Appropriate Sensor Placement for Fault-Tolerant Lane-Keeping Control of Automated Vehicles

Suryanarayanan, Shashikanth; Tomizuka, Masayoshi

doi:10.1109/tmech.2007.901943

Cited by 26 publications

(14 citation statements)

References 13 publications

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“…The core idea of MTTE is predicting the maximum torque, which tire/road interface is able to transfer to chassis, through estimating the adhesive force ˆd F and regulating the relaxation factor α governed by (9), (10). The schematic presentation of MTTE is shown in Fig.…”

Section: A Mttementioning

confidence: 99%

“…The key to traction control is antiskid control when the vehicle is driven on a slippery road. Auxiliary sensors, including acceleration sensor and chassis velocity sensor [8], [9], can be equipped to achieve more information of vehicle. The information can help TCS making use of slip ratio in nonlinear tire model to get the maximum adhesive force from tire-road surface.…”

Section: Introductionmentioning

confidence: 99%

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Traction Control System for EV Based on Modified Maximum Transmissible Torque Estimation

Song

et al. 2013

2013 IEEE Vehicle Power and Propulsion Conference (VPPC)

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Traction control system (TCS) of electric vehicles (EVs) without detecting vehicle velocity is drawing more and more attention. The most challenging problem in TCS is controlling the drive wheel with indirect control inputs. The maximum transmissible torque estimation (MTTE) method which is effective in terms of antiskid control has been proposed, however, this method is conservative because it cannot take sufficient advantage of adhesive force under constant torque condition even if the road is slippery. Therefore, a modified maximum transmissible torque estimation (M-MTTE) algorithm with a various relaxation factor is proposed in this paper. By analyzing and comparing the simulation results with prior control method, it is validated that M-MTTE is robust on antiskid control and achieves a better acceleration control effect. Keywords-electric vehicle (EV); traction control system (TCS); antiskid control; modified maximum transimissible torque estimation (M-MTTE)I.

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Section: A Mttementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Traction Control System for EV Based on Modified Maximum Transmissible Torque Estimation

Song

et al. 2013

2013 IEEE Vehicle Power and Propulsion Conference (VPPC)

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“…A vision-based measurement implementation discussed in reference [18] has been demonstrated to achieve the same accuracy (i.e. cm-level) under the condition of near view as the magnetometer on the highway used in references [1], [3], and [14]. Compared with the magnetometer measurement, the vision-based measurement does not need to pave hardware devices on the current highways and therefore has lower infrastructure cost.…”

Section: Measurement Model In the Lf2mentioning

confidence: 99%

Reliable integrated navigation system based on adaptive fuzzy federated Kalman filter for automated vehicles

Zhang

2009

Proceedings of the Institution of Mechanical Engineers, Part D:

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There is an increasing interest worldwide in developing automated vehicles on the highway. From the viewpoint of safe driving, their navigation system should be accurate, robust, and reliable. This paper presents a fault-tolerant navigation approach for automated vehicles based on multi-sensor integration utilizing a proposed adaptive fuzzy federated Kalman filter (AF-FKF). The FKF model is first discussed in detail, which fuses multiple and redundant sensors incorporating strapdown inertial navigation system, carrier phasedifferential global positioning system, electronic compass, machine vision, and digital map. The adaptive fuzzy FKF algorithm is then proposed to adjust the FKF information-sharing factors adaptively, detect, and isolate the faulty sensor in the FKF effectively. In order to compare the fault-tolerant performance, several traditional navigation methods are also considered. Simulation results demonstrate that the proposed integrated navigation approach can detect, isolate, and accommodate different types of sensor failures including hard failures and soft failures. The proposed approach can adapt to highly reliable navigation requirements for automated vehicles in complex situations.

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“…In general traction control systems that need the vehicle longitudinal speed, the nondriven wheels, which are not connected to the drive machine, serve as an approximation of the speed, but it is not applicable when the vehicle is accelerated by four wheel driving system or decelerated by brakes equipped in these wheels (Hori et al, 1998;Yin et al, 2009). Other dedicated sensors, e.g., acoustic sensors (Cevher et al, 2009), optical sensors (Turner and Austin, 2000), sensors of magnetic markers (Hyeongcheol and Tomizuka, 2003;Suryanarayanan and Tomizuka, 2007), etc., which can obtain the chassis velocity, are too sensitive and reliant on the driving environment or too expensive to be applied in actual vehicles. The vehicle velocity can also be estimated in commercial products, like Electronic Stability Program (ESP) system (Zanten, 2000), in a much more complicated way inferred from other measurements, such as wheel speed, yaw rate and acceleration measurements.…”

Section: Introductionmentioning

confidence: 99%

Novel estimation of tyre-road friction coefficient and slip ratio using electrical parameters of traction motor for electric vehicles

Liu

2013

IJVAS

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The estimation of the friction coeffi cient and the slip ratio is crucial for advanced traction control or anti-brake control of electric vehicles. In this paper, dynamic behaviours of electrical parameters of the traction motor under road change are modelled and analysed. Novel estimation only using the measurements of the armature voltage and the current is proposed. The proposed method is much quicker than traditional methods, contributing to adjust the vehicle's motion state more quickly and precisely. Further, it can eliminate the speed measuring devices of the wheel speed and the vehicle speed. Simulations verify the effectiveness.

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Appropriate Sensor Placement for Fault-Tolerant Lane-Keeping Control of Automated Vehicles

Cited by 26 publications

References 13 publications

Traction Control System for EV Based on Modified Maximum Transmissible Torque Estimation

Traction Control System for EV Based on Modified Maximum Transmissible Torque Estimation

Reliable integrated navigation system based on adaptive fuzzy federated Kalman filter for automated vehicles

Novel estimation of tyre-road friction coefficient and slip ratio using electrical parameters of traction motor for electric vehicles

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