Multi-Sensor Data Fusion for Self-Driving Cars

28 January, 2020 | TA Resource Center

Title: Multi-Sensor Data Fusion for Self-Driving Cars

Speaker: Thanuka Wickramarathne, UMass Lowell


In this lecture, we explore the notions of multi-sensor data fusion that are applicable to autonomous vehicles operating in dynamic environments. The lecture will begin with a brief introduction to data fusion covering data fusion-levels and architectures for autonomous vehicles; environment perception data and their representation; objects, grids and raw data oriented data fusion problems; and, some other details that play a vital role in real-life sensor fusion applications.

Then we will introduce sensor fusion, target tracking and situational awareness techniques with a special focus on self-driving technologies ranging from simple Kalman filters to advanced interacting multi-modal tracking techniques. In addition, we will also present some of the algorithms that are available in the literature along with some ongoing work on multiple-hypothesis tracking with the speaker’s research group in collaboration with local industry.

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