Browsing by Subject "Autonomous vehicles"

Now showing 1 - 6 of 6

Open Access
Adaptive game-theoretic decision making for autonomous vehicle control at roundabouts
(Institute of Electrical and Electronics Engineers Inc., 2019) Tian, R.; Li, S.; Li, N.; Kolmanovsky, İ.; Girard, A.; Yıldız, Yıldıray; Teel, A. R.; Egerstedt, M.
In this paper, we propose a decision making algorithm for autonomous vehicle control at a roundabout intersection. The algorithm is based on a game-theoretic model representing the interactions between the ego vehicle and an opponent vehicle, and adapts to an online estimated driver type of the opponent vehicle. Simulation results are reported.
Open Access
FMCW radar sinyalleri ile LSTM tabanlı hedef sınıflandırması
(Bilkent University, 2021-07-19) Güneş, Oytun; Morgül, Ömer
Dünya Sağlık Örgütü’ne (WHO) göre, her yıl trafik kazalarından kaynaklı yaklaşık 20-50 milyon yaralanma olmaktadır. Yaralanmaların çoğu savunmasız yayalar, bisikletliler ve motosikletliler arasındadır. Otonom araçlar (OA) bu soruna mükemmel bir çözüm gibi görünmektedir. OA’lardaki radar sensörleri, hem hız ve menzili ölçtüğü, hem de kötü hava koşullarında çalışabildiği için etkili bir sensördür. Bu çalışmada ilk olarak, 24GHz FMCW radar sinyallerini simüle ederek 300 spektrogram içeren bir veri seti oluşturulmuştur. Bir 2 boyutlu simülasyon ortamında, orijine tek bir radar yerleştirildi ve bu dikdörtgen alana farklı parametrelerde diğer nesneler (örneğin yükseklik, yön, hız) yerleştirildi. Ardından, spektrogram görüntüleri üzerindeki Mikro-Doppler model özellikleri çıkarıldı ve Uzun Kısa Süreli Bellek Ağları (LSTM’ler) ile eğitildi. Önerilen yaklaşımın etkinliği test edildi, test setinde %95 ortalama doğruluk ve F1 skoru elde edildi, sonuçta diğer bazı yöntemlerden daha iyi performans gösterdi.
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 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
Predicting human behavior using static and dynamic models
(2021-08) Albaba, Berat Mert
Modeling human behavior is a challenging problem and it is necessary for the safe integration of autonomous systems into daily life. This thesis focuses on modeling human behavior through static and dynamic models. The ﬁrst contribution of this thesis is a stochastic modeling framework, which is a synergistic combination of a static iterated reasoning approach and deep reinforcement learning. Using statistical goodness of ﬁt tests, the proposed approach is shown to accurately predict human driver behavior in highway scenarios. Although human driver behavior are modeled successfully with the static model, the scope of interactions that can be modeled with this approach is limited to short duration interactions. For interactions that are long enough to induce adaptive behavior, we need models that incorporate learning. The second contribution of this thesis is a learning model for time extended human-human interactions. Through a hierarchical reasoning solution approach, equilibrium concepts are combined with Gaussian Processes to predict the learning behavior. As a result, a novel bounded rational learning model is proposed.
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.