Fuzzy based collision avoidance control strategy considering crisis index in low speed urban area

Li, Xiaohan; Shaobin, Wu; Li, Fengjiao

doi:10.1109/itec-ap.2014.6940859

Cited by 5 publications

(2 citation statements)

References 6 publications

(2 reference statements)

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“…Although their work seems to fully consider the internal and external factors resulting in a collision involving the driver's characteristics, weather conditions, road situations, and vehicle's characteristics, the thresholds they used for linguistic variable definition were a little arbitrary and were not given detailed explanations, which make their work applicable only under a specific condition. In [17], a fuzzy-based active control strategy to keep the vehicle away from possible collisions has been proposed by evaluating the crisis index of urban driving conditions. Because the crisis index or risk level is hard to calculate using mathematical formulas, the fuzzy logic controller is introduced here to generate the final risk evaluation index according to the input relative distance between the trailing and leading vehicles, the trailing vehicle speed, and the time to collision.…”

Section: Genetic Algorithmmentioning

confidence: 99%

A genetic algorithm‐optimized fuzzy logic controller to avoid rear‐end collisions

Chen

Sui³

et al. 2016

J. Adv. Transp.

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In this paper, a rear-end collision control model is proposed using the fuzzy logic control scheme. Through detailed analysis of car-following cases, our fuzzy control system is established with reasonable control rules. Furthermore, a genetic algorithm is introduced into the fuzzy rules refining process to reduce the computational complexity while maintaining accuracy. Numerical results indicate that our genetic algorithm-optimized fuzzy logic controller outperforms the traditional fuzzy logic controller in terms of better safety guarantee and higher traffic efficiency.

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Section: Genetic Algorithmmentioning

confidence: 99%

A genetic algorithm‐optimized fuzzy logic controller to avoid rear‐end collisions

Chen

Sui³

et al. 2016

J. Adv. Transp.

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show abstract

“…The former is mainly involved in the inter-distance control on the road where the automoble drives at a constant speed, whereas the latter deals with vehicle commuting in municipal areas with frequent stops, decelerations, and accelerations [ 29 ]. In [ 30 ], Li proposed an active control strategy to keep vehicles away from possible collisions owing to distracted driving or drivers’ attention. In [ 31 ], the literature examines drivers’ adaptation using a conceptual model of adaptive behavior.…”

Section: Introductionmentioning

confidence: 99%

A Vehicle Active Safety Model: Vehicle Speed Control Based on Driver Vigilance Detection Using Wearable EEG and Sparse Representation

Zhang

Luo

Rasim

et al. 2016

Sensors

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In this paper, we present a vehicle active safety model for vehicle speed control based on driver vigilance detection using low-cost, comfortable, wearable electroencephalographic (EEG) sensors and sparse representation. The proposed system consists of three main steps, namely wireless wearable EEG collection, driver vigilance detection, and vehicle speed control strategy. First of all, a homemade low-cost comfortable wearable brain-computer interface (BCI) system with eight channels is designed for collecting the driver’s EEG signal. Second, wavelet de-noising and down-sample algorithms are utilized to enhance the quality of EEG data, and Fast Fourier Transformation (FFT) is adopted to extract the EEG power spectrum density (PSD). In this step, sparse representation classification combined with k-singular value decomposition (KSVD) is firstly introduced in PSD to estimate the driver’s vigilance level . Finally, a novel safety strategy of vehicle speed control, which controls the electronic throttle opening and automatic braking after driver fatigue detection using the above method, is presented to avoid serious collisions and traffic accidents. The simulation and practical testing results demonstrate the feasibility of the vehicle active safety model.

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Fuzzy based collision avoidance control strategy considering crisis index in low speed urban area

Cited by 5 publications

References 6 publications

A genetic algorithm‐optimized fuzzy logic controller to avoid rear‐end collisions

A genetic algorithm‐optimized fuzzy logic controller to avoid rear‐end collisions

A Vehicle Active Safety Model: Vehicle Speed Control Based on Driver Vigilance Detection Using Wearable EEG and Sparse Representation

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