Arda Kurt

The Ohio State University | OSU · Center for Automotive Research

There is an increasing demand for quantitative risk assessment tools capable of providing safety assurances for autonomous vehicle control systems. This demand is due to the recent rise of autonomous functions that are being incorporated into aerospace and automotive domains. A deductive implementation of Markov Cell-to-Cell Mapping Technique is pr...

Lane change maneuver of high-speed automated vehicles is complicated since it involves highly nonlinear vehicle dynamics, which is critical for the driving safety and handling stability. Addressing this challenge, we present the dynamic modeling and control of high-speed automated vehicles for lane change maneuver. A nonlinear single-track vehicle...

The level of autonomous functions in vehicular control systems has been on a steady rise. This rise makes it more challenging for control system engineers to ensure a high level of safety, especially against unexpected failures such as stochastic hardware failures. A generic Backtracking Process Algorithm (BPA) based on a deductive implementation o...

The level of autonomous functions in vehicular control systems has been on a steady rise. This rise makes it more challenging for control system engineers to ensure a high level of safety, especially against unexpected failures such as stochastic hardware failures. A generic Backtracking Process Algorithm (BPA) based on a deductive implementation o...

A generic backtracking process algorithm (BPA) based on the deductive implementation of a probabilistic Markov/Cell-to-Cell Mapping Technique is proposed for risk informed identification of critical scenarios involving control systems of unmanned aerial systems (UAS) operating in the National Airspace (NAS). A discrete-state representation of the U...

A control approach for automated highway driving is proposed in this study, which can learn from human driving data, and is applied to the longitudinal trajectory of an autonomous car. Naturalistic driving data are used as samples to train the model offline. Then, the model is used online to emulate what a human driver would do by computing acceler...

Looking out for pedestrians has long been one of the most important issues for intelligent vehicles. Sometimes, intelligent vehicles have to cope with a large crowd of pedestrians. This is extremely common in shared spaces such as campus, shopping mall, or transportation station. In this paper, a vehicle-pedestrian interaction simulator is introduc...

OSU SMOOTH (Smart Mobile Operation: OSU Transportation Hub) is an autonomous vehicle test bed that aims at providing intelligent transportation systems research for the first and last mile of an individualfis commute. It augments current public transportation infrastructure by providing users with access to a heterogeneous network of on-demand auto...

Advanced Driver Assistance systems (ADAS) are systems that assist the driver during the driving task. This technology has great potentials in improving driver and traffic safety. It is very important for an ADAS to predict human drivers’ behaviors at urban environment to avoid crashes. Because of the complexity of human-vehicle interaction, it is d...

This paper presents the interim results of a three-year NASA project for the development of a comprehensive framework for the validation and verification (V&V) of model-based control systems and adaptive control systems (MBCSs/ACSs), with focus on Unmanned Aircraft Systems (UAS) applications. The framework applies a formal V&V methodology based on...

Intelligence in vehicles has developed through the years as self-driving expectations and capabilities have increased. To date, the majority of the literature has focused on longitudinal control topics (e.g. Adaptive Cruise Control (ACC), Cooperative ACC (CACC), etc.). To a lesser extent, there have been a variety of research articles specifically...

Recognizing dangerous driver behavior is an essential part of predicting accurate vehicle trajectories in vehicle active safety systems. This paper proposes a lane change behavior classification approach to detect dangerous cut-in behaviors on highways. First, a probabilistic lane change behavior classifier is proposed based on Hidden Markov Models...

Due to the relatively high density of vehicles and humans at intersections, it is crucial for an Advanced Driver Assistance System (ADAS) to predict human driver behaviors to avoid crashes. Due to the complexity of human’s behavior interacting with a vehicle, it is very difficult to find an explicit model to analysis the driver’s behavior. In this...

This paper proposes a speed prediction scheme for multi-vehicle car-following scenarios, which involves vehicle intelligence, human drivers, and vehicle dynamics interacting with one another, under low vehicular communication penetration rates. The algorithm relies on a small number of vehicles equipped with communication capabilities and utilizes...

The capability to estimate driver's intention leads to the development of advanced driver assistance systems that can assist the drivers in complex situations. Developing precise driver behavior models near intersections can considerably reduce the number of accidents at road intersections. In this study, the problem of driver behavior modeling nea...

Accurate trajectory prediction of a lane changing vehicle is a key issue for risk assessment and early danger warning in advanced driver assistance systems(ADAS). This paper proposes a trajectory prediction approach for a lane changing vehicle considering high-level driver status. A driving behavior estimation and classification model is developed...

This paper develops an encoding scheme for discrete-state systems as part of a hybrid-state hierarchy. The codes are based on commands between subsystems, in the sense that the interactions of the discrete states with the continuous states are exploited to attach significance to what each discrete state does to the continuous subsystem. The resulta...

This study illustrates a methodology to reduce the time and effort spent on full-scale Intelligent Transportation System testing, through the use of small-scale testbeds. Scaled down testing platforms enable the researchers to implement, compare, and assess different architectures for intelligent transportation by deploying hardware-in-the-loop (HI...

In this paper, a state space sampling-based local trajectory generation framework for autonomous vehicles driving along a reference path is proposed. The presented framework employs a two-step motion planning architecture. In the first step, a Support Vector Machine based approach is developed to refine the reference path through maximizing the lat...

In this study, applicability of verification and correct-by-design hybrid systems modeling and reachability-based controllers for vehicular automation are investigated. Two perspectives in hybrid systems modeling will be introduced, and then reachability analysis techniques will be developed to compute exact reachable sets from a specified unsafe s...

This chapter focuses on procedures and tools used in the testing stages of the intelligent vehicle design process. By testing the developed intelligent vehicle algorithms and architectures in a series of environments, starting from pure simulation and ending with physical, full-scale tests, it is possible to avoid the time, safety, and logistics co...

This study focuses on developing and illustrating a passenger motion and behavior model for public transportation applications within Intelligent Transportation System research. The specifics of the model are selected to fit the needs of public transportation examples, as opposed to the more generic pedestrian models investigated in the literature....

form only given. This study investigates a number of control and coordination aspects of autonomous navigation in real-life urban traffic, focusing on connected vehicles as the means to achieve the required coordination. The proposed architecture includes a mixture of human driven, partial and fully autonomous experimental vehicles that are equippe...

A collision avoidance problem for the vehicle equipped with adaptive cruise control is considered in the context of hybrid systems with emphasis on safety verification.

A collision avoidance problem for the vehicle equipped with adaptive cruise control is considered in the context of hybrid systems with emphasis on safety verification.

This paper proposes a system architecture, related design approaches for autonomous mobile systems and guidelines for self-sufficient autonomy. Development of a tiered layout for a hybrid-state control in a series of stages as well as the integration of such a controller in the overall autonomy structure are proposed and demonstrated as part of mul...

This paper presents the cooperative adaptive cruise control implementation of Team Mekar at the Grand Cooperative Driving Challenge (GCDC). The Team Mekar vehicle used a dSpace microautobox for access to the vehicle controller area network bus and for control of the autonomous throttle intervention and the electric-motor-operated brake pedal. The v...

This study investigates the issue of scalability for autonomous vehicles and systems in urban environments. As the number of agents such as infrastructure elements and communication-capable vehicles increases, the systems that are designed to sense/track/control these agents get burdened under the increased number of tracks/measurements. By employi...

This study focuses on a number of control and coordination aspects of autonomous navigation in real-life urban traffic. By expanding the inherent hierarchy of the hybrid-state system formulation, a highly-structured yet modular architecture was developed to connect various traffic elements. The feasibility of coordination under vehicle-to-vehicle a...

This poster investigates sensory data processing, filtering and sensor fusion methods for autonomous vehicles operating in real-life, urban environments with human and machine drivers, and pedestrians. Extended Kalman Filters were used to develop decentralized data fusion algorithms for communicating vehicles, Particle Filters were improved by assi...

The authors present a cyber-physical systems related study on the estimation and prediction of driver states in autonomous vehicles. The first part of this study extends on a previously developed general architecture for estimation and prediction of hybrid-state systems. The extended system utilizes the hybrid characteristics of decision-behavior c...

This study proposes a probabilistic decision-making model for driving decisions. The decision-making process that is modeled stochastically is part of the Human Driver Model developed in an earlier study, in which perception, world-model and reflexive behavior are represented as separate modules. Finite-state machine design guidelines for decision-...

The first part of this study develops a general architecture for estimation and prediction of hybrid-state systems. The proposed system utilizes the hybrid characteristics of decision-behaviour coupling of many systems such as the driver and the vehicle; uses estimates of observable parameters to track instantaneous discrete state and predicts the...

This paper proposes a modular architecture for the development of an indoor testbed for intelligent transportation systems. The main focus is on repeatable, low-cost tests for urban scenarios, especially for higher-level decision making and situation awareness problems. It provides a supplement to outdoor tests and it is also used as a teaching pla...

This paper analyzes a hybrid-state-system-based controller for an autonomous vehicle in urban traffic and provides development procedures for hybrid-state systems for automatic control. The Ohio-State University Autonomous City Transport utilizes a discrete-state system, based on a finite state machine for high-level decision making and a continuou...

In this study, four signal processing schemes regarding sonar sensor based map-building applications were compared. The newly proposed method, Directional Maximum is found to be successful in terms of reducing the innate angular ambiguity of the sonar sensors. With respect to several works presented earlier in the same field and specifically map- b...