Xinshuo Weng - A Paradigm Shift for Perception and Prediction Pipeline in Autonomous Driving

Tuesday April 20th. MIT, CSAIL

Abstract:
Perception and prediction pipeline (3D object detection and multi-object tracking, trajectory forecasting) is a key component of autonomous systems such as self-driving cars. Although significant advancements have been achieved in each individual module of this pipeline, limited attention is received to improve the pipeline itself. In this talk, I will introduce an alternative to this standard perception and prediction pipeline. In contrast to the standard pipeline, this new pipeline first forecasts LiDAR point clouds using a standard LSTM autoencoder. Then, detection and tracking are performed on the predicted point clouds to obtain future object trajectories. As forecasting the LiDAR sensor data does not require object labels for training, we can scale performance of the Sequential Pointcloud Forecasting (SPF) module in this pipeline. To further improve the SPF module, I will talk about a few techniques that can produce significantly more fine-grained details and predict stochastic futures of LiDAR point clouds.

Bio:
Xinshuo Weng is a Ph.D. student at Robotics Institute of Carnegie Mellon University (CMU) advised by Kris Kitani. She received her master's degree at CMU, where she worked with Yaser Sheikh and Kris Kitani. Prior to CMU, she worked at Facebook Reality Lab as a research engineer to help build "Photorealistic Telepresence". Her recent research interest lies in 3D Computer Vision and Graph Neural Networks for autonomous systems. She has developed some 3D multi-object tracking systems such as AB3DMOT that has received +1,000 stars on GitHub. Also, she is leading a few autonomous driving workshops at major conferences such as NeurIPS 2020, IJCAI 2021, ICCV 2021. She was awarded a Qualcomm Innovation Fellowship for 2020-2021.