Vehicle Active Safety Research Articles

An Obstacle Detection and Distance Measurement Method for Sloped Roads Based on VIDAR

Environmental perception systems can provide information on the environment around a vehicle, which is key to active vehicle safety systems. However, these systems underperform in cases of sloped roads. Real-time obstacle detection using monocular vision is a challenging problem in this situation. In this study, an obstacle detection and distance measurement method for sloped roads based on Vision-IMU based detection and range method (VIDAR) is proposed. First, the road images are collected and processed. Then, the road distance and slope information provided by a digital map is input into the VIDAR to detect and eliminate false obstacles (i.e., those for which no height can be calculated). The movement state of the obstacle is determined by tracking its lowest point. Finally, experimental analysis is carried out through simulation and real-vehicle experiments. The results show that the proposed method has higher detection accuracy than YOLO v5s in a sloped road environment and is not susceptible to interference from false obstacles. The most prominent contribution of this research work is to describe a sloped road obstacle detection method, which is capable of detecting all types of obstacles without prior knowledge to meet the needs of real-time and accurate detection of slope road obstacles.

Development and Validation of AEBS Anti-slip Control Model for In-wheel Motor Drive EV in AMESim Co-simulation with Matlab/Simulink

Abstract The Advanced Emergency Braking System (AEBS) is an intelligent vehicle active safety system to avoid certain sorts of collisions. Recent researches on AEBS emphasized radar, lasers and video camera technologies mostly, also controller developments were studied from Vehicle to Vehicle (V2V) to Vehicle to Pedestrian(V2P) and other aspects with different scenarios, aiming to enhance the active safety of the vehicle. However, most of the current systems focus little on braking performance of AEBS, especially for EV with distributed drive system, like in-wheel motors. In this paper, A 15 Degree of Freedom (DOF) distributed drive Electric Vehicle (EV) model was created and an Anti-slip Controller of AEBS based on the logic threshold method was built. To validate this control strategy, a co-simulation platform between Simulink and AMESim was established and vehicle braking dynamic of four in-wheel motor drive EV was evaluated. Results display that the designed controller not only enhanced the braking performance of AEBS under sudden braking on slippery surface condition, but also meet the requirement of UNECE braking regulation which has practical meanings in boosting active safety of a four in-wheel motor drive EV and development of industrialization.

Road Adhesion Coefficient Identification Method Based on Vehicle Dynamics Model and Multi-Algorithm Fusion

<div class="section abstract"><div class="htmlview paragraph">As an important input parameter of intelligent vehicle active safety technology, road adhesion coefficient is of great significance in autonomous collision avoidance, emergency braking and collision avoidance, and variable adhesion road motion control. Traditional recognition methods based on vehicle dynamics require large data volume and low solution accuracy. This paper proposes an adhesion coefficient recognition method based on Elman neural network and Kalman filter. By establishing a seven-degree-of-freedom vehicle dynamics model, dynamic parameters such as yaw angular velocity, longitudinal velocity, lateral velocity, and angular velocity of each wheel, which are easy to measure and strongly related to the road adhesion coefficient, are analyzed as the input of the neural network model. The square root cubature Kalman filter algorithm is used to remove the noise of the input of the neural network model, and Q-learning is used to strengthen learning, and the weight coefficient and bias coefficient of the model are continuously rewarded and punished, so that the predicted value does not exceed the normal range of values. The algorithm was pre-trained through CarSim/Simulink co-simulation, 9 sets of simulation conditions were established, and 4 sets of verification schemes were designed for identification and inspection. The average error of the simulation process is 4.93%, and the accuracy is 91.23%. Compared with the traditional Elman neural network, the average recognition error of this method is reduced by 2.23%, and the accuracy rate is increased by 9.83%. Real vehicle experiments were carried out on wet asphalt pavement and dry asphalt pavement, verifying the feasibility of the method. This paper proposes a road adhesion coefficient recognition method, which can improve the applicability of intelligent vehicle active safety systems to complex scenarios.</div></div>

Research on the Identification of Tyre-Road Peak Friction Coefficient under Full Slip Rate Range Based on Normalized Tyre Model

The accurate estimation of the tyre-road peak friction coefficient is the key basis for the normal operation of the vehicle active safety control system. The estimation algorithm needs to be able to adapt to various conditions encountered in the actual driving process of the vehicle and obtain the estimation results timely and accurately. Therefore, a new normalized strategy is proposed in this paper. The core is the equal ratio between the peak friction coefficient and the utilization friction coefficient between adjacent typical roads. This strategy can establish the direct connection (normalization) between tyre force and tyre-road peak friction coefficient through most tyre models in the field of vehicle dynamics and accomplish estimation by combining with the filtering algorithm. In addition, most of the vehicle dynamic estimation algorithms are limited by road excitation, and it is difficult to obtain satisfactory estimation results. This strategy can greatly reduce the system error caused by insufficient road excitation (slip rate is not 0.15–0.20) and improve the applicability of the estimation algorithm to the actual driving process of the vehicle. Finally, the magic formula (MF) tyre model is selected to describe the tyre characteristics after treatment of the normalized strategy; the tyre-road peak friction coefficient is estimated by combining the extended Kalman filter and vehicle dynamics model. Satisfactory estimation results are obtained in both simulation and real vehicle tests, which verifies the effectiveness of the proposed normalized strategy.

A Hybrid Surrogate Modeling Approach for Vehicle Crash Simulations

Integrated vehicle safety – a combination of active and passive vehicle safety systems has the potential to increase occupant safety dramatically. To adapt the passive safety components such as belt and airbag to a given collision scenario, the system requires a priori knowledge of the collision consequences, i.e. via a camera, radar or lidar system. Therefore, real-time capable surrogate models are needed, that predict the vehicle decelerations during a crash with high accuracy. To combine the strength of both data-driven and physics-based surrogate modeling techniques, a hybrid surrogate model consisting of a machine learning-enhanced spring-damper-mass model, is developed in this work. Thereby, the spring-damper-mass model describes the deformation behavior of the vehicle structure. A neural network is then trained to predict the stiffness and damping parameters of the simplified physical model based on the impact angle, the velocity and the offset of the vehicles.

Процесс взаимодействия эластичной шины c опорной поверхностью дороги, покрытой противогололедным материалом на основе технической соли

When studying the traction properties of the elastic tire, it should be kept in mind that it is the investigation of the lateral reaction formation in the wheel contact patch with the supporting surface when it is moving with a lateral drifting that is particularly important for the researcher. In spite of numerous publications on this problem, there are practically no studies of lateral traction of the elastic wheel tire with a winter slippery road treated with chemical de-icing agent. The purpose of this study is to analyze the process of interaction of the elastic tire with a road surface covered with snow glaze and sand-salt mixture. As a result of this research, three variants of interaction of the elastic tire with the winter road have been revealed based on the different models of friction, namely, boundary, mixed and viscous models of friction, depending on the melting ability of technical salt. When mixed and viscous friction occurs between the tire and the road surface, a «third» body is formed, consisting of a mixture of particles of snow-ice deposits, salt and sand, which is able to slide on the layer of liquid salt solution formed on the supporting road surface, and significantly reduce the tire traction properties when driving with lateral drifting. The obtained results contribute to enlarging new scientific knowledge regarding lateral traction of elastic tires with the supporting surface of the road with winter slipperiness, which is important for increasing the active safety of vehicles on the roads in the cold season.

Review of the coatings used for brake discs regarding their wear resistance and environmental effect

Wear of a friction pair of brake (brake disc and pads), in addition to reducing the active safety of vehicles, leads to the formation of particles that can affect the environment and human health. In addition to the technologies that are being developed for the collection of particles created by the wear of brakes during braking process, today new materials are being introduced, as well as various technologies for processing friction pairs with the aim of reducing brake wear and thus the formation of particles. Furthermore, today, technologies for coating (cladding) the friction surfaces of disc brakes with some materials are increasingly applied and researched, in order to reduce the wear intensity (wear rate) of disc brakes, i.e. the emission of particles created by wear of friction pairs. The aim of this paper is to analyse and review different deposition techniques and materials used for brake discs coatings, as well as the effect it has on the wear rate of friction pair. There are many coating deposition techniques, and special attention is paid to the technology of laser hardfacing of brake rotors.

Vehicle rollover warning system based on TTR method with inertial measurement

This paper presents a theoretical and experimental study conducted on the rollover warning of wheeled off-road operating vehicles. The time to rollover warning algorithm was studied with real-time vehicle roll angle and roll angle velocity as the input variables, and lateral load transfer ratio was used as the rollover determination index. Subsequently, a vehicle dynamics model was built using CarSim software, and a warning algorithm was established in the MATLAB/Simulink environment. The rollover joint simulation in CarSim and MATLAB/Simulink was conducted under typical working conditions. Finally, combined with inertial measurements, a rollover warning system was independently developed. In addition, the rollover warning system was installed on a light forest firefighting truck to verify the feasibility of the system via a real vehicle experiment, and the law of vehicle rollover motion was also studied. The serpentine experiment and steady-state rotation experiment were conducted. The experimental results showed that at identical front-wheel steering angles, the roll angle and lateral acceleration increased with an increase in the vehicle speed. Furthermore, for identical vehicle speeds, the roll angle and lateral acceleration of the vehicle increased with an increase in the front-wheel steering angle. The dangerous vehicle speed was 50 km h−1 in the serpentine condition and 40 km h−1 in the steady-state rotation condition. The risk trend and alarm signal obtained by the rollover warning system were consistent with the actual situation. Thus, this can assist drivers in judging the rollover risk and effectively improve the active safety of special vehicles. Furthermore, it also provides a reference for further research on active rollover control technology of special vehicles.

Estimation of Vehicle Longitudinal Speed Based on Improved Kalman Filter

Estimation of vehicle longitudinal acceleration is very important in vehicle active safety control system. In this paper, two driving conditions of a 4WD off-road vehicle are divided by vehicle signals such as steering angle. Under different working conditions, different estimation algorithms are adopted. In the straight driving condition, the longitudinal speed was estimated by adjusting the variance weight of acceleration Kalman observation noise based on kinematics method. For steering conditions, in order to obtain the longitudinal velocity at the center of mass, by dynamic method, a lateral state estimator was designed and tire sideslip dynamics was modeled. The CarSim-Simulink co-simulation results show that the proposed algorithm has high accuracy and strong practicability.

Calculation of electric vehicle battery system

Designing a vehicle is a complex multi-stage process and involves many factors which required carefully in calculation such as: the vehicle's dynamic, active and passive safety systems, connections on the vehicle, arrangement of devices, etc. The same principles when we design an electric vehicle. In this paper, the researcher showed a method to calculate battery system on an electric vehicle from a vehicle that using an internal combustion engine and still ensure the same output power as the original car. The car only uses Li-ion batteries, the car can travel on a single charge is nearly 300 km. The results have calculated the battery system to satisfy the capacity of the original vehicle. Through the calculated results can be applied on real cars.

Research on Electric Vehicle Rollover Prevention System Based on Motor Speed Control

Vehicle driving safety is an important performance indicator for vehicles, and there is still much room for development in the active safety control of electric vehicles. A vehicle rollover is an important road traffic safety problem, as rollover accidents cause serious casualties and huge economic losses. It is very easy for vehicles in high-speed sharp turns or high-speed overtaking to roll over; in order to improve the vehicle in these conditions with the anti-rollover stability, this study proposed a real-time motor control strategy, mainly through the acquisition of vehicle attitude data and the use of multi-sensor fusion on the vehicle running state for real time. The lateral load transfer rate was used as the vehicle rollover evaluation index, and the test results indicate that when the real-time rollover index exceeds the set limit safety threshold, the motor speed is reduced through active control so that the vehicle avoids rollover accidents, or the risk of rollover is reduced. The STM32F103RET6 was used as the main chip for hardware design, control board fabrication, control program software design, and joint testing of software and hardware. The tests and data analysis prove that the motor control strategy is reliable in real time and can significantly improve the active safety of electric vehicles.

The choice of rational complex of active safety systems for smart cars

The problem of ensuring road safety affects all elements of the Driver-Car-Road-Environment system. Smart cars equipped with enough traffic assistants can significantly improve road safety. Active vehicle safety systems, including intelligent driver assistance systems and assistants, perform similar road safety functions. With all the variety of possibilities for equipping cars with systems complexes, the need arises to assess the feasibility and profitability of installing a particular complex of systems. For this, it is proposed to apply the methods of multi-criteria assessment. As a result of calculations, the best options for the sets of systems that most widely cover the road situation have been identified.

Performance evaluation of surrogate measures of safety with naturalistic driving data

Surrogate measures of safety (SMoS) play an important role in detecting traffic conflicts and in traffic safety assessment. However, the underlying assumptions of SMoS are different and a certain SMoS may be adequate/inadequate for different applications. A comprehensive approach to evaluate the validity and applicability of SMoS is lacking in the literature. This study proposes such a framework that supports evaluating SMoS in multiple dimensions. We apply the framework to gain insights into the characteristics of six widely-used SMoS for longitudinal maneuvers, i.e., Time to Collision (TTC), single-step Probabilistic Driving Risk Field (S-PDRF), Deceleration Rate to Avoid a Crash (DRAC), Potential Index for Collision with Urgent Deceleration (PICUD), Proactive Fuzzy Surrogate Safety Metric (PFS), and the Critical Fuzzy Surrogate Safety Metric (CFS). To ensure comparability, all measures are calibrated with the same risk detection criterion. Four performance indicators, i.e., Prediction Accuracy, Timeliness, Robustness, and Efficiency are computed for all six SMoS and validated using naturalistic driving data. The strengths and weaknesses of all six measures are compared and analyzed elaborately. A key result is that not a single SMoS performs well in all performance dimensions. S-PDRF performs best in terms of Robustness but consumes the most time for computation. TTC is the most efficient but performs poorly in terms of Timeliness and Robustness. The proposed evaluation approach and the derived insights can support SMoS selection in active vehicle safety system design and traffic safety assessment.

A comparative study of the recursive least squares and fuzzy logic estimation methods for the measurement of road adhesion coefficient

ABSTRACT Vehicle active safety system is the hotspot of current research and development, and the road adhesion coefficient is one of the essential parameters in the development of automobile active safety system, which plays a pivotal role in automobile safety. To improve the real-time performance and accuracy of the road adhesion coefficient, this paper proposes different control methods and compares and analyzes them. To accurately estimate the road adhesion coefficient in real-time, an eight-degree-of-freedom vehicle model was adopted in this paper. According to the tire torque and wheel speed, the adhesion coefficient was estimated based on the second-order nonlinear extended state observer. The experimental data, such as slip rate, using the improved tire model, is based on the recursive least squares method with forgetting factor and fuzzy logic algorithm real-time of tire-road friction coefficient estimation. The results show that the observer can effectively observe utilisation adhesion coefficient, and both control algorithms can estimate the road adhesion coefficient in real-time, effectively and quickly. On different roads, the use of the recursive least squares algorithm resulted in higher estimation accuracy, better robustness, and better control effect than using a fuzzy logic algorithm.

Observation of Dynamic State Parameters and Yaw Stability Control of Four-Wheel-Independent-Drive EV

Vehicle yaw stability control is an important part of the active safety of electric vehicles. In order to realize the yaw stability control of vehicles, this paper takes 4-WID electric vehicles as the research object, studies the nonlinear estimation of the state parameters of the lateral stability dynamic system and the yaw stability control strategy. The vehicle state parameter estimation strategy, based on the unscented Kalman filter (UKF) algorithm and the model predictive control algorithm, are designed to control the vehicle yaw stability, which realizes the safe and stable driving of the vehicle. Through CarSim–Simulink joint simulation and hardware-in-the-loop (HIL) experiments based on MicroAutoBox, the effectiveness and real-time performance of the designed control strategy are fully verified, which accelerate the development process of the vehicle controller, and realizes the safe and stable driving of the vehicle.

Research on Control Strategy and Central Control System of Intelligent Electric Vehicle

With the continuous improvement of world science and technology, vehicle technology has also been greatly developed. Cars bring convenience to people’s travel, and occupy an increasingly high proportion in social life. Automobile plays an important role in the continuous progress of contemporary society and the change of human travel conditions. At the same time, the increasing number of cars also brings many problems: energy consumption and environmental pollution are worsening, traffic accidents are increasing year by year. According to the who survey, the number of casualties caused by traffic accidents in China is gradually increasing. High traffic accidents have caused irreversible losses to social development, people’s lives and property. It has become the development goal to improve the vehicle active safety, control the vehicle to automatically identify the road, drive according to the planned route and reduce the frequency of traffic accidents. In the traditional driving environment, people occupy a dominant position. In the face of complex and changeable traffic environment and a large number of traffic information, it is easy to make mistakes in driving and cause serious traffic accidents. Therefore, it is very important to extricate the driver from the complex and changeable traffic environment and make driving safer. The intelligent vehicle collects the information about the road and the vehicle itself through the sensor, and sends it to the central controller which controls the transverse and longitudinal movement of the electric vehicle. The central controller sends out corresponding instructions to control the horizontal and longitudinal operation of the vehicle according to the control strategy, so as to realize the control of the driving behavior of the vehicle, so that the vehicle can drive safely on the desired track at the expected speed, It can avoid driver fatigue driving behavior and traffic accidents caused by driving technology. Various sensors in the intelligent vehicle can obtain more vehicle information at the same time, and control the vehicle through the controller processing.

Vehicle State Estimation Based on Strong Tracking Central Different Kalman Filter

Vehicle active safety control was a key technology to avoid serious safety accidents, and accurate acquisition of vehicle states signals was a necessary prerequisite to achieve active vehicle safety control. Based on the purpose, a 3-DOF nonlinear vehicle dynamics model containing constant noise and a nonlinear tire model were established, and several vehicle key states were estimated by a strong tracking central different Kalman filter (CDKF). The conclusion showed that the proposed estimator had higher accuracy and less computation requirement than the CKF, CDKF, and UKF estimators. Numerical simulation and experiments indicated that the proposed vehicle state estimation method not only had higher estimation accuracy but also had higher real-time function.

Analysis of Vehicle Moving Parameters in Various Road Conditions

The safety of road users is one of the priority issues raised by those involved in vehicle design, latest passive and active safety systems, traffic organization or driver education. Nowadays, an important road safety problem is the behaviour of drivers in emergency situations. In order to measurably estimate the driving quality, parameters such as velocity, acceleration, the way and frequency of using the control pedals are quite often used. This article describes how to assess driver's behaviour based on measurements taken on the road. The frequency of different acceleration ranges during the vehicle drive was determined based on the results obtained. For the arbitrarily adopted acceleration range of - 0.5 - 0.5 m/s2, the driver's working time was over 77 percent on average, with the difference varying significantly between different route sections. Similarly, the study compares the driving times for other ranges of acceleration.

A Novel Comprehensive Scheme for Vehicle State Estimation Using Dual Extended H-Infinity Kalman Filter

The performance of vehicle active safety systems relies on accurate vehicle state information. Estimation of vehicle state based on onboard sensors has been popular in research due to technical and cost constraints. Although many experts and scholars have made a lot of research efforts for vehicle state estimation, studies that simultaneously consider the effects of noise uncertainty and model parameter perturbation have rarely been reported. In this paper, a comprehensive scheme using dual Extended H-infinity Kalman Filter (EH∞KF) is proposed to estimate vehicle speed, yaw rate, and sideslip angle. A three-degree-of-freedom vehicle dynamics model is first established. Based on the model, the first EH∞KF estimator is used to identify the mass of the vehicle. Simultaneously, the second EH∞KF estimator uses the result of the first estimator to predict the vehicle speed, yaw rate, and sideslip angle. Finally, simulation tests are carried out to demonstrate the effectiveness of the proposed method. The test results indicate that the proposed method has higher estimation accuracy than the extended Kalman filter.

INFLUENCE OF VEHICLE SUSPENSION PARAMETERS ON ITS BRAKING PROPERTIES

The paper focuses on the study of the performance of elastic and damping parameters of the car suspension. The problem of arising vibrations of the sprung and unsprung vehicle masses on the braking process is considered. The article presents theoretical models of vehicle braking taking into account the oscillations of the vehicle's sprung masses. The research results will make it possible to establish the relationship between the suspension parameters and the estimated parameters of braking properties, to supplement the "driver - car - road" system, and also to more accurately assess the active safety of vehicles associated with braking dynamics.