.4% (Gn = 500 kg) and 44.3% (Gn = 750 kg) in comparison with this speed in the absence of displacement, then at the maximum displacement along the y -axis in the rate of oscillation instability decreases by 45.4%, 55.2% and 63.6%, respectively. In the case of such a trailer loading, the center of mass of the trailer shifts along both the x-axis and the y-axis, there is a further decrease in both the critical speed of the road train and the rate of oscillation instability. This must be taken into account when loading the trailer.

Torque allocation strategy for four in-wheel-motor drive electric vehicle based on layered control

The differential torque of four in-wheel-motor drive electric automotive will affect vehicle stability, and applications of the differential driven assisting steering (DDAS) will be limited consequentially. To solve this problem, stability analysis and control system design is essential, therefore a DDAS stability control system is designed based on the layered control of yaw moment. Correlation functions are used to reflect the shifts of vehicle characteristic state between stable and unstable states, and help to determine the control weight of each subsystem in the lower-layer controller. In the lower-layer controller, the strategy of direct steering-wheel torque control is used to build a DDAS controller. Under different vehicle moving states, differential driving torque and yaw moment vary with the change of the control weights; and according to the theory of quadratic programming, optimal allocation of four-wheel driving torques will be made according to the total driving torque. The effectiveness of the proposed control system is verified by numerical simulation and hardware-in-the-loop experiment. The results show that the proposed control method can improve vehicle stability and ensure driving safety.

The Identification of Non-Driving Activities with Associated Implication on the Take-Over Process

In conditionally automated driving, the engagement of non-driving activities (NDAs) can be regarded as the main factor that affects the driver’s take-over performance, the investigation of which is of great importance to the design of an intelligent human–machine interface for a safe and smooth control transition. This paper introduces a 3D convolutional neural network-based system to recognize six types of driver behaviour (four types of NDAs and two types of driving activities) through two video feeds based on head and hand movement. Based on the interaction of driver and object, the selected NDAs are divided into active mode and passive mode. The proposed recognition system achieves 85.87% accuracy for the classification of six activities. The impact of NDAs on the perspective of the driver’s situation awareness and take-over quality in terms of both activity type and interaction mode is further investigated. The results show that at a similar level of achieved maximum lateral error, the engagement of NDAs demands more time for drivers to accomplish the control transition, especially for the active mode NDAs engagement, which is more mentally demanding and reduces drivers’ sensitiveness to the driving situation change. Moreover, the haptic feedback torque from the steering wheel could help to reduce the time of the transition process, which can be regarded as a productive assistance system for the take-over process.

Validation of Vehicle Driving Simulator from Perspective of Velocity and Trajectory Based Driving Behavior under Curve Conditions

With their advantages of high experimental safety, convenient setting of scenes, and easy extraction of control parameters, driving simulators play an increasingly important role in scientific research, such as in road traffic environment safety evaluation and driving behavior characteristics research. Meanwhile, the demand for the validation of driving simulators is increasing as its applications are promoted. In order to validate a driving simulator in a complex environment, curve road conditions with different radii are considered as experimental evaluation scenarios. To attain this, this paper analyzes the reliability and accuracy of the experimental vehicle speed of a driving simulator. Then, qualitative and quantitative analysis of the lateral deviation of the vehicle trajectory is carried out, applying the cosine similarity method. Furthermore, a data-driven method was adopted which takes the longitudinal displacement, lateral displacement, vehicle speed and steering wheel angle of the vehicle as inputs and the lateral offset as the output. Thus, a curve trajectory planning model, a more comprehensive and human-like operation, is established. Based on directional long short-term memory (Bi–LSTM) and a recurrent neural network (RNN), a multiple Bi–LSTM (Mul–Bi–LSTM) is proposed. The prediction performance of LSTM, MLP model and Mul–Bi–LSTM are compared in detail on the validation set and testing set. The results show that the Mul–Bi–LSTM model can generate a trajectory which is very similar to the driver’s curve driving and have a preferable generalization performance. Therefore, this method can solve problems which cannot be realized in real complex scenes in the simulator validation. Selecting the trajectory as the validation parameter can more comprehensively and intuitively reflect the simulator’s curve driving state. Using a speed model and trajectory model instead of a real car experiment can improve the efficiency of simulator validation and lay a foundation for the standardization of simulator validation.