Vehicle dynamic state estimation: state of the art schemes and perspectives

Guo, Hongyan; Cao, Dongpu; Chen, Hong; Lv, Chen; Wang, Huaji; Yang, S.

doi:10.1109/jas.2017.7510811

Cited by 134 publications

(66 citation statements)

References 82 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As can be seen from Fig. 12, state-estimation of feedback variables are important for VMC and a comprehensive review of state estimation can be found in [9]. Tire/road friction estimation has attracted particular attention and an extensive review on this model based friction and estimation without tire model is available in [104].…”

Section: Vehicle Motion State Estimation (Vmse)mentioning

confidence: 99%

“…Automated driving on public roads will require a redesign of the vehicle to meet new requirements on functional safety and fault tolerance. Other industries, such as nuclear power plants, aviation, elevators, robots [9] have long dealt with these requirements, but often without the cost pressure present in the automotive industry. Triple (or higher) redundancy is usually applied to allow for being tolerant to multiple faults.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Trends in vehicle motion control for automated driving on public roads

Klomp

Jonasson

Laine

et al. 2019

Vehicle System Dynamics

View full text Add to dashboard Cite

In this paper we describe how vehicle systems and the vehicle motion control are affected by automated driving on public roads. We describe the redundancy needed for a road vehicle to meet certain safety goals. The concept of system safety as well as system solutions to fault tolerant actuation of steering and braking and the associated fault tolerant power supply is described. Notably restriction of the operational domain in case of reduced capability of the driving automation system is discussed. Further we consider path tracking, state estimation of vehicle motion control required for automated driving as well as an example of a minimum risk maneuver and redundant steering by means of differential braking. The steering by differential braking could offer heterogeneous or dissimilar redundancy that complements the redundancy of described fault tolerant steering systems for driving automation equipped vehicles. Finally the important topic of verification of driving automation systems is addressed.

show abstract

Section: Vehicle Motion State Estimation (Vmse)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Trends in vehicle motion control for automated driving on public roads

Klomp

Jonasson

Laine

et al. 2019

Vehicle System Dynamics

View full text Add to dashboard Cite

show abstract

“…This type of advanced GPS equipment cannot be included in mass production vehicles due to high-cost issues [4]. Because of that, many of the researchers focus on estimating these angles based on available measurements from on-board embedded sensors but independently [5,6]. On the other hand, in works such as [7], both roll and sideslip angles are estimated by using additional external sensors that need to be integrated into the vehicle instead of using the own on-board systems.…”

Section: Introductionmentioning

confidence: 99%

Simultaneous Estimation of Vehicle Roll and Sideslip Angles through a Deep Learning Approach

Prieto-González

Sánchez

García-Guzmán

et al. 2020

Sensors

View full text Add to dashboard Cite

Presently, autonomous vehicles are on the rise and are expected to be on the roads in the coming years. In this sense, it becomes necessary to have adequate knowledge about its states to design controllers capable of providing adequate performance in all driving scenarios. Sideslip and roll angles are critical parameters in vehicular lateral stability. The later has a high impact on vehicles with an elevated center of gravity, such as trucks, buses, and industrial vehicles, among others, as they are prone to rollover. Due to the high cost of the current sensors used to measure these angles directly, much of the research is focused on estimating them. One of the drawbacks is that vehicles are strong non-linear systems that require specific methods able to tackle this feature. The evolution in Artificial Intelligence models, such as the complex Artificial Neural Network architectures that compose the Deep Learning paradigm, has shown to provide excellent performance for complex and non-linear control problems. In this paper, the authors propose an inexpensive but powerful model based on Deep Learning to estimate the roll and sideslip angles simultaneously in mass production vehicles. The model uses input signals which can be obtained directly from onboard vehicle sensors such as the longitudinal and lateral accelerations, steering angle and roll and yaw rates. The model was trained using hundreds of thousands of data provided by Trucksim® and validated using data captured from real driving maneuvers using a calibrated ground truth device such as VBOX3i dual-antenna GPS from Racelogic®. The use of both Trucksim® software and the VBOX measuring equipment is recognized and widely used in the automotive sector, providing robust data for the research shown in this article.

show abstract

“…However, in the acquisition and transmission process of millimeter wave radar signals, due to the interference of the external environment or the failure of radar devices, the measured value of the dynamics state of the target vehicle will inevitably contain certain measurement noises, and the fault conditions such as missing detection and false detection may occur [8]. In this case, the accuracy of vehicle-borne millimeter wave radar is relatively low and does not satisfy the requirements of vehicle active safety control system [9]. Even a very short failure may cause a driving accident.…”

Section: Introductionmentioning

confidence: 99%

Active Fault Tolerant Control of Adaptive Cruise Control System Considering Vehicle-Borne Millimeter Wave Radar Sensor Failure

2020

View full text Add to dashboard Cite

Vehicle-borne millimeter wave radar sensor may malfunction during signal acquisition and transmission process, which will affect the decision of adaptive cruise control (ACC) system and the safe driving of vehicles. However, adding other redundant environment-aware devices will increases costs. Therefore, in this paper, an active fault tolerant control of ACC system considering vehicle-borne millimeter wave radar sensor failure is proposed. Sensor faults are taken as discrete events, and the mixed logical dynamical (MLD) model of ACC upper control system is built which includes both the fault-free dynamics and the fault dynamics of the system. Then, the active fault tolerant control model of ACC system is established based on model predictive control (MPC) framework. Compared with the existing researches, this work emphasizes on the active fault tolerant control without adding other redundant environment-aware devices, which has not been fully revealed by the existing researches and is important to the industrial application. Moreover, the active fault tolerant control method can be easily ported to other driver assistance system besides ACC. The simulation results show that the vehicle equipped with the active fault tolerant ACC system can still drive safely and smoothly without being affected by radar sensor failures, which demonstrates its great significance to improve vehicle's own intelligence and ensure the safe driving instead of relying entirely on sensors. INDEX TERMS Active fault tolerant, adaptive cruise control, intelligent vehicles, mixed logical dynamical model, model predictive control.

show abstract

Vehicle dynamic state estimation: state of the art schemes and perspectives

Cited by 134 publications

References 82 publications

Trends in vehicle motion control for automated driving on public roads

Trends in vehicle motion control for automated driving on public roads

Simultaneous Estimation of Vehicle Roll and Sideslip Angles through a Deep Learning Approach

Active Fault Tolerant Control of Adaptive Cruise Control System Considering Vehicle-Borne Millimeter Wave Radar Sensor Failure

Contact Info

Product

Resources

About