IIIT Hyderabad Publications |
|||||||||
|
INFER: INtermediate representations for FuturE pRedictionAuthors: Shashank Srikanth,Junaid Ahmed Ansari,Karnik Ram,Sarthak Sharma,Madhava Krishna Conference: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2019 2019) Location The Venetian Macao, Macau, China Date: 2019-11-04 Report no: IIIT/TR/2019/76 AbstractIn urban driving scenarios, forecasting future trajectories of surrounding vehicles is of paramount importance. While several approaches for the problem have been proposed, the best-performing ones tend to require extremely detailed input representations (e.g. image sequences). As a result, such methods do not generalize to datasets they have not been trained on. In this paper, we propose intermediate representations that are particularly well-suited for future prediction. As opposed to using texture (color) information from images, we condition on semantics and train an autoregressive model to accurately predict future trajectories of traffic participants (vehicles). We demonstrate that semantics provide a significant boost over techniques that operate over raw pixel intensities/disparities. Uncharacteristic of state-of-the-art approaches, our representations and models generalize across different sensing modalities (stereo imagery, LiDAR, a combination of both), and also across completely different datasets, collected across several cities, and also across countries where people drive on opposite sides of the road (left-handed vs right-handed driving). Additionally, we demonstrate an application of our approach in multi-object tracking (data association). To foster further research in transferable representations and ensure reproducibility, we release all our code and data. Full paper: pdf Centre for Robotics |
||||||||
Copyright © 2009 - IIIT Hyderabad. All Rights Reserved. |