Browsing by Author "Kolmanovsky, I."

Now showing 1 - 7 of 7

Open Access
Adaptive control allocation for constrained systems
(Elsevier, 2020-06) Tohidi, Seyed Shahabaldin; Yıldız, Yıldıray; Kolmanovsky, I.
This paper proposes an adaptive control allocation approach for uncertain over-actuated systems with actuator saturation. The proposed control allocation method does not require uncertainty estimation or persistency of excitation. Actuator constraints are respected by employing the projection algorithm. The stability analysis is provided for two different cases: when ideal adaptive parameters are inside and when they are outside of the projection boundary which is chosen consistently with the actuator saturation limits. Simulation results for the Aerodata Model in Research Environment (ADMIRE), which is used as an example of an over-actuated aircraft system with actuator saturation, demonstrate the effectiveness of the proposed method.
Open Access
Game theoretic modeling of driver and vehicle interactions for verification and validation of autonomous vehicle control systems
(Institute of Electrical and Electronics Engineers, 2018) Li, N.; Oyler, D.W.; Zhang M.; Yildız, Yıldıray; Kolmanovsky, I.; Girard, A. R.
Autonomous driving has been the subject of increased interest in recent years both in industry and in academia. Serious efforts are being pursued to address legal, technical, and logistical problems and make autonomous cars a viable option for everyday transportation. One significant challenge is the time and effort required for the verification and validation of the decision and control algorithms employed in these vehicles to ensure a safe and comfortable driving experience. Hundreds of thousands of miles of driving tests are required to achieve a well calibrated control system that is capable of operating an autonomous vehicle in an uncertain traffic environment where interactions among multiple drivers and vehicles occur simultaneously. Traffic simulators where these interactions can be modeled and represented with reasonable fidelity can help to decrease the time and effort necessary for the development of the autonomous driving control algorithms by providing a venue where acceptable initial control calibrations can be achieved quickly and safely before actual road tests. In this paper, we present a game theoretic traffic model that can be used to: 1) test and compare various autonomous vehicle decision and control systems and 2) calibrate the parameters of an existing control system. We demonstrate two example case studies, where, in the first case, we test and quantitatively compare two autonomous vehicle control systems in terms of their safety and performance, and, in the second case, we optimize the parameters of an autonomous vehicle control system, utilizing the proposed traffic model and simulation environment. IEEE
Open Access
Game Theoretic Modeling of Vehicle Interactions at Unsignalized Intersections and Application to Autonomous Vehicle Control
(IEEE, 2018) Li, N.; Kolmanovsky, I.; Girard, A.; Yıldız, Yıldıray
In this paper, we discuss a game theoretic approach to model the time-extended, multi-step, and interactive decision making of vehicles at unsignalized intersections. The vehicle interaction model is then used to define an autonomous vehicle controller. Simulation results for a common intersection scenario are reported.
Open Access
Game theory-based traffic modeling for calibration of automated driving algorithms
(Springer, Cham, 2019) Yıldız, Yıldıray; Li, Nan; Zhang, M.; Kolmanovsky, I.; Girard, A.; Waschl, H.; Kolmanovsky, I.; Willems, F.
Automated driving functions need to be validated and calibrated so that a self-driving car can operate safely and efficiently in a traffic environment where interactions between it and other traffic participants constantly occur. In this paper, we describe a traffic simulator capable of representing vehicle interactions in traffic developed based on a game-theoretic traffic model. We demonstrate its functionality for parameter optimization in automated driving algorithms by designing a rule-based highway driving algorithm and calibrating the parameters using the traffic simulator.
Open Access
Game-theoretic modeling of traffic in unsignalized intersection network for autonomous vehicle control verification and validation
(IEEE, 2020) Tian, R.; Li, N.; Kolmanovsky, I.; Yıldız, Yıldıray; Girard, A. R.
For a foreseeable future, autonomous vehicles (AVs) will operate in traffic together with human-driven vehicles. Their planning and control systems need extensive testing, including early-stage testing in simulations where the interactions among autonomous/human-driven vehicles are represented. Motivated by the need for such simulation tools, we propose a game-theoretic approach to modeling vehicle interactions, in particular, for urban traffic environments with unsignalized intersections. We develop traffic models with heterogeneous (in terms of their driving styles) and interactive vehicles based on our proposed approach, and use them for virtual testing, evaluation, and calibration of AV control systems. For illustration, we consider two AV control approaches, analyze their characteristics and performance based on the simulation results with our developed traffic models, and optimize the parameters of one of them.
Open Access
Stochastic driver modeling and validation with traffic data
(IEEE, 2019) Albaba, Mert; Yıldız, Yıldıray; Li, N.; Kolmanovsky, I.; Girard, A.
This paper describes a stochastic modeling approach for predicting driver responses in highway traffic. Different from existing approaches in the literature, the proposed modeling framework allows simultaneous decision making for multiple drivers (>100), in a computationally feasible manner, instead of modeling the decisions of an ego driver and assuming a predetermined driving pattern for other drivers in a given scenario. This is achieved by a unique combination of hierarchical game theory, which is used to model strategic decision making, and stochastic reinforcement learning, which is employed to model multi-move decision making. The proposed approach can be utilized to create high fidelity traffic simulators, which can be used to facilitate the validation of autonomous driving control algorithms by providing a safe and relatively fast environment for initial assessment and tuning. What makes the proposed approach appealing especially for autonomous driving research is that the driver models are strategic, meaning that their responses are based on predicted actions of other intelligent agents in the traffic scenario, where these agents can be human drivers or autonomous vehicles. Therefore, these models can be used to create traffic models with multiple human-machine interactions. To evaluate the fidelity of the framework, created stochastic driver models are compared with real driving patterns, processed from the traffic data collected by US Federal Highway Administration on US101 (Hollywood Freeway) on June 15th, 2005.
Open Access
A Traffic Simulation Model with Interactive Drivers and High-fidelity Car Dynamics
(Elsevier, 2019) Su, G.; Li, N.; Yıldız, Yıldıray; Girard, A.; Kolmanovsky, I.
We integrate a set of game-theoretic driver decision-making models with the high-fidelity car driving simulator The Open Racing Car Simulator (TORCS). The game-theoretic driver models simulate the interactive decision making processes of the drivers and TORCS simulates vehicle dynamics in multi-vehicle highway traffic scenarios. We use the integrated simulator to collect human driving data and then use these data to validate and re-calibrate our driver and traffic models. Such an integrated simulator can be used in the development, verification and validation of automated driving functions.