CVPR 2019 WAD Challenge on Trajectory Prediction and 3D Perception

04/06/2020
by   Sibo Zhang, et al.
0

This paper reviews the CVPR 2019 challenge on Autonomous Driving. Baidu's Robotics and Autonomous Driving Lab (RAL) providing 150 minutes labeled Trajectory and 3D Perception dataset including about 80k lidar point cloud and 1000km trajectories for urban traffic. The challenge has two tasks in (1) Trajectory Prediction and (2) 3D Lidar Object Detection. There are more than 200 teams submitted results on Leaderboard and more than 1000 participants attended the workshop.

READ FULL TEXT

page 3

page 4

research
06/26/2022

Woodscape Fisheye Object Detection for Autonomous Driving – CVPR 2022 OmniCV Workshop Challenge

Object detection is a comprehensively studied problem in autonomous driv...
research
04/20/2021

Efficient Online Transfer Learning for 3D Object Classification in Autonomous Driving

Autonomous driving has achieved rapid development over the last few deca...
research
10/14/2022

Motion Inspired Unsupervised Perception and Prediction in Autonomous Driving

Learning-based perception and prediction modules in modern autonomous dr...
research
07/17/2021

Woodscape Fisheye Semantic Segmentation for Autonomous Driving – CVPR 2021 OmniCV Workshop Challenge

We present the WoodScape fisheye semantic segmentation challenge for aut...
research
06/16/2023

The 1st-place Solution for CVPR 2023 OpenLane Topology in Autonomous Driving Challenge

We present the 1st-place solution of OpenLane Topology in Autonomous Dri...
research
02/19/2021

SLPC: a VRNN-based approach for stochastic lidar prediction and completion in autonomous driving

Predicting future 3D LiDAR pointclouds is a challenging task that is use...
research
05/29/2020

Probabilistic Object Classification using CNN ML-MAP layers

Deep networks are currently the state-of-the-art for sensory perception ...

Please sign up or login with your details

Forgot password? Click here to reset