Umit Ozguner

The Ohio State University | OSU · Department of Electrical and Computer Engineering

Q. How did your education and early career lead to your initial and continuing interest in the control field?

Recent research in pedestrian simulation often aims to develop realistic behaviors in various situations, but it is challenging for existing algorithms to generate behaviors that identify weaknesses in automated vehicles' performance in extreme and unlikely scenarios and edge cases. To address this, specialized pedestrian behavior algorithms are ne...

Modeling mixed-traffic motion and interactions is crucial to assess safety, efficiency, and feasibility of future urban areas. The lack of traffic regulations, diverse transport modes, and the dynamic nature of mixed-traffic zones like shared spaces make realistic modeling of such environments challenging. This paper focuses on the generalizability...

Entropy measures uncertainty present within the data. Highly Automated Vehicles (HAVs) can navigate safely and efficiently if location information of occluded dynamic objects is available. It is assumed that dynamic objects have GPS receivers, and location information can be acquired through a fast communication link. However, GPS info can be easil...

Understanding the causal relationship between an autonomous vehicle's input state and its output action is important for safety mitigation and explainable automated driving. However, reinforcement learning approaches have the drawback of being black box models. This work proposes an interpretable state representation that can capture state-action c...

How many scenarios are sufficient to validate the safe Operational Design Domain (ODD) of an Automated Driving System (ADS) equipped vehicle? Is a more significant number of sampled scenarios guaranteeing a more accurate safety assessment of the ADS? Despite the various empirical success of ADS safety evaluation with scenario sampling in practice,...

Predicting vulnerableroad user behavior is an essential prerequisite for deploying Automated Driving Systems (ADS) in the real-world. Pedestrian crossing intention should be recognized in real-time, especially for urban driving. Recent works have shown the potential of using vision-based deep neural network models for this task. However, these mode...

Dear Authors and Readers, My time as Editor-in-Chief of IEEE TRANSACTIONS ON INTELLIGENT VEHICLES has come to an end. It was an honor to help start up the T-IV and be the Editor-in-Chief for the past 6 years. I want to thank all of you for your help and commitment to T-IV. I thanked the Senior Editors and Associate Editors directly earlier, but the...

Many trucks are used for a class of activities involving a sequence of basic load-haul-dump operations. The repetitiveness of this operation has been an enabler for autonomous vehicle technology in efforts to increase safety and efficiency. In this paper, we present a framework for the automation of the load-haul-dump operation in a mine setting us...

A connected and automated vehicle safety metric determines the performance of a subject vehicle (SV) by analyzing the data involving the interactions among the SV and other dynamic road users and environmental features. When the data set contains only a finite set of samples collected from the naturalistic mixed multi-modal traffic driving environm...

An online evolving method, named evolving finite state machine (e-FSM), is proposed to develop an optimal Markov driving model. The model has the same properties as a standard Markov model, but its states and transition dynamics evolve without human supervision. In this article, we introduce: 1) the principles of the e-FSM’s novel capabilities: on...

Unlike the simpler and more limited vehicle automation systems of earlier generations, for which operation and safety could be defined by standards such as the Functional Safety Standard (ISO 26262), novel automated systems become increasingly complex to analyze due to the exploding number of possible Operational esign omain (O) configurations. To...

A connected and automated vehicle safety metric determines the performance of a subject vehicle (SV) by analyzing the data involving the interactions among the SV and other dynamic road users and environmental features. When the data set contains only a finite set of samples collected from the naturalistic mixed-traffic driving environment, a metri...

A typical scenario-based evaluation framework seeks to characterize a black-box system's safety performance (e.g., failure rate) through repeatedly sampling initialization configurations (scenario sampling) and executing a certain test policy for scenario propagation (scenario testing) with the black-box system involved as the test subject. In this...

A typical scenario-based evaluation framework seeks to characterize a black-box system's safety performance (e.g., failure rate) through repeatedly sampling initialization configurations (scenario sampling) and executing a certain test policy for scenario propagation (scenario testing) with the black-box system involved as the test subject. In this...

Social distancing (SD) is an effective measure to prevent the spread of the infectious Coronavirus Disease 2019 (COVID-19). However, a lack of spatial awareness may cause unintentional violations of this new measure. Against this backdrop, we propose an active surveillance system to slow the spread of COVID-19 by warning individuals in a region-of-...

Avoiding unseen or partially occluded vulnerable road users (VRUs) is a major challenge for fully autonomous driving in urban scenes. However, occlusion-aware risk assessment systems have not been widely studied. Here, we propose a pedestrian emergence estimation and occlusion-aware risk assessment system for urban autonomous driving. First, the pr...

How many scenarios are sufficient to validate the safe Operational Design Domain (ODD) of an Automated Driving System (ADS) equipped vehicle? Is a more significant number of sampled scenarios guaranteeing a more accurate safety assessment of the ADS? Despite the various empirical success of ADS safety evaluation with scenario sampling in practice,...

Predicting vulnerable road user behavior is an essential prerequisite for deploying Automated Driving Systems (ADS) in the real-world. Pedestrian crossing intention should be recognized in real-time, especially for urban driving. Recent works have shown the potential of using vision-based deep neural network models for this task. However, these mod...

This paper introduces a sliding-mode-based extremum-seeking algorithm aimed at generating optimal set-points of wind turbines in wind farms. A distributed extremum-seeking control is directed to fully utilize the captured wind energy by taking into consideration the wake and aerodynamic properties between wind turbines. The proposed approach is a m...

Modeling mixed-traffic motion and interactions is crucial to assess safety, efficiency, and feasibility of future urban areas. The lack of traffic regulations, diverse transport modes, and the dynamic nature of mixed-traffic zones like shared spaces make realistic modeling of such environments challenging. This paper focuses on the generalizability...

In mixed traffic scenarios, a certain number of pedestrians might coexist in a small area while interacting with vehicles. In this situation, every pedestrian must simultaneously react to the surrounding pedestrians and vehicles. Analytical modeling of such collective pedestrian motion can benefit intelligent transportation practices like shared sp...

Learned pointcloud representations do not generalize well with an increase in distance to the sensor. For example, at a range greater than 60 meters, the sparsity of lidar pointclouds reaches to a point where even humans cannot discern object shapes from each other. However, this distance should not be considered very far for fast-moving vehicles:...

A scenario-based test of operational vehicle safety presents a set of principal other vehicle (POV) trajectories that seek to enforce the subject vehicle (SV) into a certain safety-critical situation. Current scenarios are mostly (i) statistics-driven: inspired by human driver crash data, (ii) deterministic: POV trajectories are pre-determined and...

Social distancing has been proven as an effective measure against the spread of the infectious COronaVIrus Disease 2019 (COVID-19). However, individuals are not used to tracking the required 6-feet (2-meters) distance between themselves and their surroundings. An active surveillance system capable of detecting distances between individuals and warn...

In this paper, an online evolving framework is proposed to detect and revise a controller's imperfect decision-making in advance. The framework consists of three modules: the evolving Finite State Machine (e-FSM), action-reviser, and controller modules. The e-FSM module evolves a stochastic model (e.g., Discrete-Time Markov Chain) from scratch by d...

Monocular vision-based navigation for automated driving is a challenging task due to the lack of enough information to compute temporal relationships among objects on the road. Optical flow is an option to obtain temporal information from monocular camera images and has been used widely with the purpose of identifying objects and their relative mot...

Vehicle-pedestrian interaction (VPI) is one of the most challenging tasks for automated driving systems. The design of driving strategies for such systems usually starts with verifying VPI in simulation. This work proposed an improved framework for the study of VPI in uncontrolled pedestrian crossing scenarios. The framework admits the mutual effec...

Pedestrian motion modeling in mixed traffic scenarios is crucial to the development of autonomous systems in transportation related applications. This work investigated how pedestrian motion is affected by surrounding pedestrians and vehicles, i.e., vehicle-pedestrian interaction. A social force based pedestrian motion model was proposed, in which...

Automated driving in urban settings is challenging. Human participant behavior is difficult to model, and conventional, rule-based Automated Driving Systems (ADSs) tend to fail when they face unmodeled dynamics. On the other hand, the more recent, end-to-end Deep Reinforcement Learning (DRL) based model-free ADSs have shown promising results. Howev...

In this paper, an online evolving framework is proposed to detect and revise a controller’s imperfect decision-making in advance. The framework consists of three modules: the evolving Finite State Machine (e-FSM), action-reviser, and controller modules. The e-FSM module evolves a stochastic model (e.g., Discrete-Time Markov Chain) from scratch by d...

We propose a data-driven control framework for autonomous driving which combines learning-based risk assessment with personalized, safety-focused, predictive control. Different control strategies are used depending on the detected risk level of the driving situation (risky vs. non-risky). This requires a model which can understand the context of th...

An online evolving framework is proposed to support modeling the safe Automated Vehicle (AV) control system by making the controller able to recognize unexpected situations and react appropriately by choosing a better action. Within the framework, the evolving Finite State Machine (e-FSM), which is an online model able to (1) determine states uniqu...

This paper proposes a framework to recognize driving intentions and to predict driving behaviors of lane changing on the highway by using externally sensable traffic data from the host-vehicle. The framework consists of a driving characteristic estimator and a driving behavior predictor. A driver's implicit driving characteristic information is uni...

In pedestrian-dense traffic scenarios, an autonomous vehicle may have to safely drive through a crowd of pedestrians while the vehicle tries to keep the desired speed as much as possible. This requires a model that can predict the motion of crowd pedestrians and a method for the vehicle to predictively adjust its speed. In this study, the model-bas...

Predicting the collective motion of a group of pedestrians (a crowd) under the vehicle influence is essential for the development of autonomous vehicles to deal with mixed urban scenarios where interpersonal interaction and vehicle-crowd interaction (VCI) are significant. This usually requires a model that can describe individual pedestrian motion...

Identification of nonlinear systems is presented using a neural network variant known as the temporal convolutional network (TCN). The identification capabilities of TCNs and standard feedforward neural networks (FNNs) are benchmarked and compared using the Silverbox dataset: a publicly available dataset from a circuit equivalent to a nonlinear spr...

In this paper, a cooperative distributed optimization method via sliding mode extremum seeking (ES) control for a class of large-scale interconnected systems is presented. In this approach, a consensus algorithm is exploited to communicate the value of the global cost function to the ES controllers. Then, each sliding mode ES controller is designed...

Motion planning for autonomous vehicles requires spatio-temporal motion plans (i.e. state trajectories) to account for dynamic obstacles. This requires a trajectory tracking control process which faithfully tracks planned trajectories. In this paper, a control scheme is presented which first optimizes a planned trajectory and then tracks the optimi...

Jackknifing during tractor-trailer reverse driving presents a major challenge in control system design. In this paper, maneuverability conditions were explicitly derived for tractor-trailer systems that are hitched off-axle. A control safety governor was designed and constructed to guarantee that the system stays within the derived maneuverability...

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...

Pedestrian safety is of paramount importance for intelligent transportation systems. This study focuses on the scenarios where pedestrians appear as crowds and interact with moving vehicles in a relatively-free space. Based on social force model (SFM), a vehicle-crowd interaction (VCI) model is proposed to describe both the behavior of crowd pedest...

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...

In this paper, a sliding mode based Extremum Seeking (ES) control scheme is proposed to solve a class of multivariable optimization problems. This approach recasts the problem of multivariable ES control into a sequence of single variable ES control. Our approach is suitable for non-separable problems, differentiating itself from previous work in t...

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...

This paper investigates the cooperative and flexible vehicle platooning problem in automated highway systems. We formulate the platoon into a dynamically decoupled system and address the flexible platooning problem using distributed model predictive control (DMPC) techniques. A two-step noniterative DMPC strategy is proposed that sequentially solve...

Shockwaves lead to speed variation and capacity drop, which hamper the stationarity and throughput of traffic network greatly in reality. In order to dominate or suppress shockwaves, there exist two philosophies: the analytical and numerical methods to investigate various traffic management schemes. However, both are studied completely separately i...

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...

Dynamic eco-driving is a well-known umbrella term describing speed control schemes that utilize connected and automated vehicle technology for the purpose of saving fuel. If dynamic eco-driving is to be widely prescribed as an integral part of widespread fuel-saving endeavors, its expected performance as part of the overall traffic system must be a...

This note proposes a distributed model predictive control (DMPC) scheme with switched cost functions for a class of spatially interconnected systems with communication constraints. A non-iterative and parallel communication strategy is considered to ensure that all distributed controllers complete input updates at each single information exchange s...

Situational assessment (SA) is one of the key parts for the application of intelligent alternative-energy vehicles (IAVs) in the sustainable transportation. It helps IAVs understand and comprehend traffic environments better. In SA, it is crucial to be aware of uncertainty-risks, such as sensor failure or communication loss. The objective of this s...

This paper presents a concurrent region decomposition and allocation algorithm that solves the multi-MSA coordination problem within the context of multi-target tracking and surveillance missions. Our collaboration approach achieves favorable computational characteristics, compared to its alternatives, by taking advantage of a data structure we nam...

Reliable cooperative adaptive cruise control on highways requires the lead vehicle of a vehicle convoy to be capable of resisting disturbances outside the convoy. This paper proposes a controller synthesis approach adopting behavior classification to improve the lead vehicle's ability to deal with outer disturbances. First, a behavior classifier is...

This work proposes a non-iterative distributed model predictive control (DMPC) scheme for flexible vehicle platooning with state and input constraints. The proposed DMPC scheme partitions the multi-vehicle system into alternate clusters belonging to two categories, and distributively solves constrained optimal control problems (COCPs) in a two-step...

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...

Traffic congestion and energy issues have set a high bar for current ground transportation systems. With advances in vehicular communication technologies, collaborations of connected vehicles have becoming a fundamental block to build automated highway transportation systems of high efficiency. This paper presents a distributed optimal control sche...