scholarly journals Accurate and Efficient Driving Intention Inference Based on Traffic Environment Information and FES-XGB Framework

2022 ◽  
Vol 30 (0) ◽  
pp. 30-41
Author(s):  
Shuo Wang ◽  
Hideki Fujii ◽  
Shinobu Yoshimura
Keyword(s):  
Traffic Environment ◽  
Intention Inference

Design Intention Inference for Virtual Co-Design Agents

2020 ◽  
Author(s):  
Matthew V. Law ◽  
Amritansh Kwatra ◽  
Nikhil Dhawan ◽  
Matthew Einhorn ◽  
Amit Rajesh ◽  
...  
Keyword(s):  
Intention Inference

Ergonomics analysis based on intention inference

2021 ◽  
pp. 1-16
Author(s):  
First A. Wenbo Huang ◽  
Second B. Changyuan Wang ◽  
Third C. Hongbo Jia
Keyword(s):  
Eye Movement ◽  
Recognition Rate ◽  
Tactile Feedback ◽  
Movement Direction ◽  
Support Vector ◽  
Eeg Signals ◽  
Intention Recognition ◽  
Baseline Removal ◽  
Inference Methods ◽  
Intention Inference

Traditional intention inference methods rely solely on EEG, eye movement or tactile feedback, and the recognition rate is low. To improve the accuracy of a pilot’s intention recognition, a human-computer interaction intention inference method is proposed in this paper with the fusion of EEG, eye movement and tactile feedback. Firstly, EEG signals are collected near the frontal lobe of the human brain to extract features, which includes eight channels, i.e., AF7, F7, FT7, T7, AF8, F8, FT8, and T8. Secondly, the signal datas are preprocessed by baseline removal, normalization, and least-squares noise reduction. Thirdly, the support vector machine (SVM) is applied to carry out multiple binary classifications of the eye movement direction. Finally, the 8-direction recognition of the eye movement direction is realized through data fusion. Experimental results have shown that the accuracy of classification with the proposed method can reach 75.77%, 76.7%, 83.38%, 83.64%, 60.49%,60.93%, 66.03% and 64.49%, respectively. Compared with traditional methods, the classification accuracy and the realization process of the proposed algorithm are higher and simpler. The feasibility and effectiveness of EEG signals are further verified to identify eye movement directions for intention recognition.

Multi-ship encounter situation adaptive understanding by individual navigation intention inference

2021 ◽  
Vol 237 ◽  
pp. 109612
Author(s):  
Shaobo Wang ◽  
Yingjun Zhang ◽  
Yisong Zheng
Keyword(s):  
Intention Inference

MODEL OF ACTIVITY OF A CANDIDATE FOR DRIVERS IN THE SYSTEM «CADET-CAR-ROAD-TRAFFIC ENVIRONMENT»

2021 ◽  
Vol 75 (4) ◽  
pp. 98-104
Author(s):  
Ageev Еvgeny Viktorovich ◽  
◽  
Novikov Alexander Nikolaevich ◽  
Vinogradov Еvgeny Sergeevich ◽  
◽  
...  
Keyword(s):  
Professional Training ◽  
Road Traffic ◽  
Direct Impact ◽  
Behavior Skills ◽  
Traffic Environment ◽  
Safe Behavior ◽  
Main Factors ◽  
Operator Tasks

Presents a model of the activity of a candidate for drivers in the system «Cadet-Car-Road-Traffic environment». The main factors that have a direct impact on the formation of safe behavior skills while driving a vehicle at the stage of professional training of driving personnel are identified, which are: the probability of trouble-free, error-free, and timely performance of operator tasks.

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