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Lanka, Venkata Raghava Ravi
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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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Analysis and Mathematical Modeling of Car-Following Behavior of Automated Vehicles for Safety Evaluation

National Highway Traffic Safety Administration-Garrick Forkenbrock
Ohio State University-Venkata Raghava Ravi Lanka
Published 2019-04-02 by SAE International in United States
With the emergence of Driving Automation Systems (SAE levels 1-5), the necessity arises for methods of evaluating these systems. However, these systems are much more challenging to evaluate than traditional safety features (SAE level 0). This is because an understanding of the Driving Automation system’s response in all possible scenarios is desired, but prohibitive to comprehensively test. Hence, this paper attempts to evaluate one such system, by modeling its behavior. The model generated parameters not only allow for objective comparison between vehicles, but also provide a more complete understanding of the system. The model can also be used to extrapolate results by simulating other scenarios without the need for conducting more tests.In this paper, low speed automated driving (also known as Traffic Jam Assist (TJA)) is studied. This study focused on the longitudinal behavior of automated vehicles while following a lead vehicle (LV) in traffic jam scenarios. The automated vehicle behavior is modeled using three car-following models. The models are then used to predict the behavior of the vehicle in a randomized scenario. This also…
This content contains downloadable datasets
Annotation ability available