Vehicle state estimation for anti-lock control with nonlinear observer

Sun, Fei; Huang, Xuhui; Rudolph, Joachim; Lolenko, Kostyantyn

doi:10.1016/j.conengprac.2015.07.003

Cited by 28 publications

(12 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, slip-based approaches are insufficient for high excitation levels including road roughness in wet conditions. After experimental tests of vehicle state estimation including tire-road friction coefficient, significant inaccuracies of estimation during sharp cornering were detected [16].…”

Section: Introductionmentioning

confidence: 99%

Identification of Road-Surface Type Using Deep Neural Networks for Friction Coefficient Estimation

Šabanovič

Žuraulis

Prentkovskis

et al. 2020

Sensors

View full text Add to dashboard Cite

Nowadays, vehicles have advanced driver-assistance systems which help to improve vehicle safety and save the lives of drivers, passengers and pedestrians. Identification of the road-surface type and condition in real time using a video image sensor, can increase the effectiveness of such systems significantly, especially when adapting it for braking and stability-related solutions. This paper contributes to the development of the new efficient engineering solution aimed at improving vehicle dynamics control via the anti-lock braking system (ABS) by estimating friction coefficient using video data. The experimental research on three different road surface types in dry and wet conditions has been carried out and braking performance was established with a car mathematical model (MM). Testing of a deep neural networks (DNN)-based road-surface and conditions classification algorithm revealed that this is the most promising approach for this task. The research has shown that the proposed solution increases the performance of ABS with a rule-based control strategy.

show abstract

Section: Introductionmentioning

confidence: 99%

Identification of Road-Surface Type Using Deep Neural Networks for Friction Coefficient Estimation

Šabanovič

Žuraulis

Prentkovskis

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…Scenario 3: because the driver had blind spots in vision, it is possible for the right-turning vehicle to collide with pedestrians crossing the road. e parameters in this scenario were defined as follows: pt � 0.01 s, T � 5 s, L 1 � 8 m, W 1 � 2 m, and D � 1.5 m; the pedestrian's coordinates were (3,9). V 1 , V 2 , and R consist of 10,000 normally distributed random numbers, N (20, 1); V 2 represented the velocity of the pedestrian in this case.…”

Section: Simulation Results Analysis Of Four Modesmentioning

confidence: 99%

“…e collision between the right-turning vehicle and the pedestrians was judged by the pedestrian's safety profile condition, when the condition met the scenario 2 or scenario 3, which was given in the same way as equation (9). e probability was accumulated according to the pedestrian's safety profile condition; the pedestrian's safety profile condition was shown in…”

Section: Right-turning Vehicle Collision Probability Calculation Algomentioning

confidence: 99%

“…At present, most previous studies focused on the forward collision warning (FCW) system and active collision avoidance [7]; however, there was a lack of research on the collisions caused by right-turning vehicles. Some researchers estimated the motion state of vehicles and pedestrians [8,9]. By establishing a probability model, Hashimoto et al predicted and identified a pedestrian's crossing decision in advance [10]; however, this research lacked the calculation of collision probability for a vehicle on a right-turning course.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Study on a Right-Turning Intelligent Vehicle Collision Warning and Avoidance Algorithm Based on Monte Carlo Simulation

Shen

Shan

Gao

et al. 2020

Journal of Advanced Transportation

View full text Add to dashboard Cite

With the development of intelligent vehicle technology, the demand for advanced driver assistant systems kept increasing. To improve the performance of the active safety systems, we focused on right-turning vehicle’s collision warning and avoidance. We put forward an algorithm based on Monte Carlo simulation to calculate the collision probability between the right-turning vehicle and another vehicle (or pedestrian) in intersections. We drew collision probability curves which used time-to-collision as the horizontal axis and collision probability as the vertical axis. We established a three-level collision warning system and used software to calculate and simulate the collision probability and warning process. To avoid the collision actively when turning right, a two-stage braking strategy is applied. Taking four right-turning collision conditions as examples, the two-stage braking strategy was applied, analysing and comparing the anteroposterior curve diagram simultaneously to avoid collision actively and reduce collision probability. By comparison, the collision probability 2 s before active collision avoidance was more than 80% and the collision probability may even reach 100% in certain conditions. To improve the active safety performance, the two-stage braking strategy can reduce the collision probability from exceeding 50% to approaching 0% in 2 s and reduce collision probability to less than 5% in 3 s. By changing four initial positions, the collision probability curve calculation algorithm and the two-stage braking strategy are validated and analysed. The results verified the rationality of the collision probability curve calculation algorithm and the two-stage braking strategy.

show abstract

“…In order to directly deal with nonlinear problems in vehicle dynamics state estimation, concerns about nonlinear observer (NLO) tend to increase, and sufficient conditions for this type of observer are derived by making use of different stability theories [111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128]. Experimental tests have shown that the estimated performance of the NLO is generally better than that of the EKF [111,113,115].…”

Section: Model-based Vehicle State Estimationmentioning

confidence: 99%

Advanced Estimation Techniques for Vehicle System Dynamic State: A Survey

Jin

Yin

Chen

2019

Sensors

View full text Add to dashboard Cite

In order to improve handling stability performance and active safety of a ground vehicle, a large number of advanced vehicle dynamics control systems—such as the direct yaw control system and active front steering system, and in particular the advanced driver assistance systems—towards connected and automated driving vehicles have recently been developed and applied. However, the practical effects and potential performance of vehicle active safety dynamics control systems heavily depend on real-time knowledge of fundamental vehicle state information, which is difficult to measure directly in a standard car because of both technical and economic reasons. This paper presents a comprehensive technical survey of the development and recent research advances in vehicle system dynamic state estimation. Different aspects of estimation strategies and methodologies in recent literature are classified into two main categories—the model-based estimation approach and the data-driven-based estimation approach. Each category is further divided into several sub-categories from the perspectives of estimation-oriented vehicle models, estimations, sensor configurations, and involved estimation techniques. The principal features of the most popular methodologies are summarized, and the pros and cons of these methodologies are also highlighted and discussed. Finally, future research directions in this field are provided.

show abstract

Vehicle state estimation for anti-lock control with nonlinear observer

Cited by 28 publications

References 28 publications

Identification of Road-Surface Type Using Deep Neural Networks for Friction Coefficient Estimation

Identification of Road-Surface Type Using Deep Neural Networks for Friction Coefficient Estimation

Study on a Right-Turning Intelligent Vehicle Collision Warning and Avoidance Algorithm Based on Monte Carlo Simulation

Advanced Estimation Techniques for Vehicle System Dynamic State: A Survey

Contact Info

Product

Resources

About