Donghyun Kim
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Previous Organizations / Institutes
The organizations / institutes from the scholar's other articles in the past ten years
Research Subjects
Donghyun Kim has a total of 8 co-authors. Here are the top co-authors by number of shared publications:
- Aws Khalil
Department of Electrical and Computer Engineering, University of Michigan-Dearborn, Dearborn, MI 48128, USA
2 shared publications - Haewoon Nam
Department of Electrical Engineering, Hanyang University, Ansan, South Korea
2 shared publications - Jaerock Kwon
Department of Electrical and Computer Engineering, University of Michigan-Dearborn, Dearborn, MI 48128, USA
2 shared publications Gye-Seong Lee
Korea Institute of Industrial Technology,EV Components Materials RD group,Gwang-Ju,Republic of Korea
1 shared publication- Hae-Sol Lee
Robotics and Virtual Engineering, Korea University of Science and Technology, Daejeon, South Korea
1 shared publication - Hyun-Rok Cha
Automotive Material & Component R&D Group, Korea Institute of Industrial Technology, Gwangju 61012, Republic of Korea
1 shared publication
Latest Publications
- Open Access
- 5 November 2023
- Published by Elsevier in IBRO Neuroscience Reports
- Vol. 15, S950
ARTIFICIAL NEURAL NETWORK CONTROLLER USING SENSORY MOTOR CONTINGENCY FOR HIGHLY AUTOMATED MOBILITY
- 5 November 2023
- Published by Elsevier in IBRO Neuroscience Reports
- Vol. 15, S950
Open AccessARTIFICIAL NEURAL NETWORK CONTROLLER USING SENSORY MOTOR CONTINGENCY FOR HIGHLY AUTOMATED MOBILITY
- Open Access
- 16 February 2023
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Access
- Vol. 11, 16951-16963
OPEMI: Online Performance Evaluation Metrics Index for Deep Learning-Based Autonomous Vehicles
Vision-based autonomous driving is rapidly growing. There are, however, presently no agreed-upon metrics for assessing how well deep neural network (DNN) models perform in driving. To compare novel approaches and architectures to existing ones, some researchers employed a mean error between labeled and predicted values in a test dataset and others presented a new metric that is designed to match their requirements. The discrepancy in the usage of various performance metrics and lack of objective metrics to judge the driving performance were our primary motives for developing a feasible solution. In this study, we propose online performance evaluation metrics index (OPEMI), an integrated metric that can evaluate the driving capabilities of autonomous driving models in various driving scenarios. To evaluate driving performance precisely and objectively, OPEMI incorporates several variables, including driving control stability, driving trajectory stability, journey duration, travel distance, success rate, and speed. To demonstrate the validity of OPEMI, we first confirmed that the prediction accuracy has a weak correlation with driving performance. Then, we have discussed the constraints in the existing driving performance metrics in certain circumstances, and their failure to assess the driving models. Finally, we conducted experiments with four popular DNN models and two in-house models under three different driving scenarios (generic, urban, and racing). The results show that the proposed evaluation metric, OPEMI, realistically displays driving performance and demonstrates its validity in various driving scenarios.
- 16 February 2023
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Access
- Vol. 11, 16951-16963
Open AccessOPEMI: Online Performance Evaluation Metrics Index for Deep Learning-Based Autonomous Vehicles
Vision-based autonomous driving is rapidly growing. There are, however, presently no agreed-upon metrics for assessing how well deep neural network (DNN) models perform in driving. To compare novel approaches and architectures to existing ones, some researchers employed a mean error between labeled and predicted values in a test dataset and others presented a new metric that is designed to match their requirements. The discrepancy in the usage of various performance metrics and lack of objective metrics to judge the driving performance were our primary motives for developing a feasible solution. In this study, we propose online performance evaluation metrics index (OPEMI), an integrated metric that can evaluate the driving capabilities of autonomous driving models in various driving scenarios. To evaluate driving performance precisely and objectively, OPEMI incorporates several variables, including driving control stability, driving trajectory stability, journey duration, travel distance, success rate, and speed. To demonstrate the validity of OPEMI, we first confirmed that the prediction accuracy has a weak correlation with driving performance. Then, we have discussed the constraints in the existing driving performance metrics in certain circumstances, and their failure to assess the driving models. Finally, we conducted experiments with four popular DNN models and two in-house models under three different driving scenarios (generic, urban, and racing). The results show that the proposed evaluation metric, OPEMI, realistically displays driving performance and demonstrates its validity in various driving scenarios.
- 1 May 2019
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- Vol. 18, 1-5
Improvement on Stiffness of Vehicle Body Applying Variable Platform for Vehicle
As the society emphasizes individuality, various demands on vehicles are also increasing. Accordingly, development of vehicle technology, suitable for small quantity batch production, is needed. To meet the requirements, this paper studied the technology of the variable architecture chassis platform, and this chassis platform will contribute to the autonomous industrial EV (Electric Vehicle).
- 1 May 2019
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- Vol. 18, 1-5
Improvement on Stiffness of Vehicle Body Applying Variable Platform for Vehicle
As the society emphasizes individuality, various demands on vehicles are also increasing. Accordingly, development of vehicle technology, suitable for small quantity batch production, is needed. To meet the requirements, this paper studied the technology of the variable architecture chassis platform, and this chassis platform will contribute to the autonomous industrial EV (Electric Vehicle).