Invited Talk: INTERPRET: INTERACTION-dataset-based PREdicTion Challenge (Wei Zhan)

Video: https://slideslive.com/38930879/interpret-interactiondatasetbased-prediction-challenge

Abstract: It is a consensus in both academia and industry that behavior prediction is one of the most challenging problems blocking the realization of fully autonomous vehicles. It is a key asset for the behavior-related research community to have motion datasets with highly interactive driving behavior and critical situations in complex scenarios with different driving cultures. Prediction benchmarks with comprehensive evaluations are also crucial. This talk presents the INTERACTION dataset, which provides the highly accurate trajectories of various road users with densely interactive and critical behavior from different countries. Corresponding HD maps with full semantics of lane connections and traffic rules are also included in the dataset. The prediction challenge based on the INTERACTION dataset, INTERPRET as a NeurIPS’20 Competition, is also presented in this talk. The challenge offers multiple tracks to test the capabilities of the prediction model on data approximation, generalizability, as well as fatality in open-loop and closed-loop. The results on the leaderboard in the preliminary stage of the challenge are also briefly mentioned.

Bio: Wei Zhan is a Postdoctoral Scholar at UC Berkeley working with Professor Masayoshi Tomizuka. He received his Ph.D. from UC Berkeley in 2019. His research focus is interactive prediction and planning for autonomous driving, and his research interests span robotics, control, computer vision and machine learning. He has been coordinating the research activities in Autonomous Driving Group in Mechanical Systems Control Lab for years, from perception and prediction to decision and control on real autonomous vehicles. One of his publications on probabilistic prediction received the Best Student Paper Award in IEEE Intelligent Vehicle Symposium 2018 (IV’18). He is the lead author of the INTERACTION dataset, which provides highly interactive driving behavior in various complex scenarios from different countries. He is a key organizer of the prediction challenge based on the INTERACTION dataset as a NeurIPS’20 Competition. He also organized several workshops on Behavior Prediction and Decision (IV'19), Prediction Dataset and Benchmark (IROS'19), and Socially Compatible Behavior Generation (IV'20).