Perceive, Predict, and Plan: Safe Motion Planning Through Interpretable Semantic Representations

13 August 2020

Papers citing "Perceive, Predict, and Plan: Safe Motion Planning Through Interpretable Semantic Representations"

6 / 56 papers shown

Title
LookOut: Diverse Multi-Future Prediction and Planning for Self-Driving Alexander Cui Sergio Casas Abbas Sadat Renjie Liao R. Urtasun 111 126 0 16 Jan 2021
Explainability of deep vision-based autonomous driving systems: Review and challenges Éloi Zablocki H. Ben-younes P. Pérez Matthieu Cord XAI 37 169 0 13 Jan 2021
DiGNet: Learning Scalable Self-Driving Policies for Generic Traffic Scenarios with Graph Neural Networks Peide Cai Hengli Wang Yuxiang Sun Ming-Yu Liu GNN 14 14 0 13 Nov 2020
Implicit Latent Variable Model for Scene-Consistent Motion Forecasting Sergio Casas Cole Gulino Simon Suo Katie Z Luo Renjie Liao R. Urtasun 152 155 0 23 Jul 2020
Spatially-Aware Graph Neural Networks for Relational Behavior Forecasting from Sensor Data Sergio Casas Cole Gulino Renjie Liao R. Urtasun AI4CE 174 211 0 18 Oct 2019
Discrete Residual Flow for Probabilistic Pedestrian Behavior Prediction Ajay Jain Sergio Casas Renjie Liao Yuwen Xiong Song Feng Sean Segal R. Urtasun 149 73 0 17 Oct 2019