A machine learning approach for personalized autonomous lane change initiation and control

2017 ◽  
Author(s):  
Charlott Vallon ◽  
Ziya Ercan ◽  
Ashwin Carvalho ◽  
Francesco Borrelli
Keyword(s):  
Machine Learning ◽  
Learning Approach ◽  
Lane Change ◽  
Machine Learning Approach ◽  
And Control

scholarly journals Control Oriented Prediction of Driver Brake Intention and Intensity Using a Composite Machine Learning Approach

Energies ◽  
2019 ◽  
Vol 12 (13) ◽  
pp. 2483 ◽  
Author(s):  
Jianhao ◽  
Jing ◽  
Longqiang ◽  
Yi ◽  
Hanzhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Clustering Algorithm ◽  
External Input ◽  
Feature Subset Selection ◽  
Learning Approach ◽  
Area Network ◽  
Feature Subset ◽  
Sensor Failure ◽  
Machine Learning Approach ◽  
And Control

Driver perception, decision, and control behaviors are easily affected by traffic conditions and driving style, showing the tendency of randomness and personalization. Brake intention and intensity are integrated and control-oriented parameters that are crucial to the development of an intelligent braking system. In this paper, a composite machine learning approach was proposed to predict driver brake intention and intensity with a proper prediction horizon. Various driving data were collected from Controller Area Network (CAN) bus under a real driving condition, which mainly contained urban and rural road types. ReliefF and RReliefF (they don’t have abbreviations) algorithms were employed as feature subset selection methods and applied in a prepossessing step before the training. The rank importance of selected predictors exhibited different trends or even negative trends when predicting brake intention and intensity. A soft clustering algorithm, Fuzzy C-means, was adopted to label the brake intention into categories, namely slight, medium, intensive, and emergency braking. Data sets with misplaced labels were used for training of an ensemble machine learning method, random forest. It was validated that brake intention could be accurately predicted 0.5 s ahead. An open-loop nonlinear autoregressive with external input (NARX) network was capable of learning the long-term dependencies in comparison to the static neural network and was suggested for online recognition and prediction of brake intensity 1 s in advance. As system redundancy and fault tolerance, a close-loop NARX network could be adopted for brake intensity prediction in the case of possible sensor failure and loss of CAN message.

A Machine Learning Approach to Identify a Circulating MicroRNA Signature for Alzheimer Disease

2019 ◽  
Vol 5 (1) ◽  
pp. 15-28 ◽  
Author(s):  
Xuemei Zhao ◽  
John Kang ◽  
Vladimir Svetnik ◽  
Donald Warden ◽  
Gordon Wilcock ◽  
...  
Keyword(s):  
Machine Learning ◽  
Alzheimer Disease ◽  
Pcr Analysis ◽  
Circulating Microrna ◽  
Learning Approach ◽  
Mirna Signature ◽  
Serum Samples ◽  
Less Invasive ◽  
Machine Learning Approach ◽  
And Control

Abstract Background Accurate diagnosis of Alzheimer disease (AD) involving less invasive molecular procedures and at reasonable cost is an unmet medical need. We identified a serum miRNA signature for AD that is less invasive than a measure in cerebrospinal fluid. Methods From the Oxford Project to Investigate Memory and Aging (OPTIMA) study, 96 serum samples were profiled by a multiplex (>500 analytes) microRNA (miRNA) reverse transcription quantitative PCR analysis, including 51 controls, 32 samples from patients with AD, and 13 samples from patients with mild cognitive impairment (MCI). Clinical diagnosis of a subset of AD and the controls was confirmed by postmortem (PM) histologic examination of brain tissue. In a machine learning approach, the AD and control samples were split 70:30 as the training and test cohorts. A multivariate random forest statistical analysis was applied to construct and test a miRNA signature for AD identification. In addition, the MCI participants were included in the test cohort to assess whether the signature can identify early AD patients. Results A 12-miRNA signature for AD identification was constructed in the training cohort, demonstrating 76.0% accuracy in the independent test cohort with 90.0% sensitivity and 66.7% specificity. The signature, however, was not able to identify MCI participants. With a subset of AD and control participants with PM-confirmed diagnosis status, a separate 12-miRNA signature was constructed. Although sample size was limited, the PM-confirmed signature demonstrated improved accuracy of 85.7%, largely owing to improved specificity of 80.0% with comparable sensitivity of 88.9%. Conclusion Although additional and more diverse cohorts are needed for further clinical validation of the robustness, the miRNA signature appears to be a promising blood test to diagnose AD.

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