Identification of Linear Tire Cornering Stiffness Using Subspace Methods

In this paper, subspace identification methods are proposed to estimate the linear tire cornering stiffness, which are only based on the road tests data without any prior knowledge. This kind of data-driven method has strong robustness. In order to validate the feasibility and effectiveness of the algorithms, a series of standard road tests are carried out. Comparing with different subspace algorithms used in road tests, it can be concluded that the front tire cornering stiffness can be estimated accurately by the N4SID and CCA methods when the double lane change test data are taken into analysis.

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Influence of Autonomous Vehicles on Car-Following Behavior of Human Drivers

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198119862628 ◽

2019 ◽

Vol 2673 (12) ◽

pp. 367-379 ◽

Cited By ~ 2

Author(s):

Yalda Rahmati ◽

Mohammadreza Khajeh Hosseini ◽

Alireza Talebpour ◽

Benjamin Swain ◽

Christopher Nelson

Keyword(s):

Autonomous Vehicles ◽

Simulation Models ◽

Driving Behavior ◽

Data Driven ◽

Microscopic Simulation ◽

Car Following ◽

The Road ◽

Human Driver ◽

Significant Difference ◽

On The Road

Despite numerous studies on general human–robot interactions, in the context of transportation, automated vehicle (AV)–human driver interaction is not a well-studied subject. These vehicles have fundamentally different decision-making logic compared with human drivers and the driving interactions between AVs and humans can potentially change traffic flow dynamics. Accordingly, through an experimental study, this paper investigates whether there is a difference between human–human and human–AV interactions on the road. This study focuses on car-following behavior and conducted several car-following experiments utilizing Texas A&M University’s automated Chevy Bolt. Utilizing NGSIM US-101 dataset, two scenarios for a platoon of three vehicles were considered. For both scenarios, the leader of the platoon follows a series of speed profiles extracted from the NGSIM dataset. The second vehicle in the platoon can be either another human-driven vehicle (scenario A) or an AV (scenario B). Data is collected from the third vehicle in the platoon to characterize the changes in driving behavior when following an AV. A data-driven and a model-based approach were used to identify possible changes in driving behavior from scenario A to scenario B. The findings suggested there is a statistically significant difference between human drivers’ behavior in these two scenarios and human drivers felt more comfortable following the AV. Simulation results also revealed the importance of capturing these changes in human behavior in microscopic simulation models of mixed driving environments.

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Automated Data-Driven Hint Generation in Intelligent Tutoring Systems for Code-Writing: On the Road of Future Research

International Journal of Emerging Technologies in Learning (iJET) ◽

10.3991/ijet.v13i09.8023 ◽

2018 ◽

Vol 13 (09) ◽

pp. 174

Author(s):

Hieu Trong Bui ◽

Syed Malek F D Syed Mustapha

Keyword(s):

Intelligent Tutoring Systems ◽

Intelligent Tutoring ◽

Research Field ◽

Data Driven ◽

Future Research ◽

Large Solution ◽

Tutoring Systems ◽

The Road ◽

On The Road ◽

New Research

Introductory programming is an essential part of the curriculum in any engineering discipline in universities. However, for many beginning students, it is very difficult to learn. In particular, these students often get stuck and frustrated when attempting to solve programming exercises. One way to assist beginning programmers to overcome difficulties in learning to program is to use intelligent tutoring systems (ITSs) for programming, which can provide students with personalized hints of students’ solving process in programming exercises. Currently, mostly these systems manually construct the domain models. They take much time to construct, especially for exercises with very large solution spaces. One of the major challenges associated with handling ITSs for programming comes from the diversity of possible code solutions that a student can write. The use of data-driven approaches to develop these ITSs is just starting to be explored in the field. Given that this is still a relatively new research field, many challenges are still remained unsolved. Our goal in this paper is to review and classify analysis techniques that are requested to generate data-driven hints in ITSs for programming. This work also aims equally to identify the possible future directions in this research field.

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Asymmetric Energy Harvesting and Hydraulically Interconnected Suspension: Modeling and Validations

Volume 3: 21st International Conference on Advanced Vehicle Technologies; 16th International Conference on Design Education ◽

10.1115/detc2019-98402 ◽

2019 ◽

Cited By ~ 1

Author(s):

Yuzhe Chen ◽

Bonan Qin ◽

Sijing Guo ◽

Liangyao Yu ◽

Lei Zuo

Keyword(s):

Energy Harvesting ◽

Vehicle Dynamics ◽

Model Simulation ◽

Lane Change ◽

The Road ◽

Car Model ◽

Sinusoidal Input ◽

Double Lane Change ◽

Lane Change Test ◽

Better Than

Abstract This paper introduces a new form of energy-harvesting suspension that is integrated in a hydraulically interconnected suspension (HIS) system. The combined energy-harvesting and hydraulic interconnection features provide improved energy efficiency and vehicle dynamics performance. A half car model and a full car model are developed to validate the effectiveness of this design. Different dynamic input scenarios are used for model simulation, which includes single-wheel sinusoidal input, two-wheel sinusoidal input and double lane change test. The system performs better than a conventional suspension system in rolling dynamics in the cases of the single-wheel road input and double lane change test. The heaving dynamics is dependent on the frequency of the road input. The energy harvesting can generate up to 421 w at 4 Hz and 40 mm (peak to peak) road input.

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