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Driver’s Response Prediction Using Naturalistic Data Set

SAE International Journal of Advances and Current Practices in Mobility

Ohio State University-Venkata Raghava Ravi Lanka, Dennis Guenther
SEA, Ltd.-Gary Heydinger
  • Journal Article
  • 2019-01-0128
Published 2019-04-02 by SAE International in United States
Evaluating the safety of Autonomous Vehicles (AV) is a challenging problem, especially in traffic conditions involving dynamic interactions. A thorough evaluation of the vehicle’s decisions at all possible critical scenarios is necessary for estimating and validating its safety. However, predicting the response of the vehicle to dynamic traffic conditions can be the first step in the complex problem of understanding vehicle’s behavior. This predicted response of the vehicle can be used in validating vehicle’s safety.In this paper, models based on Machine Learning were explored for predicting and classifying driver’s response. The Naturalistic Driving Study dataset (NDS), which is part of the Strategic Highway Research Program-2 (SHRP2) was used for training and validating these Machine Learning models. Various popular Machine Learning Algorithms were used for classifying and predicting driver’s response, such as Extremely Randomized Trees and Gaussian Mixture Model based Hidden Markov Model, which are widely used in multiple domains.For classifying driver’s response, longitudinal acceleration vs lateral acceleration plot (Ax-Ay plot) was divided into nine different classes and selected Machine Learning models were trained for predicting…
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Inertia Tensor and Center of Gravity Measurement for Engines and Other Automotive Components

Ohio State University-Dennis Guenther
SEA Ltd.-Dale A. Andreatta, Gary Heydinger, Scott Zagorski
Published 2019-04-02 by SAE International in United States
A machine has been developed to measure the complete inertia matrix; mass, center of gravity (CG) location, and all moments and products of inertia. Among other things these quantities are useful in studying engine vibrations, calculation of the torque roll axis, and in the placement of engine mounts. While the machine was developed primarily for engines it can be used for other objects of similar size and weight, and even smaller objects such as tires and wheels/rims.A key feature of the device is that the object, once placed on the test table, is never reoriented during the test cycle. This reduces the testing time to an hour or less, with the setup time being a few minutes to a few hours depending on the complexity of the shape of the object. Other inertia test methods can require up to five reorientations, separate CG measurement, and up to several days for a complete test.The device uses a system of pivots, springs, and three sensors to get the three moments and three products of inertia, plus the…
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An ATV Model for CarSim

SEA Ltd.-Gary Heydinger, Scott Zagorski, Anmol Sidhu
The Ohio State University-Bingrui Jia, Dennis Guenther
Published 2018-04-03 by SAE International in United States
This paper presents the development of a CarSim model of an All-Terrain Vehicle (ATV) that can be used to predict the handling and stability characteristic of the vehicle. The inertia and suspension characteristics of a subject ATV are measured and a model of the ATV is built in CarSim based on the measurements. A simplified suspension model is developed to convert the suspension compliance measurements into parameters suitable to a CarSim model. Procedures used to apply vehicle mass, inertia and suspension kinematics data in CarSim are also shown. The model is evaluated using predictions of vehicle response during a constant radius circle test. The simulation results of the maneuver are compared with the field test results shown in a recent CPSC report on ATV’s. Similar cornering characteristics are found in both results. Modifications are made to the model to study how changes to the ATV affect performance. The model developed provides a sound baseline for more comprehensive ATV models that contain more accurate tire and damping models based on actual tire and shock absorber measurements.
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Analysis of Human Driver Behavior in Highway Cut-in Scenarios

National Highway Traffic Safety Administration-Frank Barickman
The Ohio State University-SeHwan Kim, Junmin Wang, Dennis Guenther, Gary Heydinger
Published 2017-03-28 by SAE International in United States
The rapid development of driver assistance systems, such as lane-departure warning (LDW) and lane-keeping support (LKS), along with widely publicized reports of automated vehicle testing, have created the expectation for an increasing amount of vehicle automation in the near future. As these systems are being phased in, the coexistence of automated vehicles and human-driven vehicles on roadways will be inevitable and necessary. In order to develop automated vehicles that integrate well with those that are operated in traditional ways, an appropriate understanding of human driver behavior in normal traffic situations would be beneficial.Unlike many research studies that have focused on collision-avoidance maneuvering, this paper analyzes the behavior of human drivers in response to cut-in vehicles moving at similar speeds. Both automated and human-driven vehicles are likely to encounter this scenario in daily highway driving. This research has identified several possible cut-in scenario configurations that can be experienced on the highway. Data have been collected from a diverse pool of human subjects using a driving simulator with preprogrammed scenarios. To understand each driver’s behavior in response…
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