Studying Battery Range and Range Anxiety for Electric Vehicles based on Real Travel Demands

2021 ◽  
Vol 65 (1) ◽  
pp. 332-336
Author(s):  
Zhengming Zhang ◽  
Renran Tian
Keyword(s):  
Large Scale ◽  
Electric Cars ◽  
Battery Design ◽  
Midwestern Usa ◽  
Range Anxiety ◽  
Naturalistic Driving Study ◽  
Naturalistic Driving ◽  
Active Research ◽  
Do So

Determination of appropriate battery ranges is critical for developing and utilizing electric cars, which remains an active research topic. In particular, the issues of range anxiety have not been well studied concerning the battery design. Towards these research gaps, this study firstly investigates the baseline battery ranges based on the actual travel data collected from a large-scale longitudinal naturalistic driving study in the Midwestern USA. The occurrences and severity levels of range anxiety are then studied given the baseline, which leads to an augmented optimization model to eliminate such issues. Results show that in the baseline model, 60% of drivers can replace their gas cars entirely with 400-mile battery ranges, and less than 40% can do so with 200-mile battery ranges. Even when all the travel needs are satisfied, the optimal battery ranges can still cause range anxiety issues for all the drivers. An additional 25 miles of battery range can help solve the problem based on the improved optimization results.

Naturalistic Data Collection of Driver Performance in Familiar and Unfamiliar Vehicles

2005 ◽  
Vol 49 (22) ◽  
pp. 1994-1998
Author(s):  
Suzanne E. Lee ◽  
Thomas A. Dingus ◽  
Sheila G. Klauer ◽  
Vicki L. Neale ◽  
Jeremy Sudweeks
Keyword(s):  
Data Collection ◽  
Large Scale ◽  
Data Set ◽  
Driver Performance ◽  
Final Data ◽  
Naturalistic Driving Study ◽  
Naturalistic Driving ◽  
Instrumented Vehicles ◽  
Instrumented Vehicle ◽  
Privately Owned

The 100-Car Naturalistic Driving Study was the first large-scale instrumented vehicle study with no special driver instructions, unobtrusive data collection instrumentation, and no in-vehicle experimenter. The final data set includes approximately 2,000,000 vehicle miles, almost 43,000 hours of data, 241 primary and secondary drivers, 12 to 13 months of data collection for each vehicle, and data from a highly capable instrumentation system. In addition, 78 of 102 vehicles were privately owned and 22 were leased. After 12 months, leased vehicles were provided to 22 private vehicle drivers who then drove the leased vehicles for an additional four weeks. Driving performance for the same drivers in familiar and unfamiliar instrumented vehicles was then compared. Results provided evidence of increased relative risk for the same driver for weeks 1 through 4 of driving an unfamiliar leased vehicle as compared to the same period of driving their privately owned vehicle.

Effectiveness of Using SHRP2 Naturalistic Driving Study Data to Analyze Driver Behavior at Highway-Rail Grade Crossings

2017 ◽  
Author(s):  
Aaron Dean ◽  
Pasi Lautala ◽  
David Nelson
Keyword(s):  
Machine Vision ◽  
Large Scale ◽  
Driver Behavior ◽  
Test Sample ◽  
Head Tracking ◽  
Tracking Data ◽  
Data Set ◽  
Preliminary Results ◽  
Naturalistic Driving Study ◽  
Naturalistic Driving

Highway-rail grade crossing (crossing) collisions and fatalities have been in decline, but a recent ‘plateau’ has caused the Federal Railroad Administration (FRA) to concentrate on decreasing further casualties. The Michigan Tech Rail Transportation Program has been selected to perform a large-scale study that will utilize the SHRP2 Naturalistic Driving Study (NDS) data to analyze how various crossing warning devices affect driver behavior and whether there are clear differences between the effectiveness of the warning devices. The main results of this study are the development of a coding scheme for a visual narrative, used to validate machine vision head tracking data, and an improved baseline for the head tracking data using bivariate probability density. Head tracking data from the NDS and its correlation with coded narratives are vital to analyze driver behavior as they traverse crossings. This paper also presents preliminary results for the comparative analysis of the head tracking data from an initial test sample. Future work will extend the analysis to a larger data set, and ensure that use of the head tracking data is a viable tool for the ongoing behavior analysis work. Based on preliminary results from testing of the first data set, it is expected there will be significant positive correlation in future samples and the machine vision head tracking will prove consistent enough for use in the large scale behavioral study.

scholarly journals MIT Advanced Vehicle Technology Study: Large-Scale Naturalistic Driving Study of Driver Behavior and Interaction With Automation

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 102021-102038 ◽  
Author(s):  
Lex Fridman ◽  
Daniel E. Brown ◽  
Michael Glazer ◽  
William Angell ◽  
Spencer Dodd ◽  
...  
Keyword(s):  
Large Scale ◽  
Driver Behavior ◽  
Technology Study ◽  
Naturalistic Driving Study ◽  
Vehicle Technology ◽  
Naturalistic Driving

Large-scale Motion of Solar Filaments

2000 ◽  
Vol 179 ◽  
pp. 205-208
Author(s):  
Pavel Ambrož ◽  
Alfred Schroll
Keyword(s):  
Magnetic Flux ◽  
Proper Motion ◽  
Time Scale ◽  
Large Scale ◽  
Accuracy Of Measurements ◽  
Solar Filaments ◽  
General Movement ◽  
Scale Motion ◽  
Velocity Scatter

AbstractPrecise measurements of heliographic position of solar filaments were used for determination of the proper motion of solar filaments on the time-scale of days. The filaments have a tendency to make a shaking or waving of the external structure and to make a general movement of whole filament body, coinciding with the transport of the magnetic flux in the photosphere. The velocity scatter of individual measured points is about one order higher than the accuracy of measurements.

Methods for the Quantification of Salivary Cortisol and of a-amylase in Biosensors and Portable Devices

2018 ◽  
Vol 68 (12) ◽  
pp. 2857-2859
Author(s):  
Cristina Mihaela Ghiciuc ◽  
Andreea Silvana Szalontay ◽  
Luminita Radulescu ◽  
Sebastian Cozma ◽  
Catalina Elena Lupusoru ◽  
...  
Keyword(s):  
Real Time ◽  
Medical Practice ◽  
Salivary Cortisol ◽  
Clinical Studies ◽  
Large Scale ◽  
Psychological Research ◽  
Portable Devices ◽  
Salivary Amylase ◽  
Specificity And Sensitivity

There is an increasing interest in the analysis of salivary biomarkers for medical practice. The objective of this article was to identify the specificity and sensitivity of quantification methods used in biosensors or portable devices for the determination of salivary cortisol and salivary a-amylase. There are no biosensors and portable devices for salivary amylase and cortisol that are used on a large scale in clinical studies. These devices would be useful in assessing more real-time psychological research in the future.

Identification of N-Substituted Triazolo-azetidines as Novel Antibacterials using pDualrep2 HTS Platform

2019 ◽  
Vol 22 (5) ◽  
pp. 346-354
Author(s):  
Yan A. Ivanenkov ◽  
Renat S. Yamidanov ◽  
Ilya A. Osterman ◽  
Petr V. Sergiev ◽  
Vladimir A. Aladinskiy ◽  
...  
Keyword(s):  
Antibacterial Activity ◽  
Large Scale ◽  
Underlying Mechanism ◽  
Luciferase Assay ◽  
Reporter System ◽  
E Coli ◽  
Starting Point ◽  
High Concentrations ◽  
Mic Value

Aim and Objective: Antibiotic resistance is a serious constraint to the development of new effective antibacterials. Therefore, the discovery of the new antibacterials remains one of the main challenges in modern medicinal chemistry. This study was undertaken to identify novel molecules with antibacterial activity. Materials and Methods: Using our unique double-reporter system, in-house large-scale HTS campaign was conducted for the identification of antibacterial potency of small-molecule compounds. The construction allows us to visually assess the underlying mechanism of action. After the initial HTS and rescreen procedure, luciferase assay, C14-test, determination of MIC value and PrestoBlue test were carried out. Results: HTS rounds and rescreen campaign have revealed the antibacterial activity of a series of Nsubstituted triazolo-azetidines and their isosteric derivatives that has not been reported previously. Primary hit-molecule demonstrated a MIC value of 12.5 µg/mL against E. coli Δ tolC with signs of translation blockage and no SOS-response. Translation inhibition (26%, luciferase assay) was achieved at high concentrations up to 160 µg/mL, while no activity was found using C14-test. The compound did not demonstrate cytotoxicity in the PrestoBlue assay against a panel of eukaryotic cells. Within a series of direct structural analogues bearing the same or bioisosteric scaffold, compound 2 was found to have an improved antibacterial potency (MIC=6.25 µg/mL) close to Erythromycin (MIC=2.5-5 µg/mL) against the same strain. In contrast to the parent hit, this compound was more active and selective, and provided a robust IP position. Conclusion: N-substituted triazolo-azetidine scaffold may be used as a versatile starting point for the development of novel active and selective antibacterial compounds.

The Nature of Time

2018 ◽  
Author(s):  
Donald C. Williams
Keyword(s):  
Large Scale ◽  
Time Travel ◽  
Dimensional Manifold ◽  
Nature Of Time ◽  
Manifold Theory ◽  
The Universe ◽  
Theoretical Cost ◽  
Do So

This chapter provides a fuller treatment of the pure manifold theory with an expanded discussion of competing doctrines. It is argued that competing doctrines fail to account for the extensive and/or transitory aspect(s) of time, or they do so at great theoretical cost. The pure manifold theory accounts for the extensive aspect of time because it admits a four-dimensional manifold and it accounts for the transitory aspect of time because it hypothesizes that the increase of entropy is the thing that is ‘felt’ in veridical cases of felt passage. A four-dimensionalist theory of time travel is outlined, along with a sketch of large-scale cosmological traits of the universe.

Machine Learning Approach for Predicting Lane-Change Maneuvers using the SHRP2 Naturalistic Driving Study Data

2021 ◽  
pp. 036119812110035
Author(s):  
Anik Das ◽  
Mohamed M. Ahmed
Keyword(s):  
Machine Learning ◽  
Prediction Accuracy ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Lane Change ◽  
Adaptive Boosting ◽  
Extreme Gradient Boosting ◽  
Naturalistic Driving Study ◽  
Naturalistic Driving ◽  
Change Prediction

Accurate lane-change prediction information in real time is essential to safely operate Autonomous Vehicles (AVs) on the roadways, especially at the early stage of AVs deployment, where there will be an interaction between AVs and human-driven vehicles. This study proposed reliable lane-change prediction models considering features from vehicle kinematics, machine vision, driver, and roadway geometric characteristics using the trajectory-level SHRP2 Naturalistic Driving Study and Roadway Information Database. Several machine learning algorithms were trained, validated, tested, and comparatively analyzed including, Classification And Regression Trees (CART), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Adaptive Boosting (AdaBoost), Support Vector Machine (SVM), K Nearest Neighbor (KNN), and Naïve Bayes (NB) based on six different sets of features. In each feature set, relevant features were extracted through a wrapper-based algorithm named Boruta. The results showed that the XGBoost model outperformed all other models in relation to its highest overall prediction accuracy (97%) and F1-score (95.5%) considering all features. However, the highest overall prediction accuracy of 97.3% and F1-score of 95.9% were observed in the XGBoost model based on vehicle kinematics features. Moreover, it was found that XGBoost was the only model that achieved a reliable and balanced prediction performance across all six feature sets. Furthermore, a simplified XGBoost model was developed for each feature set considering the practical implementation of the model. The proposed prediction model could help in trajectory planning for AVs and could be used to develop more reliable advanced driver assistance systems (ADAS) in a cooperative connected and automated vehicle environment.

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