About

Levent Guvenc is a professor in the Department of Mechanical and Aerospace Engineering at Ohio State University, with a joint appointment in the Department of Electrical and Computer Engineering. He received his B.S. degree in mechanical engineering from Bogazici University in Istanbul, Turkey, in 1985, where he graduated with the highest GPA in the College of Engineering. He earned his M.S. degree in mechanical engineering from Clemson University in 1988 as a Fulbright grantee, and his Ph.D. in mechanical engineering from Ohio State University in 1992. His professional experience includes a tenure at Istanbul Technical University from 1996 to 2011, where he was a faculty member and director of Mekar Labs, as well as the European Union Framework 6 funded Automotive Control and Mechatronics Research Center of Excellence. He also served as the chair of the Department of Mechanical Engineering at Istanbul Okan University from 2011 to 2014. Currently, he is a co-founder and co-director of the Automated Driving Lab at Ohio State University, focusing his research on connected and autonomous driving, cooperative mobility of road vehicles, automotive control and mechatronics, and applied robust control. Guvenc has been actively involved in various professional organizations, serving on technical committees related to automotive control, mechatronics, and intelligent vehicular systems, and has held editorial positions in prominent journals. He is an inventor with six patents and has authored or co-authored over 260 technical publications, edited volumes, books, and book chapters. Recognized for his contributions to the field, he was elected an ASME fellow in 2014 and is an IEEE Vehicular Technology Society Distinguished Lecturer for 2023-2025.

Research topics

• Computer Science
• Transport engineering
• Engineering
• Automotive engineering
• Computer network
• Simulation
• Operations research
• Petroleum engineering
• Environmental science
• Aeronautics
• Aerospace engineering
• Operating system

Selected publications

• Development of an Advisory System for Parking of a Car and Trailer

  arXiv (Cornell University) · 2025-01-10

  preprintOpen access

  Trailer parking is a challenging task due to the unstable nature of the vehicle-trailer system in reverse motion and the unintuitive steering actions required at the vehicle to accomplish the parking maneuver. This paper presents a strategy to tackle this kind of maneuver with an advisory graphic aid to help the human driver with the task of manually backing up the vehicle-trailer system. A kinematic vehicle-trailer model is derived to describe the low-speed motion of the vehicle-trailer system, and its inverse kinematics is established by generating an equivalent virtual trailer axle steering command. The advisory system graphics is generated based on the inverse kinematics and displays the expected trailer orientation given the current vehicle steer angle and configuration (hitch angle). Simulation study and animation are set up to test the efficacy of the approach, where the user can select both vehicle speed and vehicle steering angle freely, which allows the user to stop the vehicle-trailer system and experiment with different steering inputs to see their effect on the predicted trailer motion before proceeding with the best one according to the advisory graphics, hence creating a series of piecewise continuous control actions similar to how manual trailer reverse parking is usually carried out. The advisory graphics proves to provide the driver with an intuitive understanding of the trailer motion at any given configuration (hitch angle).

  PublisherOA PDFDOI

• Vehicle-in-Virtual-Environment (VVE) Method for Developing and Evaluating VRU Safety of Connected and Autonomous Driving with Focus on Bicyclist Safety

  ArXiv.org · 2025-08-30

  preprintOpen access

  Extensive research has already been conducted in the autonomous driving field to help vehicles navigate safely and efficiently. At the same time, plenty of current research on vulnerable road user (VRU) safety is performed which largely concentrates on perception, localization, or trajectory prediction of VRUs. However, existing research still exhibits several gaps, including the lack of a unified planning and collision avoidance system for autonomous vehicles, limited investigation into delay tolerant control strategies, and the absence of an efficient and standardized testing methodology. Ensuring VRU safety remains one of the most pressing challenges in autonomous driving, particularly in dynamic and unpredictable environments. In this two year project, we focused on applying the Vehicle in Virtual Environment (VVE) method to develop, evaluate, and demonstrate safety functions for Vulnerable Road Users (VRUs) using automated steering and braking of ADS. In this current second year project report, our primary focus was on enhancing the previous year results while also considering bicyclist safety.

  PublisherOA PDFDOI

• Developing a Vehicle-in-Virtual-Environment (VVE) Based Autonomous Driving Function Development and Evaluation Pipeline for Vulnerable Road User Safety

  SAE technical papers on CD-ROM/SAE technical paper series · 2025-03-31 · 1 citations

  articleSenior author

  <div class="section abstract"><div class="htmlview paragraph">Traditional methods for developing and evaluating autonomous driving functions, such as model-in-the-loop (MIL) and hardware-in-the-loop (HIL) simulations, heavily depend on the accuracy of simulated vehicle models and human factors, especially for vulnerable road user safety systems. Continuation of development during public road deployment forces other road users including vulnerable ones to involuntarily participate in the development process, leading to safety risks, inefficiencies, and a decline in public trust. To address these deficiencies, the Vehicle-in-Virtual-Environment (VVE) method was proposed as a safer, more efficient, and cost-effective solution for developing and testing connected and autonomous driving technologies by operating the real vehicle and multiple other actors like vulnerable road users in different test areas while being immersed within the same highly realistic virtual environment. This VVE approach synchronizes real-world vehicle and vulnerable road user motion within the same virtual scenario, enabling the safe and realistic testing of various traffic situations in a safe and repeatable manner. In this paper, we propose a new testing pipeline that sequentially integrates MIL, HIL, and VVE methods to comprehensively develop and evaluate autonomous driving functions. The effectiveness of this testing pipeline will be demonstrated using an autonomous driving path-tracking algorithm with local deep reinforcement learning modification for vulnerable road user collision avoidance.</div></div>

  PublisherDOI

• Vehicle-in-Virtual-Environment (VVE) Based Autonomous Driving Function Development and Evaluation Methodology for Vulnerable Road User Safety

  arXiv (Cornell University) · 2025-01-10

  preprintOpen accessSenior author

  Traditional methods for developing and evaluating autonomous driving functions, such as model-in-the-loop (MIL) and hardware-in-the-loop (HIL) simulations, heavily depend on the accuracy of simulated vehicle models and human factors, especially for vulnerable road user safety systems. Continuation of development during public road deployment forces other road users including vulnerable ones to involuntarily participate in the development process, leading to safety risks, inefficiencies, and a decline in public trust. To address these deficiencies, the Vehicle-in-Virtual-Environment (VVE) method was proposed as a safer, more efficient, and cost-effective solution for developing and testing connected and autonomous driving technologies by operating the real vehicle and multiple other actors like vulnerable road users in different test areas while being immersed within the same highly realistic virtual environment. This VVE approach synchronizes real-world vehicle and vulnerable road user motion within the same virtual scenario, enabling the safe and realistic testing of various traffic situations in a safe and repeatable manner. In this paper, we propose a new testing pipeline that sequentially integrates MIL, HIL, and VVE methods to comprehensively develop and evaluate autonomous driving functions. The effectiveness of this testing pipeline will be demonstrated using an autonomous driving path-tracking algorithm with local deep reinforcement learning modification for vulnerable road user collision avoidance.

  PublisherOA PDFDOI

• Vehicle-in-Virtual-Environment Method for ADAS and Connected and Automated Driving Function Development, Demonstration and Evaluation

  SAE technical papers on CD-ROM/SAE technical paper series · 2024-04-08 · 3 citations

  article

  <div class="section abstract"><div class="htmlview paragraph">The current approach for new Advanced Driver Assistance System (ADAS) and Connected and Automated Driving (CAD) function development involves a significant amount of public road testing which is inefficient due to the number miles that need to be driven for rare and extreme events to take place, thereby being very costly also, and unsafe as the rest of the road users become involuntary test subjects. A new development, evaluation and demonstration method for safe, efficient, and repeatable development, demonstration and evaluation of ADAS and CAD functions called Vehicle-in-Virtual –Environment (VVE) was recently introduced as a solution to this problem. The vehicle is operated in a large, empty, and flat area during VVE while its localization and perception sensor data is fed from the virtual environment with other traffic and rare and extreme events being generated as needed. The virtual environment can be easily configured and modified to construct different testing scenarios on demand. This paper focuses on the VVE approach and introduces the coordinate transformations needed to sync pose (location and orientation) in the virtual and physical worlds and handling of localization and perception sensor data using the highly realistic 3D simulation model of a recent autonomous shuttle deployment site in Columbus, Ohio as the virtual world. As a further example that uses multiple actors, the use of VVE for Vehicle-to-VRU communication based Vulnerable Road User (VRU) safety is presented in the paper using VVE experiments and real pedestrian(s) in a safe and repeatable manner. VVE experiments are used to demonstrate the efficacy of the method.</div></div>

  PublisherDOI

• Stability of Local Trajectory Planning for Level-2+ Semi-Autonomous Driving without Absolute Localization

  Electronics · 2024-09-26 · 1 citations

  articleOpen accessSenior authorCorresponding

  Autonomous driving has long grappled with the need for precise absolute localization, making full autonomy elusive and raising the capital entry barriers for startups. This study delves into the feasibility of local trajectory planning for Level-2+ (L2+) semi-autonomous vehicles without the dependence on accurate absolute localization. Instead, emphasis is placed on estimating the pose change between consecutive planning timesteps from motion sensors and on integrating the relative locations of traffic objects into the local planning problem within the ego vehicle’s local coordinate system, thereby eliminating the need for absolute localization. Without the availability of absolute localization for correction, the measurement errors of speed and yaw rate greatly affect the estimation accuracy of the relative pose change between timesteps. This paper proved that the stability of the continuous planning problem under such motion sensor errors can be guaranteed at certain defined conditions. This was achieved by formulating it as a Lyapunov-stability analysis problem. Moreover, a simulation pipeline was developed to further validate the proposed local planning method, which features adjustable driving environment with multiple lanes and dynamic traffic objects to replicate real-world conditions. Simulations were conducted at two traffic scenes with different sensor error settings for speed and yaw rate measurements. The results substantiate the proposed framework’s functionality even under relatively inferior sensor errors distributions, i.e., speed error verr∼N(−0.1,0.1) m/s and yaw rate error θ˙err∼N(0.57,1.72) deg/s. Experiments were also conducted to evaluate the stability limits of the planned results under abnormally larger motion sensor errors. The results provide a good match to the previous theoretical analysis. Our findings suggested that precise absolute localization may not be the sole path to achieving reliable trajectory planning, eliminating the necessity for high-accuracy dual-antenna Global Positioning System (GPS) as well as the pre-built high-fidelity (HD) maps for map-based localization.

  PublisherOA PDFDOI

• Deep Reinforcement Learning Based Collision Avoidance of Automated Driving Agent

  SAE technical papers on CD-ROM/SAE technical paper series · 2024-04-09

  articleSenior author

  <div class="section abstract"><div class="htmlview paragraph">Automated driving has become a very promising research direction with many successful deployments and the potential to reduce car accidents caused by human error. Automated driving requires automated path planning and tracking with the ability to avoid collisions as its fundamental requirement. Thus, plenty of research has been performed to achieve safe and time efficient path planning and to develop reliable collision avoidance algorithms. This paper uses a data-driven approach to solve the abovementioned fundamental requirement. Consequently, the aim of this paper is to develop Deep Reinforcement Learning (DRL) training pipelines which train end-to-end automated driving agents by utilizing raw sensor data. The raw sensor data is obtained from the Carla autonomous vehicle simulation environment here. The proposed automated driving agent learns how to follow a pre-defined path with reasonable speed automatically. First, the A* path searching algorithm is applied to generate an optimal path from origin to destination for the agent represented by waypoints. Further, preview path tracking errors, steering control and distance to destination are introduced to build the reward function. In addition, raw data from multiple sensors is processed separately and concatenated together to help the proposed agent get a comprehensive understanding of its environment. Two traffic scenarios including traffic rule free urban road and road segment with two intersections, traffic light and stop sign are used to evaluate the performance of the proposed automated driving agent. The performance of proposed Deep Q-Learning (DQN) agent is evaluated in multiple aspects. Compared to traditional mid-to-mid DRL agent with explicit decomposition of high-level behavior decision and low-level control, the proposed DRL agents are expected to have better performance and smaller size since all processing steps are optimized simultaneously. Moreover, the pre-calculated A* path provides a good reference point for subsequent DRL training.</div></div>

  PublisherDOI

• Cooperative Collision Avoidance in a Connected Vehicle Environment

  arXiv (Cornell University) · 2023-06-02 · 1 citations

  preprintOpen access

  Connected vehicle (CV) technology is among the most heavily researched areas in both the academia and industry. The vehicle to vehicle (V2V), vehicle to infrastructure (V2I) and vehicle to pedestrian (V2P) communication capabilities enable critical situational awareness. In some cases, these vehicle communication safety capabilities can overcome the shortcomings of other sensor safety capabilities because of external conditions such as 'No Line of Sight' (NLOS) or very harsh weather conditions. Connected vehicles will help cities and states reduce traffic congestion, improve fuel efficiency and improve the safety of the vehicles and pedestrians. On the road, cars will be able to communicate with one another, automatically transmitting data such as speed, position, and direction, and send alerts to each other if a crash seems imminent. The main focus of this paper is the implementation of Cooperative Collision Avoidance (CCA) for connected vehicles. It leverages the Vehicle to Everything (V2X) communication technology to create a real-time implementable collision avoidance algorithm along with decision-making for a vehicle that communicates with other vehicles. Four distinct collision risk environments are simulated on a cost effective Connected Autonomous Vehicle (CAV) Hardware in the Loop (HIL) simulator to test the overall algorithm in real-time with real electronic control and communication hardware.

  PublisherOA PDFDOI

• Vehicle in Virtual Environment (VVE) Method of Autonomous Driving Function Evaluation and Development

  SAE technical papers on CD-ROM/SAE technical paper series · 2023-04-11 · 3 citations

  article

  <div class="section abstract"><div class="htmlview paragraph">Autonomous vehicle (AV) algorithms need to be tested extensively in order to make sure the vehicle and the passengers will be safe while using it after the implementation. Testing these algorithms in real world create another important safety critical point. Real world testing is also subjected to limitations such as logistic limitations to carry or drive the vehicle to a certain location. For this purpose, hardware in the loop (HIL) simulations as well as virtual environments such as CARLA and LG SVL are used widely. This paper discusses a method that combines the real vehicle with the virtual world, called vehicle in virtual environment (VVE). This method projects the vehicle location and heading into a virtual world for desired testing, and transfers back the information from sensors in the virtual world to the vehicle. As a result, while vehicle is moving in the real world, it simultaneously moves in the virtual world and obtains the situational awareness via multiple virtual sensors. This would allow testing in a safe environment with the real vehicle while providing some additional benefits on vehicle dynamics fidelity, logistics limitations and passenger experience testing. The paper also demonstrates an example case study where path following and the virtual sensors are utilized to test a radar based stopping algorithm.</div></div>

  PublisherDOI

• Autonomous Vehicle Decision Making with Policy Prediction for Handling a Round Intersection

  Preprints.org · 2023-10-24 · 1 citations

  preprintOpen accessSenior author

  Autonomous shuttles have been used as end mile solutions for smart mobility in smart cities. The urban driving conditions of smart cities with many other actors sharing the road and the presence of intersections have posed challenges to the use of autonomous shuttles. Round intersections are more challenging as it is more difficult to perceive the other vehicles in and near the intersection. Thus, this paper focuses on decision making of autonomous vehicles for handling round intersections. The round intersection is introduced first, followed by introductions of the Markov Decision Process (MDP), the Partially Observable Markov Decision Process (POMDP) and the Object Oriented Partially Observable Markov Decision Process (OOPOMDP) which are used for decision making with uncertain knowledge of the motion of the other vehicles. The Partially Observable Monte-Carlo Planning (POMCP) algorithm is used as the solution method and OOPOMDP is applied to decision making for autonomous vehicles in round intersections. Decision making is formulated first as a POMDP problem, and the penalty function is formulated and set accordingly. This is followed by improvement of decision making with policy prediction. Augmented objective state and policy-based state transition are introduced and simulations are used to demonstrate the effectiveness of the proposed method for collision free handling of round intersections by the ego vehicle.

  PublisherOA PDFDOI

Frequent coauthors

• Levent Güvenç

  The Ohio State University

  78 shared

• Mümin Tolga Emirler

  Yıldız Technical University

  22 shared

• Şükrü Yaren Gelbal

  The Ohio State University

  21 shared

• Mustafa Ridvan Cantas

  14 shared

• Xinchen Li

  Dalian University of Technology

  8 shared

• E.S. Öztürk

  Istanbul University-Cerrahpaşa

  8 shared

• Sheng Zhu

  Nanjing University of Aeronautics and Astronautics

  8 shared

• İsmail Meriç Can Uygan

  8 shared

Labs

• Mechanical and Aerospace EngineeringPI

Awards & honors

• ASME Fellow (2014)
• IEEE Vehicular Technology Society Distinguished Lecturer (20…
• IEEE Open Journal of Vehicular Technology Associate Editor (…