Real-time 3D Traffic Cone Detection for Autonomous Driving

by   Ankit Dhall, et al.

Considerable progress has been made in semantic scene understanding of road scenes with monocular cameras. It is, however, mainly related to certain classes such as cars and pedestrians. This work investigates traffic cones, an object class crucial for traffic control in the context of autonomous vehicles. 3D object detection using images from a monocular camera is intrinsically an ill-posed problem. In this work, we leverage the unique structure of traffic cones and propose a pipelined approach to the problem. Specifically, we first detect cones in images by a tailored 2D object detector; then, the spatial arrangement of keypoints on a traffic cone are detected by our deep structural regression network, where the fact that the cross-ratio is projection invariant is leveraged for network regularization; finally, the 3D position of cones is recovered by the classical Perspective n-Point algorithm. Extensive experiments show that our approach can accurately detect traffic cones and estimate their position in the 3D world in real time. The proposed method is also deployed on a real-time, critical system. It runs efficiently on the low-power Jetson TX2, providing accurate 3D position estimates, allowing a race-car to map and drive autonomously on an unseen track indicated by traffic cones. With the help of robust and accurate perception, our race-car won both Formula Student Competitions held in Italy and Germany in 2018, cruising at a top-speed of 54 kmph. Visualization of the complete pipeline, mapping and navigation can be found on our project page.


page 1

page 4

page 6


TraCon: A novel dataset for real-time traffic cones detection using deep learning

Substantial progress has been made in the field of object detection in r...

Object Detection in Specific Traffic Scenes using YOLOv2

object detection framework plays crucial role in autonomous driving. In ...

Real-time Full-stack Traffic Scene Perception for Autonomous Driving with Roadside Cameras

We propose a novel and pragmatic framework for traffic scene perception ...

Real-time 3D Pose Estimation with a Monocular Camera Using Deep Learning and Object Priors On an Autonomous Racecar

We propose a complete pipeline that allows object detection and simultan...

Explorations and Lessons Learned in Building an Autonomous Formula SAE Car from Simulations

This paper describes the exploration and learnings during the process of...

Real-Time And Robust 3D Object Detection with Roadside LiDARs

This work aims to address the challenges in autonomous driving by focusi...

Learned Two-Plane Perspective Prior based Image Resampling for Efficient Object Detection

Real-time efficient perception is critical for autonomous navigation and...

Please sign up or login with your details

Forgot password? Click here to reset