Content

• Mixed/Augmented Reality Traffic Simulation

• Autonomous Vehicle Control

• Motion Planning

• Undergraduate Project

• Experience and Accomplishment

Publication

• Jihun Han, Tyler Ard, Prakhar Gupta, Rongyao Wang, Ardalan Vahidi, Yunyi Jia, Dominik Karbowski, “Human Driver Interaction with An Eco-Speed Advisory System in Connected Vehicles: Simulation and Experimental Results,” Proceeding of Transportation Research Board, 2024. [Winner, Best Paper Award, Road User Measurement and Evaluation (ACH50) Committee] 

• Prakhar Gupta, Rongyao Wang, Tyler Ard, Jihun Han, Dominik Karbowski, Ardalan Vahidi, Yunyi Jia, An X-in-the-Loop (XIL) Testing Framework for Validation of Connected and Autonomous Vehicles

• Rongyao Wang, Yiqiang Han, Umesh Vaidya, Deep Koopman Data-Driven Optimal Control Framework for Autonomous Racing

  • Abstract

A model-based, data-driven control framework is introduced within the context of autonomous driving in this study. We propose a data-driven control algorithm that combines autonomous system identification using model-free learning and robust control using a model-based controller design. We present a full solution framework that is capable to automatically generate optimal paths while performing system identification of a vehicle with unknown dynamics. We then design model-based control which is actively learned from a data-driven approach. Based on our new system identification algorithm, we can approximate an accurate, explainable, and linearized system representation in a high-dimensional latent space, without any prior knowledge of the system. To validate the algorithm, we conduct the model predictive control of an autonomous vehicle based on the augmented system identification on a scaled racing vehicle. The result indicates that we are able to design control in the lifted space to achieve tasks in path control and obstacle avoidance. The automatic path generation combined with the data-driven control requires no a-priori knowledge of the vehicle and also proved to be effective that only requires less than 5 laps to design an optimal trajectory while identified a system that is able to achieve minimum lap time without extra learning episodes. 

• Wenjian Hao, Rongyao Wang, Alexander Krolicki, Yiqiang Han, Cell A* for Navigation of Unmanned Aerial Vehicles in Partially-known Environments: arXiv:2009.07404

  • Abstract

Proper path planning is the first step of robust and efficient autonomous navigation for mobile robots. Meanwhile, it is still challenging for robots to work in a complex environment without complete prior information. This paper presents an extension to the A* search algorithm and its variants to make the path planning stable with less computational burden while handling long-distance tasks. The implemented algorithm is capable of online searching for a collision-free and smooth path when heading to the defined goal position. This paper deploys the algorithm on the autonomous drone platform and implements it on a remote control car for algorithm efficiency validation.

• Akshatha Ramesh, Dhananjay Nikam, Venkat Narayanan Balachandran, Longxiang Guo, Rongyao Wang, Leo Hu, Gurcan Comert, Yunyi Jia, Cloud-based collaborative road-damage monitoring with deep learning and smartphones

  • Abstract

Road damage such as potholes and cracks may reduce ride comfort and traffic safety. This influence can be prevented by regular, proper monitoring and maintenance of roads. Traditional methods and existing methods of surveying are very time-consuming, expensive, require a lot of human effort, and, thus, cannot be conducted frequently. A more efficient and cost-effective process is required to augment profilometer and traditional road-condition recognition systems. In this study, we propose deep-learning methods using smartphone data to devise a cost-effective and ad-hoc approach. Information from sensors on smartphones such as motion sensors and cameras are harnessed to detect road damage using deep-learning algorithms. In order to give heuristic and accurate information about the road damage, we used a cloud-based collaborative approach to fuse all the data and update a map frequently with these road-surface conditions. During the experiment, the deep-learning models achieved good prediction accuracy on our dataset, and the cloud-based fusion approach was able to group and merge the detections from different vehicles.

Thesis

• Master Research Thesis: Data-Driven System Identification and Optimal Control Framework for Grand-Prix Style Autonomous Racing