Arda Kurt

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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 vehicle(More)
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 most(More)
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 platform.(More)
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-making(More)
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 coupling(More)
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 near a road(More)
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 lateral(More)
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 based on(More)
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 and(More)
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 assigning(More)