Vehicle state and tire-road adhesion are of great use and importance to vehicle active safety control systems. However, it is always not easy to obtain the information with high accuracy and low expense. Recently, man...Vehicle state and tire-road adhesion are of great use and importance to vehicle active safety control systems. However, it is always not easy to obtain the information with high accuracy and low expense. Recently, many estimation methods have been put forward to solve such problems, in which Kalman filter becomes one of the most popular techniques. Nevertheless, the use of complicated model always leads to poor real-time estimation while the role of road friction coefficient is often ignored. For the purpose of enhancing the real time performance of the algorithm and pursuing precise estimation of vehicle states, a model-based estimator is proposed to conduct combined estimation of vehicle states and road friction coefficients. The estimator is designed based on a three-DOF vehicle model coupled with the Highway Safety Research Institute（HSRI） tire model; the dual extended Kalman filter （DEKF） technique is employed, which can be regarded as two extended Kalman filters operating and communicating simultaneously. Effectiveness of the estimation is firstly examined by comparing the outputs of the estimator with the responses of the vehicle model in CarSim under three typical road adhesion conditions（high-friction, low-friction, and joint-friction）. On this basis, driving simulator experiments are carried out to further investigate the practical application of the estimator. Numerical results from CarSim and driving simulator both demonstrate that the estimator designed is capable of estimating the vehicle states and road friction coefficient with reasonable accuracy. The DEKF-based estimator proposed provides the essential information for the vehicle active control system with low expense and decent precision, and offers the possibility of real car application in future.展开更多
The identification of maximum road friction coefficient and optimal slip ratio is crucial to vehicle dynamics and control.However,it is always not easy to identify the maximum road friction coefficient with high robus...The identification of maximum road friction coefficient and optimal slip ratio is crucial to vehicle dynamics and control.However,it is always not easy to identify the maximum road friction coefficient with high robustness and good adaptability to various vehicle operating conditions.The existing investigations on robust identification of maximum road friction coefficient are unsatisfactory.In this paper,an identification approach based on road type recognition is proposed for the robust identification of maximum road friction coefficient and optimal slip ratio.The instantaneous road friction coefficient is estimated through the recursive least square with a forgetting factor method based on the single wheel model,and the estimated road friction coefficient and slip ratio are grouped in a set of samples in a small time interval before the current time,which are updated with time progressing.The current road type is recognized by comparing the samples of the estimated road friction coefficient with the standard road friction coefficient of each typical road,and the minimum statistical error is used as the recognition principle to improve identification robustness.Once the road type is recognized,the maximum road friction coefficient and optimal slip ratio are determined.The numerical simulation tests are conducted on two typical road friction conditions（single-friction and joint-friction）by using CarSim software.The test results show that there is little identification error between the identified maximum road friction coefficient and the pre-set value in CarSim.The proposed identification method has good robustness performance to external disturbances and good adaptability to various vehicle operating conditions and road variations,and the identification results can be used for the adjustment of vehicle active safety control strategies.展开更多
The accurate estimation of road friction coeffi- cient in the active safety control system has become increasingly prominent. Most previous studies on road friction estimation have only used vehicle longitudinal or la...The accurate estimation of road friction coeffi- cient in the active safety control system has become increasingly prominent. Most previous studies on road friction estimation have only used vehicle longitudinal or lateral dynamics and often ignored the load transfer, which tends to cause inaccurate of the actual road friction coef- ficient. A novel method considering load transfer of front and rear axles is proposed to estimate road friction coef- ficient based on braking dynamic model of two-wheeled vehicle. Sliding mode control technique is used to build the ideal braking torque controller, which control target is to control the actual wheel slip ratio of front and rear wheels tracking the ideal wheel slip ratio. In order to eliminate the chattering problem of the sliding mode controller, integral switching surface is used to design the sliding mode sur- face. A second order linear extended state observer is designed to observe road friction coefficient based on wheel speed and braking torque of front and rear wheels. The proposed road friction coefficient estimation schemes are evaluated by simulation in ADAMS/Car. The results show that the estimated values can well agree with the actual values in different road conditions. The observer can estimate road friction coefficient exactly in real-time andresist external disturbance. The proposed research provides a novel method to estimate road friction coefficient with strong robustness and more accurate.展开更多
Road friction coefficient is a key factor for the stability control of the vehicle dynamics in the critical conditions. Obviously the vehicle dynamics stability control systems, including the anti-lock brake system（...Road friction coefficient is a key factor for the stability control of the vehicle dynamics in the critical conditions. Obviously the vehicle dynamics stability control systems, including the anti-lock brake system（ABS）, the traction control system（TCS）, and the active yaw control（AYC） system, need the accurate tire and road friction information. However, the simplified method based on the linear tire and vehicle model could not obtain the accurate road friction coefficient for the complicated maneuver of the vehicle. Because the active braking control mode of AYC is different from that of ABS, the road friction coefficient cannot be estimated only with the dynamics states of the tire. With the related dynamics states measured by the sensors of AYC, a comprehensive strategy of the road friction estimation for the active yaw control is brought forward with the sensor fusion technique. Firstly, the variations of the dynamics characteristics of vehicle and tire, and the stability control mode in the steering process are considered, and then the proper road friction estimation methods are brought forward according to the vehicle maneuver process. In the steering maneuver without braking, the comprehensive road friction from the four wheels may be estimated based on the multi-sensor signal fusion method. The estimated values of the road friction reflect the road friction characteristic. When the active brake involved, the road friction coefficient of the braked wheel may be estimated based on the brake pressure and tire forces, the estimated values reflect the road friction between the braked wheel and the road. So the optimal control of the wheel slip rate may be obtained according to the road friction coefficient. The methods proposed in the paper are integrated into the real time controller of AYC, which is matched onto the test vehicle. The ground tests validate the accuracy of the proposed method under the complicated maneuver conditions.展开更多
Many surveys on vehicle traffic safety have shown that the tire road friction coefficient(TRFC)is correlated with the probability of an accident.The probability of road accidents increases sharply on slippery road sur...Many surveys on vehicle traffic safety have shown that the tire road friction coefficient(TRFC)is correlated with the probability of an accident.The probability of road accidents increases sharply on slippery road surfaces.Therefore,accurate knowledge of TRFC contributes to the optimization of driver maneuvers for further improving the safety of intelligent vehicles.A large number of researchers have employed different tools and proposed different algorithms to obtain TRFC.This work investigates these different methods that have been widely utilized to estimate TRFC.These methods are divided into three main categories:off-board sensors-based,vehicle dynamics-based,and data-driven-based methods.This review provides a comparative analysis of these methods and describes their strengths and weaknesses.Moreover,some future research directions regarding TRFC estimation are presented.展开更多
Road friction coefficient real-time estimation methods is an important issue and problem in automotive active safety con- trol system development. First a fixed feedback gain sliding mode observer of road adhesion coe...Road friction coefficient real-time estimation methods is an important issue and problem in automotive active safety con- trol system development. First a fixed feedback gain sliding mode observer of road adhesion coefficient is designed through the es-tablishment of tire/road dynamic friction model in this article. The simulation results shows that the observer can well real-time iden-tify the current road adhesion characteristics. And more importantly, the observer only need wheel speed signal and the braking torque （brake pressure） signal, so the system is low cost, and its adaptability is good. There is no doubt this estimation method has a good application prospect.展开更多
According to the road adaptive requirements for the vehicle longitudinal safety assistant system an estimation method of the road longitudinal friction coefficient is proposed.The method can simultaneously be applied ...According to the road adaptive requirements for the vehicle longitudinal safety assistant system an estimation method of the road longitudinal friction coefficient is proposed.The method can simultaneously be applied to both the high and the low slip ratio conditions. Based on the simplified magic formula tire model the road longitudinal friction coefficient is preliminarily estimated by the recursive least squares method.The estimated friction coefficient and the tires model parameters are considered as extended states. The extended Kalman filter algorithm is employed to filter out the noise and adaptively adjust the tire model parameters. Then the final road longitudinal friction coefficient is accurately and robustly estimated. The Carsim simulation results show that the proposed method is better than the conventional algorithm. The road longitudinal friction coefficient can be quickly and accurately estimated under both the high and the low slip ratio conditions.The error is less than 0.1 and the response time is less than 2 s which meets the requirements of the vehicle longitudinal safety assistant system.展开更多
The precise and accurate control of the friction between tire and road is the foundation of the vehicle driving conditions furthest utilization and vehicle dynamics control. First the tire/road longitudinal and latera...The precise and accurate control of the friction between tire and road is the foundation of the vehicle driving conditions furthest utilization and vehicle dynamics control. First the tire/road longitudinal and lateral dy- namic friction characteristics is studied in this paper. The lumped, distributed and lumped average dynamic tire models are established; Then the steady-state LuGre dynamic tire model characteristics is studied and was com- pared with magic formulas tire model, etc. to verify the reliability of the model. The results showed that the LuGre model can express tire dynamic friction characteristics. The tire dynamics study in this paper has impor- tant significance for the tire mechanics and automotive electronic control system development.展开更多
Including information of the current road surface conditions can significantly improve the effectiveness of an AEB （automated emergency braking） system to avoid accidents or reduce the injury severity in rear-end cr...Including information of the current road surface conditions can significantly improve the effectiveness of an AEB （automated emergency braking） system to avoid accidents or reduce the injury severity in rear-end crashes. A method to estimate the friction potential based on on-board sensor information is shown in this work. This work expands the scope of existing investigations on whether the accuracy needed for the warning and intervention strategies of AEB can be reached with the proposed method. First, the bandwidth of surface conditions investigated is extended by including low friction surfaces comparable to ice. Second, situations of changing surface conditions and wheel-individual surface conditions were evaluated. Finally, estimation based on different sensor sets was conducted with regard to series application. The investigations are based on measurements performed on a proving ground. The main emphasis was placed on estimation during longitudinal driving conditions. The used sensors include advanced vehicle dynamics measurement equipment as well as standard on-board sensors of the vehicle.展开更多
路径规划是智能汽车的核心技术之一,而在避障场景下的路径规划对智能汽车的行驶安全性与稳定性提出了更为严苛的要求。针对智能汽车避障场景,提出一种双层结构并考虑胎路附着极限的路径规划方法。在上层预规划中设计了一种改进快速搜索...路径规划是智能汽车的核心技术之一,而在避障场景下的路径规划对智能汽车的行驶安全性与稳定性提出了更为严苛的要求。针对智能汽车避障场景,提出一种双层结构并考虑胎路附着极限的路径规划方法。在上层预规划中设计了一种改进快速搜索随机树(Improved Rapidly-exploring Random Tree,IRRT)算法,根据纵向车速自适应调整随机树的扩展步长,以兼顾搜索效率与路径精度;随后针对采样点扩展角度施加胎路附着极限的约束,并在此约束范围内进行随机采样,在预规划层面提升路径的合理性;结合人工势场法,构建采样点引力势场、目标点引力势场以及道路边界斥力势场,从而修正随机采样点的位置,提高搜索效率。为改善路径平滑度并进一步确保车辆稳定性,在下层重规划中利用非线性模型预测控制(Nonlinear Model Predictive Control,NMPC)建立车辆平面运动模型与胎路附着极限约束,对预测时域内的预规划路径进行二次优化,生成重规划路径。最后,基于CarSim/Simulink联合仿真平台以及硬件在环测试平台验证所提算法在静态与动态避障场景下的有效性。研究结果表明,预规划中提出的IRRT算法相较于经典RRT*算法在搜索效率、路径合理性及车辆稳定性方面具有优势。与单层IRRT和传统NMPC方法相比,IRRT-NMPC双层路径规划方法在避障过程中能够在满足实时性要求的同时进一步确保车辆不会超出胎路附着极限,提升了车辆的稳定性和安全性。展开更多
基金supported by National Natural Science Foundation of China(Grant Nos. 51075176, 51105165)
文摘Vehicle state and tire-road adhesion are of great use and importance to vehicle active safety control systems. However, it is always not easy to obtain the information with high accuracy and low expense. Recently, many estimation methods have been put forward to solve such problems, in which Kalman filter becomes one of the most popular techniques. Nevertheless, the use of complicated model always leads to poor real-time estimation while the role of road friction coefficient is often ignored. For the purpose of enhancing the real time performance of the algorithm and pursuing precise estimation of vehicle states, a model-based estimator is proposed to conduct combined estimation of vehicle states and road friction coefficients. The estimator is designed based on a three-DOF vehicle model coupled with the Highway Safety Research Institute（HSRI） tire model; the dual extended Kalman filter （DEKF） technique is employed, which can be regarded as two extended Kalman filters operating and communicating simultaneously. Effectiveness of the estimation is firstly examined by comparing the outputs of the estimator with the responses of the vehicle model in CarSim under three typical road adhesion conditions（high-friction, low-friction, and joint-friction）. On this basis, driving simulator experiments are carried out to further investigate the practical application of the estimator. Numerical results from CarSim and driving simulator both demonstrate that the estimator designed is capable of estimating the vehicle states and road friction coefficient with reasonable accuracy. The DEKF-based estimator proposed provides the essential information for the vehicle active control system with low expense and decent precision, and offers the possibility of real car application in future.
基金Supported by National Hi-tech Research and Development Program of China(863 Program,Grant No.2006AA110101)
文摘The identification of maximum road friction coefficient and optimal slip ratio is crucial to vehicle dynamics and control.However,it is always not easy to identify the maximum road friction coefficient with high robustness and good adaptability to various vehicle operating conditions.The existing investigations on robust identification of maximum road friction coefficient are unsatisfactory.In this paper,an identification approach based on road type recognition is proposed for the robust identification of maximum road friction coefficient and optimal slip ratio.The instantaneous road friction coefficient is estimated through the recursive least square with a forgetting factor method based on the single wheel model,and the estimated road friction coefficient and slip ratio are grouped in a set of samples in a small time interval before the current time,which are updated with time progressing.The current road type is recognized by comparing the samples of the estimated road friction coefficient with the standard road friction coefficient of each typical road,and the minimum statistical error is used as the recognition principle to improve identification robustness.Once the road type is recognized,the maximum road friction coefficient and optimal slip ratio are determined.The numerical simulation tests are conducted on two typical road friction conditions（single-friction and joint-friction）by using CarSim software.The test results show that there is little identification error between the identified maximum road friction coefficient and the pre-set value in CarSim.The proposed identification method has good robustness performance to external disturbances and good adaptability to various vehicle operating conditions and road variations,and the identification results can be used for the adjustment of vehicle active safety control strategies.
基金Supported by Fundamental Research Funds for the Central Universities(Grant No.NS2015015)
文摘The accurate estimation of road friction coeffi- cient in the active safety control system has become increasingly prominent. Most previous studies on road friction estimation have only used vehicle longitudinal or lateral dynamics and often ignored the load transfer, which tends to cause inaccurate of the actual road friction coef- ficient. A novel method considering load transfer of front and rear axles is proposed to estimate road friction coef- ficient based on braking dynamic model of two-wheeled vehicle. Sliding mode control technique is used to build the ideal braking torque controller, which control target is to control the actual wheel slip ratio of front and rear wheels tracking the ideal wheel slip ratio. In order to eliminate the chattering problem of the sliding mode controller, integral switching surface is used to design the sliding mode sur- face. A second order linear extended state observer is designed to observe road friction coefficient based on wheel speed and braking torque of front and rear wheels. The proposed road friction coefficient estimation schemes are evaluated by simulation in ADAMS/Car. The results show that the estimated values can well agree with the actual values in different road conditions. The observer can estimate road friction coefficient exactly in real-time andresist external disturbance. The proposed research provides a novel method to estimate road friction coefficient with strong robustness and more accurate.
基金supported by National Natural Science Foundation of China (Grant No. 50575120)Ministry of Science and Technology of China (Grant No. 20071850519)
文摘Road friction coefficient is a key factor for the stability control of the vehicle dynamics in the critical conditions. Obviously the vehicle dynamics stability control systems, including the anti-lock brake system（ABS）, the traction control system（TCS）, and the active yaw control（AYC） system, need the accurate tire and road friction information. However, the simplified method based on the linear tire and vehicle model could not obtain the accurate road friction coefficient for the complicated maneuver of the vehicle. Because the active braking control mode of AYC is different from that of ABS, the road friction coefficient cannot be estimated only with the dynamics states of the tire. With the related dynamics states measured by the sensors of AYC, a comprehensive strategy of the road friction estimation for the active yaw control is brought forward with the sensor fusion technique. Firstly, the variations of the dynamics characteristics of vehicle and tire, and the stability control mode in the steering process are considered, and then the proper road friction estimation methods are brought forward according to the vehicle maneuver process. In the steering maneuver without braking, the comprehensive road friction from the four wheels may be estimated based on the multi-sensor signal fusion method. The estimated values of the road friction reflect the road friction characteristic. When the active brake involved, the road friction coefficient of the braked wheel may be estimated based on the brake pressure and tire forces, the estimated values reflect the road friction between the braked wheel and the road. So the optimal control of the wheel slip rate may be obtained according to the road friction coefficient. The methods proposed in the paper are integrated into the real time controller of AYC, which is matched onto the test vehicle. The ground tests validate the accuracy of the proposed method under the complicated maneuver conditions.
基金Supported by the National Natural Science Funds for Distinguished Young Scholar of China(Grant No.52025121)National Natural Science Foundation of China(Grant Nos.51975118,52002066).
文摘Many surveys on vehicle traffic safety have shown that the tire road friction coefficient(TRFC)is correlated with the probability of an accident.The probability of road accidents increases sharply on slippery road surfaces.Therefore,accurate knowledge of TRFC contributes to the optimization of driver maneuvers for further improving the safety of intelligent vehicles.A large number of researchers have employed different tools and proposed different algorithms to obtain TRFC.This work investigates these different methods that have been widely utilized to estimate TRFC.These methods are divided into three main categories:off-board sensors-based,vehicle dynamics-based,and data-driven-based methods.This review provides a comparative analysis of these methods and describes their strengths and weaknesses.Moreover,some future research directions regarding TRFC estimation are presented.
基金Partially Supported by Henan Polytechnic University Doctoral Fund(No.B2010-12)+2 种基金Natural Science Fund of Henan Province Education Department(No.2011B580001)Henan Province Key Technology Research Project(No.122102210045)
文摘Road friction coefficient real-time estimation methods is an important issue and problem in automotive active safety con- trol system development. First a fixed feedback gain sliding mode observer of road adhesion coefficient is designed through the es-tablishment of tire/road dynamic friction model in this article. The simulation results shows that the observer can well real-time iden-tify the current road adhesion characteristics. And more importantly, the observer only need wheel speed signal and the braking torque （brake pressure） signal, so the system is low cost, and its adaptability is good. There is no doubt this estimation method has a good application prospect.
基金The National Natural Science Foundation of China(No.61273236)the Natural Science Foundation of Jiangsu Province(No.BK2010239)the Ph.D. Programs Foundation of Ministry of Education of China(No.200802861061)
文摘According to the road adaptive requirements for the vehicle longitudinal safety assistant system an estimation method of the road longitudinal friction coefficient is proposed.The method can simultaneously be applied to both the high and the low slip ratio conditions. Based on the simplified magic formula tire model the road longitudinal friction coefficient is preliminarily estimated by the recursive least squares method.The estimated friction coefficient and the tires model parameters are considered as extended states. The extended Kalman filter algorithm is employed to filter out the noise and adaptively adjust the tire model parameters. Then the final road longitudinal friction coefficient is accurately and robustly estimated. The Carsim simulation results show that the proposed method is better than the conventional algorithm. The road longitudinal friction coefficient can be quickly and accurately estimated under both the high and the low slip ratio conditions.The error is less than 0.1 and the response time is less than 2 s which meets the requirements of the vehicle longitudinal safety assistant system.
基金supported by Henan Polytechnic University Doctoral Fund under Grant No.B2010-12the natural science fund of Henan province education department under Grant No.2011B580001Henan Province Key Technology Research Project under Grant No.122102210045
文摘The precise and accurate control of the friction between tire and road is the foundation of the vehicle driving conditions furthest utilization and vehicle dynamics control. First the tire/road longitudinal and lateral dy- namic friction characteristics is studied in this paper. The lumped, distributed and lumped average dynamic tire models are established; Then the steady-state LuGre dynamic tire model characteristics is studied and was com- pared with magic formulas tire model, etc. to verify the reliability of the model. The results showed that the LuGre model can express tire dynamic friction characteristics. The tire dynamics study in this paper has impor- tant significance for the tire mechanics and automotive electronic control system development.
文摘Including information of the current road surface conditions can significantly improve the effectiveness of an AEB （automated emergency braking） system to avoid accidents or reduce the injury severity in rear-end crashes. A method to estimate the friction potential based on on-board sensor information is shown in this work. This work expands the scope of existing investigations on whether the accuracy needed for the warning and intervention strategies of AEB can be reached with the proposed method. First, the bandwidth of surface conditions investigated is extended by including low friction surfaces comparable to ice. Second, situations of changing surface conditions and wheel-individual surface conditions were evaluated. Finally, estimation based on different sensor sets was conducted with regard to series application. The investigations are based on measurements performed on a proving ground. The main emphasis was placed on estimation during longitudinal driving conditions. The used sensors include advanced vehicle dynamics measurement equipment as well as standard on-board sensors of the vehicle.
文摘路径规划是智能汽车的核心技术之一,而在避障场景下的路径规划对智能汽车的行驶安全性与稳定性提出了更为严苛的要求。针对智能汽车避障场景,提出一种双层结构并考虑胎路附着极限的路径规划方法。在上层预规划中设计了一种改进快速搜索随机树(Improved Rapidly-exploring Random Tree,IRRT)算法,根据纵向车速自适应调整随机树的扩展步长,以兼顾搜索效率与路径精度;随后针对采样点扩展角度施加胎路附着极限的约束,并在此约束范围内进行随机采样,在预规划层面提升路径的合理性;结合人工势场法,构建采样点引力势场、目标点引力势场以及道路边界斥力势场,从而修正随机采样点的位置,提高搜索效率。为改善路径平滑度并进一步确保车辆稳定性,在下层重规划中利用非线性模型预测控制(Nonlinear Model Predictive Control,NMPC)建立车辆平面运动模型与胎路附着极限约束,对预测时域内的预规划路径进行二次优化,生成重规划路径。最后,基于CarSim/Simulink联合仿真平台以及硬件在环测试平台验证所提算法在静态与动态避障场景下的有效性。研究结果表明,预规划中提出的IRRT算法相较于经典RRT*算法在搜索效率、路径合理性及车辆稳定性方面具有优势。与单层IRRT和传统NMPC方法相比,IRRT-NMPC双层路径规划方法在避障过程中能够在满足实时性要求的同时进一步确保车辆不会超出胎路附着极限,提升了车辆的稳定性和安全性。
