Rivian, United States
Early Academic Pursuits:
Lingqiao Qin began their academic journey with a Bachelor's degree in Civil Engineering from Beijing Jiaotong University in 2009. Following this, they pursued a Master's degree in Vehicle Safety Engineering from The George Washington University in 2012. They continued their education with a Master's degree in Industrial and Systems Engineering from the University of Wisconsin-Madison in 2016, and later earned a Ph.D. in Transportation Engineering with a minor in Statistics from the same institution in 2021.
Her accumulated a wealth of experience in the field of transportation engineering, vehicle safety, and machine learning. They have worked with prestigious companies in the autonomous vehicle industry, holding positions such as Systems Engineer at Zoox and currently serving as a Staff Technical Safety Engineer at Rivian. At Rivian, Her been involved in crucial aspects of functional safety, contributing to feature requirements, conducting hazard analysis and risk assessments, and providing guidance on safety metrics for autonomous systems. Their role involves coordination with various engineering teams to ensure compliance throughout the functional safety lifecycle.
Contributions and Research Focus:
Her research focus spans various aspects of transportation engineering and safety. They have published several papers in reputable journals, covering topics such as the impact of weather and road geometry on acceleration behavior, sensor layout strategies for traffic flow parameter acquisition, and deep learning-based traffic flow prediction methods. In their role as a Research Assistant at the Traffic Operations and Safety Lab, Her engaged in 3D virtual scene development, statistical modeling, and data visualization for crash data. Their work extended to large-scale driver distraction evaluation in fatal crashes and investigating risk factors influencing crash severity with winter precipitation in the United States.
Accolades and Recognition:
While specific accolades and recognition are not explicitly mentioned,Her consistent academic achievements, contributions to research, and positions at renowned companies like Rivian and Zoox are indicative of their professional recognition in the field.
Impact and Influence:
Her work has contributed significantly to the fields of transportation engineering, vehicle safety, and machine learning. Their research publications reflect a commitment to advancing knowledge and understanding in these domains. Additionally, their roles at leading companies demonstrate practical applications of their expertise in the development and safety assurance of autonomous and connected vehicles.
Legacy and Future Contributions:
Her legacy lies in their impactful research, contributions to safety standards in autonomous vehicles, and their role in shaping the future of transportation. Their extensive skills in machine learning, data analysis, and transportation engineering position them to continue making substantial contributions to the evolving landscape of autonomous and connected vehicles. As the industry progresses, Her is likely to play a vital role in shaping safety standards and technological advancements.
A hybrid deep learning based traffic flow prediction method and its understanding
Y Wu, H Tan, L Qin, B Ran, Z Jiang
Day-ahead traffic flow forecasting based on a deep belief network optimized by the multi-objective particle swarm algorithm
L Li, L Qin, X Qu, J Zhang, Y Wang, B Ran
Short-term prediction of lane-level traffic speeds: A fusion deep learning model
Y Gu, W Lu, L Qin, M Li, Z Shao
Vehicle trajectory prediction using LSTMs with spatial–temporal attention mechanisms
L Lin, W Li, H Bi, L Qin
An improved Bayesian combination model for short-term traffic prediction with deep learning
Y Gu, W Lu, X Xu, L Qin, Z Shao, H Zhang
Weather and road geometry impact on longitudinal driving behavior: Exploratory analysis using an empirically supported acceleration modeling framework
SH Hamdar, L Qin, A Talebpour
Short-term passenger flow prediction under passenger flow control using a dynamic radial basis function network
H Li, Y Wang, X Xu, L Qin, H Zhang
Estimation of missing values in heterogeneous traffic data: Application of multimodal deep learning model
L Li, B Du, Y Wang, L Qin, H Tan
Passenger flow control with multi-station coordination in subway networks: algorithm development and real-world case study
X Xu, H Li, J Liu, B Ran, L Qin
Learning the route choice behavior of subway passengers from AFC data
X Xu, L Xie, H Li, L Qin