ABS braking on rough terrain

Merwe, Nico A. van der; Schalk, P.; Žuraulis, Vidas

doi:10.1016/j.jterra.2018.10.003

Cited by 13 publications

(10 citation statements)

References 28 publications

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“…The algorithm presented in [39] was used as a reference. The extensions of this algorithm in different vehicle dynamics cases are used by other researchers as well [40].…”

Section: Introductionmentioning

confidence: 99%

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

Šabanovič

Žuraulis

Prentkovskis

et al. 2020

Sensors

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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.

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“…The algorithm presented in [39] was used as a reference. The extensions of this algorithm in different vehicle dynamics cases are used by other researchers as well [40].…”

Section: Introductionmentioning

confidence: 99%

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

Šabanovič

Žuraulis

Prentkovskis

et al. 2020

Sensors

Self Cite

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“…Dictated by two upper and one lower threshold for the angular acceleration and an upper longitudinal slip threshold, the Bosch algorithm combines these three pressure control modes in various configurations resulting in a repetitive ABS control cycle consisting of eight phases. Studies [10,11] have shown that by fine tuning the parameters of the Bosch algorithm, reasonable results can be obtained when braking over rough terrain, thus it is a suitable algorithm to be used as a baseline.…”

Section: Bosch Algorithmmentioning

confidence: 99%

Model-free Intelligent Control for Antilock Braking Systems on Rough Terrain

2021

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Advancements have been made in the field of vehicle dynamics, improving the handling and safety of the vehicle through control systems such as the Antilock Braking System (ABS). An ABS enhances the braking performance and steerability of a vehicle under severe braking conditions by preventing wheel lockup. However, its performance degrades on rough terrain resulting in an increased wheel lockup and stopping distance compared to without. This is largely as a result of noisy measurements, and un-modelled dynamics that occur as a result of the vertical and torsional excitation experienced over rough terrain. Therefore, it is proposed that a model-free intelligent technique, which may adapt to these dynamics, be used to overcome this problem. The Double Deep Q-learning (DDQN) technique in conjunction with a Temporal Convolutional Network (TCN) is proposed as the intelligent control algorithm, and straight line braking simulations are performed using a single tyre model, with tyre characteristics approximated by the LuGre tyre model. The rough terrain is modelled after the measured Belgian paving with the normal forces at the tyre contact patch approximated using FTire in ADAMS. Comparisons are drawn against the Bosch algorithm, and results show that the intelligent control approach achieves lateral stability by preventing wheel lockup whilst braking over rough terrain, without deteriorating the stopping distance.

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“…Nevertheless, the most important task is accurate enough identification or the road conditions as soon as possible (Zheng et al 2011); however, in the case of adaptation according to the parameters of a system operating in real time, a delay takes place already, because the system will adapt itself only after a certain time of operation upon specific road conditions. An assessment of the road irregularities and ABS control adaptation are also important in cases of emergency braking on specific pavements (Ivanov et al 2015;Van der Merwe et al 2018;Žuraulis et al 2018). The other group of active vehicle braking systems includes Adaptive Cruise Control (ACC) and Autonomous Emergency Braking (AEB) systems.…”

Section: System Real-time Adaptationmentioning

confidence: 99%

Technological Measures of Forefront Road Identification for Vehicle Comfort and Safety Improvement

2019

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This paper presents the technological measures currently being developed at institutes and vehicle research centres dealing with forefront road identification. In this case, road identification corresponds with the surface irregularities and road surface type, which are evaluated by laser scanning and image analysis. Real-time adaptation, adaptation in advance and system external informing are stated as sequential generations of vehicle suspension and active braking systems where road identification is significantly important. Active and semi-active suspensions with their adaptation technologies for comfort and road holding characteristics are analysed. Also, an active braking system such as Anti-lock Braking System (ABS) and Autonomous Emergency Braking (AEB) have been considered as very sensitive to the road friction state. Artificial intelligence methods of deep learning have been presented as a promising image analysis method for classification of 12 different road surface types. Concluding the achieved benefit of road identification for traffic safety improvement is presented with reference to analysed research reports and assumptions made after the initial evaluation.

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ABS braking on rough terrain

Cited by 13 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

Model-free Intelligent Control for Antilock Braking Systems on Rough Terrain

Technological Measures of Forefront Road Identification for Vehicle Comfort and Safety Improvement

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